Valeria Di Cola, Olivier Broennimann, Blaise Petitpierre, Manuela D'Amen, Frank Breiner & Antoine Guisan

08 November, 2017

Miscellaneous methods and utilities for spatial ecology analysis, written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) & Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland.

ecospat offers the possibility to perform Pre-modelling Analysis, such as Spatial autocorrelation analysis, MESS (Multivariate Environmental Similarity Surfaces) analyses, Phylogenetic diversity Measures, Biotic Interactions. It also provides functions to complement biomod2 in preparing the data, calibrating and evaluating (e.g. boyce index) and projecting the models. Complementary analysis based on model predictions (e.g. co-occurrences analyses) are also provided.

In addition, the ecospat package includes Niche Quantification and Overlap functions that were used in Broennimann et al. 2012 and Petitpierre et al. 2012 to quantify climatic niche shifts between the native and invaded ranges of invasive species.

Load data

library(ecospat)
## Loading required package: ade4
## Loading required package: ape
## Loading required package: gbm
## Loading required package: survival
## Loading required package: lattice
## Loading required package: splines
## Loading required package: parallel
## Loaded gbm 2.1.3
## Loading required package: sp
citation("ecospat")
## 
## To cite package 'ecospat' in publications use:
## 
##   Olivier Broennimann, Valeria Di Cola and Antoine Guisan (2017).
##   ecospat: Spatial Ecology Miscellaneous Methods. R package
##   version 2.2.0.
##   http://www.unil.ch/ecospat/home/menuguid/ecospat-resources/tools.html
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {ecospat: Spatial Ecology Miscellaneous Methods},
##     author = {Olivier Broennimann and Valeria {Di Cola} and Antoine Guisan},
##     year = {2017},
##     note = {R package version 2.2.0},
##     url = {http://www.unil.ch/ecospat/home/menuguid/ecospat-resources/tools.html},
##   }

Test data for the ecospat library

ecospat.testData()

data(ecospat.testData)
names(ecospat.testData)
##  [1] "numplots"                      "long"                         
##  [3] "lat"                           "ddeg"                         
##  [5] "mind"                          "srad"                         
##  [7] "slp"                           "topo"                         
##  [9] "Achillea_atrata"               "Achillea_millefolium"         
## [11] "Acinos_alpinus"                "Adenostyles_glabra"           
## [13] "Aposeris_foetida"              "Arnica_montana"               
## [15] "Aster_bellidiastrum"           "Bartsia_alpina"               
## [17] "Bellis_perennis"               "Campanula_rotundifolia"       
## [19] "Centaurea_montana"             "Cerastium_latifolium"         
## [21] "Cruciata_laevipes"             "Doronicum_grandiflorum"       
## [23] "Galium_album"                  "Galium_anisophyllon"          
## [25] "Galium_megalospermum"          "Gentiana_bavarica"            
## [27] "Gentiana_lutea"                "Gentiana_purpurea"            
## [29] "Gentiana_verna"                "Globularia_cordifolia"        
## [31] "Globularia_nudicaulis"         "Gypsophila_repens"            
## [33] "Hieracium_lactucella"          "Homogyne_alpina"              
## [35] "Hypochaeris_radicata"          "Leontodon_autumnalis"         
## [37] "Leontodon_helveticus"          "Myosotis_alpestris"           
## [39] "Myosotis_arvensis"             "Phyteuma_orbiculare"          
## [41] "Phyteuma_spicatum"             "Plantago_alpina"              
## [43] "Plantago_lanceolata"           "Polygonum_bistorta"           
## [45] "Polygonum_viviparum"           "Prunella_grandiflora"         
## [47] "Rhinanthus_alectorolophus"     "Rumex_acetosa"                
## [49] "Rumex_crispus"                 "Vaccinium_gaultherioides"     
## [51] "Veronica_alpina"               "Veronica_aphylla"             
## [53] "Agrostis_capillaris"           "Bromus_erectus_sstr"          
## [55] "Campanula_scheuchzeri"         "Carex_sempervirens"           
## [57] "Cynosurus_cristatus"           "Dactylis_glomerata"           
## [59] "Daucus_carota"                 "Festuca_pratensis_sl"         
## [61] "Geranium_sylvaticum"           "Leontodon_hispidus_sl"        
## [63] "Potentilla_erecta"             "Pritzelago_alpina_sstr"       
## [65] "Prunella_vulgaris"             "Ranunculus_acris_sl"          
## [67] "Saxifraga_oppositifolia"       "Soldanella_alpina"            
## [69] "Taraxacum_officinale_aggr"     "Trifolium_repens_sstr"        
## [71] "Veronica_chamaedrys"           "Parnassia_palustris"          
## [73] "glm_Agrostis_capillaris"       "glm_Leontodon_hispidus_sl"    
## [75] "glm_Dactylis_glomerata"        "glm_Trifolium_repens_sstr"    
## [77] "glm_Geranium_sylvaticum"       "glm_Ranunculus_acris_sl"      
## [79] "glm_Prunella_vulgaris"         "glm_Veronica_chamaedrys"      
## [81] "glm_Taraxacum_officinale_aggr" "glm_Plantago_lanceolata"      
## [83] "glm_Potentilla_erecta"         "glm_Carex_sempervirens"       
## [85] "glm_Soldanella_alpina"         "glm_Cynosurus_cristatus"      
## [87] "glm_Campanula_scheuchzeri"     "glm_Festuca_pratensis_sl"     
## [89] "glm_Bromus_erectus_sstr"       "glm_Saxifraga_oppositifolia"  
## [91] "glm_Daucus_carota"             "glm_Pritzelago_alpina_sstr"   
## [93] "gbm_Bromus_erectus_sstr"       "gbm_Saxifraga_oppositifolia"  
## [95] "gbm_Daucus_carota"             "gbm_Pritzelago_alpina_sstr"

Test data for the Niche Overlap Analysis

ecospat.testNiche.inv()

data(ecospat.testNiche.inv)
names(ecospat.testNiche.inv)
##  [1] "x"           "y"           "aetpet"      "gdd"         "p"          
##  [6] "pet"         "stdp"        "tmax"        "tmin"        "tmp"        
## [11] "species_occ" "predictions"

ecospat.testNiche.nat()

data(ecospat.testNiche.nat)
names(ecospat.testNiche.nat)
##  [1] "x"           "y"           "aetpet"      "gdd"         "p"          
##  [6] "pet"         "stdp"        "tmax"        "tmin"        "tmp"        
## [11] "species_occ" "predictions"

Test tree for Phylogenetic Diversity Analysis

ecospat.testTree()

fpath <- system.file("extdata", "ecospat.testTree.tre", package="ecospat")
fpath
## [1] "/private/var/folders/y_/356n2gv11490bhhswc7m8m6w0000gn/T/RtmpDWdwN5/Rinst6c6618fe9469/ecospat/extdata/ecospat.testTree.tre"
tree<-read.tree(fpath)
tree$tip.label
##  [1] "Rumex_acetosa"             "Polygonum_bistorta"       
##  [3] "Polygonum_viviparum"       "Rumex_crispus"            
##  [5] "Cerastium_latifolium"      "Silene_acaulis"           
##  [7] "Gypsophila_repens"         "Vaccinium_gaultherioides" 
##  [9] "Soldanella_alpina"         "Cruciata_laevipes"        
## [11] "Galium_album"              "Galium_anisophyllon"      
## [13] "Galium_megalospermum"      "Gentiana_verna"           
## [15] "Gentiana_bavarica"         "Gentiana_purpurea"        
## [17] "Gentiana_lutea"            "Bartsia_alpina"           
## [19] "Rhinanthus_alectorolophus" "Prunella_grandiflora"     
## [21] "Acinos_alpinus"            "Plantago_alpina"          
## [23] "Plantago_lanceolata"       "Veronica_officinalis"     
## [25] "Veronica_aphylla"          "Veronica_alpina"          
## [27] "Veronica_chamaedrys"       "Veronica_persica"         
## [29] "Globularia_cordifolia"     "Globularia_nudicaulis"    
## [31] "Myosotis_alpestris"        "Myosotis_arvensis"        
## [33] "Aposeris_foetida"          "Centaurea_montana"        
## [35] "Hieracium_lactucella"      "Leontodon_helveticus"     
## [37] "Leontodon_autumnalis"      "Hypochaeris_radicata"     
## [39] "Achillea_atrata"           "Achillea_millefolium"     
## [41] "Homogyne_alpina"           "Senecio_doronicum"        
## [43] "Adenostyles_glabra"        "Arnica_montana"           
## [45] "Aster_bellidiastrum"       "Bellis_perennis"          
## [47] "Doronicum_grandiflorum"    "Phyteuma_orbiculare"      
## [49] "Phyteuma_spicatum"         "Campanula_rotundifolia"

Plot tree

plot(tree, cex=0.6)

plot of chunk tree

Pre-Modelling Analysis

Spatial Auto-correlation

Mantel Correlogram with ecospat.mantel.correlogram()

ecospat.mantel.correlogram(dfvar=ecospat.testData[c(2:16)],colxy=1:2, n=100, 
                           colvar=3:7, max=1000, nclass=10, nperm=100)

plot of chunk mantel_cor

The graph indicates that spatial autocorrelation (SA) is minimal at a distance of 180 meters. Note however that SA is not significantly different than zero for several distances (open circles).

Predictor Variable Selection

Number of Predictors with Pearson Correlation ecospat.npred()

colvar <- ecospat.testData[c(4:8)]
x <- cor(colvar, method="pearson")
ecospat.npred (x, th=0.75)
## [1] 4

Number of Predictors with Spearman Correlation ecospat.npred()

x <- cor(colvar, method="spearman")
ecospat.npred (x, th=0.75)
## [1] 4

Climate Analogy Tools

Climate Analogy with ecospat.climan()

x <- ecospat.testData[c(4:8)]
p<- x[1:90,] #A projection dataset.
ref<- x[91:300,] # A reference dataset
ecospat.climan(ref,p)
##  [1]  0.185415746 -0.028290993 -0.032909931 -0.009237875 -0.034642032
##  [6] -0.209006928 -0.084295612 -0.103622863  0.355220600 -0.136258661
## [11] -0.087182448 -0.209006928 -0.143187067 -0.124711316 -0.114844720
## [16] -0.230596451  0.276046242  0.249093277 -0.125288684 -0.101226337
## [21] -0.113883908 -0.204653076 -0.001154734 -0.132217090 -0.100461894
## [26]  0.464738681 -0.416578541 -0.044457275 -0.018475751 -0.122225532
## [31] -0.137611720 -0.050808314  0.254605027 -0.062012319  0.238294633
## [36] -0.159141330 -0.147806005  0.277670365 -0.071593533 -0.019053118
## [41]  0.390781314  0.175132571  0.401892929  0.843703731  0.286155800
## [46]  0.321142114  0.668511130  0.252253209  0.440050672  0.177247206
## [51]  0.831525456  0.303710525  0.197182304  0.219273698  0.196637663
## [56]  0.195300816  0.142395786  0.176988160 -0.051991905  0.265163111
## [61] -0.020785219 -0.017898383  0.553965995  0.409635110  0.323633285
## [66]  0.468693064  0.124983005 -0.032909931  0.165642783  0.147046687
## [71]  0.202895471  0.341992334  0.225508458  0.133254065  0.485295264
## [76] -0.047344111 -0.012282931  0.165429659  0.134199992  0.216655251
## [81]  0.139419127  0.121254775  0.098782992  0.591393741  0.110866239
## [86]  0.146010655  0.095562156  0.093353356  0.081712342  0.160531262

Extrapolation detection, creating a MESS object with ecospat.mess()

x <- ecospat.testData[c(2,3,4:8)]
proj<- x[1:90,] #A projection dataset.
cal<- x[91:300,] #A calibration dataset
mess.object<-ecospat.mess (proj, cal, w="default")

Plot MESS with ecospat.plot.mess()

ecospat.plot.mess (xy=proj[c(1:2)], mess.object, cex=1, pch=15)

plot of chunk mess In the MESS plot pixels in red indicate sites where at least one environmental predictor has values outside of the range of that predictor in the calibration dataset. In the MESSw plot, same as previous plot but with weighted by the number of predictors.Finally, the MESSneg plot shows at each site how many predictors have values outside of their calibration range.

Phylogenetic Diversity Measures

fpath <- system.file("extdata", "ecospat.testTree.tre", package="ecospat")
tree <- read.tree(fpath)
data <- ecospat.testData[9:52]

Calculate Phylogenetic Diversity Measures ecospat.calculate.pd

pd<- ecospat.calculate.pd(tree, data, method = "spanning", type = "species", root = TRUE, average = FALSE, verbose = TRUE )
## Progress (. = 100 pixels calculated):
## ... [300]
## All 300 pixels done.
pd
##   [1]   6.9782188   6.7981743   4.9964700   4.9964700   4.9964700
##   [6]  29.8820547  58.7451752   6.5223035  30.6152478   1.5258335
##  [11]   0.0000000  44.3661803  38.4155607   6.5223035  24.0929443
##  [16]  78.1607950  38.4155607  29.0894143  29.0894143  89.9839758
##  [21]  27.4135569  40.2827035   1.5258335  56.7686202  18.9535475
##  [26]  34.8871800   0.0000000   1.5258335  39.9291325  48.5997861
##  [31]  82.8763723  29.0894143  24.0929443  24.0929443  35.0949481
##  [36]  85.1406422  54.7974724  41.2817284  32.4100269  30.0984781
##  [41]  46.8247511  42.8358475  35.6223697  91.5539224  72.7022527
##  [46]   0.0000000  21.1862293  29.7320308  10.1187868  30.6152478
##  [51]  27.4135569  59.0015345  78.1536692  42.6423378  24.0929443
##  [56]  46.8050070  49.3924266  29.0894143  38.5290848  43.3611373
##  [61]  63.6397674  49.6097169  34.6522309  37.1871282 109.8813371
##  [66] 106.6971561  52.2512132  80.6221671  68.3867818  49.1362998
##  [71]  56.6138690  41.9283257  29.0894143  33.2026673  16.1897593
##  [76]  79.1938213  42.8115427  25.6187778  34.6805724  96.9902366
##  [81]  75.2672695   7.5313673  31.4078882  50.5865673  13.9570775
##  [86] 104.4121025  43.0464918  36.6693230  52.8590823  24.8855847
##  [91] 107.2302322  33.9358604  54.0048319  30.6152478 102.0983385
##  [96]   8.3170826  52.3071062   8.3170826  61.8562896  58.1179346
## [101]  59.7939424   8.3170826  81.6495398  51.1054635  75.8701970
## [106]  77.6947419  56.7929250  70.3693202  81.3965205  29.9118877
## [111] 111.0790432  75.7518798 112.5482496  32.9763735  42.5644761
## [116]  40.4507005  83.8955419  36.6693230   2.3184739  57.5978451
## [121]  91.3453370  33.3983912  50.1351419   7.7084002  63.9227817
## [126]   0.7926404  67.2813325  91.2965996  90.9578739 105.9024741
## [131]  74.6128871  46.1321553  15.2479619  24.0929443  70.4802708
## [136]  68.8949899 118.6657550 101.3545260 119.8539056  23.6602184
## [141] 105.8968281  15.9336325 138.4059855  39.6674173  51.7391372
## [146]  58.4119283  81.1388699  96.6048825  72.2156025  56.3601992
## [151] 112.9489963  63.3258805  50.1594468  23.0021994  87.1886965
## [156]  12.7714946  33.7421666  23.2537702  14.3226164   6.9752071
## [161]   0.7926404  13.5641350  36.2007616  63.9227817  40.3310946
## [166]  52.8264129  67.9956878  29.5843437   0.0000000 191.7818606
## [171] 133.6077875  83.3977825 118.6711630  51.1512871  69.3838811
## [176]  87.7066616  35.8005270  93.7797077  85.8984840  23.4933413
## [181] 149.7094684  52.4451847 112.1873673  53.4479612  51.4341108
## [186] 106.6959500  14.4361405  41.6547546  89.4018733  59.1068292
## [191]   3.0516670  60.7852739  28.1850877  52.1002690 114.3651475
## [196]  86.2640717  83.7092232  39.8499777  55.3514065 116.1795597
## [201]  21.2346203  75.4593878 197.8157358 140.3806968  93.2192350
## [206]  36.5337815 146.3370747 214.5450205  64.2439145  83.3740177
## [211]  57.0440643 149.5697614 196.9415036  31.0984631  57.4769230
## [216]  28.4014469  42.3978747 194.5384819  60.5204195  73.0060715
## [221]  52.1628582  30.2801165  63.1752097  29.1789484  82.7662787
## [226] 196.8309769   3.4666557   0.0000000  31.5688084  60.5650008
## [231]  43.3334929  62.5952411  13.9570775  18.9495667  35.2646601
## [236]  32.6155790   0.0000000  14.6693623  24.2745827  73.9480832
## [241]  19.2825866   0.0000000  40.6115985  68.9862341   6.9782188
## [246]  11.5030881  27.9105497  72.4020225  39.6781995  35.4596364
## [251]  33.9160835  27.5735165  15.9619740  27.9105497  17.8628493
## [256]  36.0936777  87.0440848  27.9105497  66.6907987  21.6475811
## [261]  67.5969904   0.0000000   0.0000000   0.0000000  58.0542370
## [266]   0.0000000   0.0000000  27.9105497   0.0000000   0.0000000
## [271]  27.9105497  34.8887684  56.5556633  27.9105497  30.3097595
## [276]  88.4296666  37.8150727  54.2397810  31.6243116   7.5799087
## [281]  73.0136833  31.8638035  41.7172212 120.5228857  32.2001243
## [286] 151.4545228  10.1544492  70.8133537  59.3255687  25.7211220
## [291]  24.1115267  43.1500941 150.0299191  72.2758570  85.9498096
## [296] 159.7242106  66.8328159  24.0929443   0.0000000  27.9105497

Plot the results (correlation of phylogenetic diversity with species richness)

plot(pd)

plot of chunk pd

Niche Quantification and Comparison with Ordination techniques

Loading test data for the niche dynamics analysis in the invaded range

inv <- ecospat.testNiche.inv

Loading test data for the niche dynamics analysis in the native range

nat <- ecospat.testNiche.nat

PCA-ENVIRONMENT

The PCA is calibrated on all the sites of the study area

Calibrating the PCA in the whole studay area, including both native and invaded ranges (same as PCAenv in Broenniman et al. 2012)

pca.env <- dudi.pca(rbind(nat,inv)[,3:10],scannf=F,nf=2) 

Plot Variables Contribution with ecospat.plot.contrib()

ecospat.plot.contrib(contrib=pca.env$co, eigen=pca.env$eig)

plot of chunk unnamed-chunk-17 The correlation circle indicate the contribution of original predictors to the PCA axes.

Predict the scores on the axes

# PCA scores for the whole study area
scores.globclim <- pca.env$li

# PCA scores for the species native distribution
scores.sp.nat <- suprow(pca.env,nat[which(nat[,11]==1),3:10])$li

# PCA scores for the species invasive distribution
scores.sp.inv <- suprow(pca.env,inv[which(inv[,11]==1),3:10])$li

# PCA scores for the whole native study area
scores.clim.nat <- suprow(pca.env,nat[,3:10])$li

# PCA scores for the whole invaded study area
scores.clim.inv <- suprow(pca.env,inv[,3:10])$li

Calculate the Occurrence Densities Grid with ecospat.grid.clim.dyn()

For a species in the native range (North America)

# gridding the native niche
grid.clim.nat <- ecospat.grid.clim.dyn(glob=scores.globclim, 
                                       glob1=scores.clim.nat,
                                       sp=scores.sp.nat, R=100,
                                       th.sp=0) 

For a species in the invaded range (Australia)

# gridding the invasive niche
grid.clim.inv <- ecospat.grid.clim.dyn(glob=scores.globclim,
                                       glob1=scores.clim.inv,
                                       sp=scores.sp.inv, R=100,
                                       th.sp=0) 

Calculate Niche Overlap with ecospat.niche.overlap()

# Compute Schoener's D, index of niche overlap
D.overlap <- ecospat.niche.overlap (grid.clim.nat, grid.clim.inv, cor=T)$D 
D.overlap
## [1] 0.224586

The niche overlap between the native and the ivaded range is 22%.

Perform the Niche Equivalency Test with ecospat.niche.equivalency.test() according to Warren et al. (2008)

It is reccomended to use at least 1000 replications for the equivalency test. As an example we used rep = 10, to reduce the computational time.

eq.test <- ecospat.niche.equivalency.test(grid.clim.nat, grid.clim.inv,
                                          rep=10, alternative = "greater")

Niche equivalency test H1: Is the overlap between the native and invaded niche higher than two random niches?

Perform the Niche Similarity Test with ecospat.niche.similarity.test()

Shifting randomly the invasive niche in the invaded study area It is recomended to use at least 1000 replications for the similarity test. As an example we used rep = 10, to reduce the computational time.

sim.test <- ecospat.niche.similarity.test(grid.clim.nat, grid.clim.inv,
                                          rep=10, alternative = "greater",
                                          rand.type=2) 

Niche similarity test H1: Is the overlap between the native and invaded higher than when the invasive niche is randomly introduced in the invaded study area?

Plot Equivalency test

ecospat.plot.overlap.test(eq.test, "D", "Equivalency")

plot of chunk unnamed-chunk-24

Plot Similarity test

ecospat.plot.overlap.test(sim.test, "D", "Similarity")

plot of chunk unnamed-chunk-25 We see that the niche overlap D is 22% and this value is compared to the random distribution of the niche equivalency and niche similarity tests.

Delimiting niche categories and quantifying niche dynamics in analogue climates with ecospat.niche.dyn.index()

niche.dyn <- ecospat.niche.dyn.index (grid.clim.nat, grid.clim.inv, intersection = 0.1)

Visualizing niche categories, niche dynamics and climate analogy between ranges with ecospat.plot.niche.dyn()

Plot niche overlap

ecospat.plot.niche.dyn(grid.clim.nat, grid.clim.inv, quant=0.25, interest=2,
                       title= "Niche Overlap", name.axis1="PC1",
                       name.axis2="PC2")

ecospat.shift.centroids(scores.sp.nat, scores.sp.inv, scores.clim.nat, scores.clim.inv)

plot of chunk unnamed-chunk-26

Plot the niche dynamics along one gradient (here temperature) with ecospat.plot.niche.dyn()

# gridding the native niche
grid.clim.t.nat <- ecospat.grid.clim.dyn(glob=as.data.frame(rbind(nat,inv)[,10]), 
                                         glob1=as.data.frame(nat[,10]),
                                    sp=as.data.frame(nat[which(nat[,11]==1),10]),
                                    R=1000, th.sp=0) 
# gridding the invaded niche
grid.clim.t.inv <- ecospat.grid.clim.dyn(glob=as.data.frame(rbind(nat,inv)[,10]),
                                         glob1=as.data.frame(inv[,10]),
                                         sp=as.data.frame(inv[which(inv[,11]==1),10]), 
                                         R=1000, th.sp=0) 
t.dyn<-ecospat.niche.dyn.index (grid.clim.t.nat, grid.clim.t.inv,
                                intersection=0.1)
ecospat.plot.niche.dyn(grid.clim.t.nat, grid.clim.t.inv, quant=0, 
                       interest=2, title= "Niche Overlap", 
                       name.axis1="Average temperature")

plot of chunk unnamed-chunk-27

Biotic Interactions

Species Co-occurrences Analysis with a Presence-absence matrix using the function ecospat.co_occurrences()

data <- ecospat.testData[c(9:16,54:57)]

For each pair of species (sp1, sp2), the number (N) of plots where both species were present is divided by the number of plots where the rarest of the two species is present. This index ranges from 0 (no co-occurrence) to 1 (always in co-occurrence) as given in eq. 1.

where N(S1 intersects S2) is the number of times species S1 and S2 co-occur, while Min(NS1, NS2) is the number of times species S1 and S2 co-occur, while is the occurrence frequency of the rarest of the two species.

ecospat.co_occurrences (data)

plot of chunk unnamed-chunk-28

##                       Aposeris_foetida Arnica_montana Aster_bellidiastrum
## Aposeris_foetida             1.0000000      0.3636364          0.25000000
## Arnica_montana               0.3636364      1.0000000          0.36363636
## Aster_bellidiastrum          0.2500000      0.3636364          1.00000000
## Bartsia_alpina               0.2222222      0.5454545          0.59090909
## Bromus_erectus_sstr          0.1250000      0.0000000          0.00000000
## Campanula_scheuchzeri        0.2444444      0.6818182          0.79545455
## Carex_sempervirens           0.4000000      0.5000000          0.65909091
## Cynosurus_cristatus          0.4222222      0.2272727          0.04545455
##                       Bartsia_alpina Bromus_erectus_sstr
## Aposeris_foetida          0.22222222              0.1250
## Arnica_montana            0.54545455              0.0000
## Aster_bellidiastrum       0.59090909              0.0000
## Bartsia_alpina            1.00000000              0.0000
## Bromus_erectus_sstr       0.00000000              1.0000
## Campanula_scheuchzeri     0.76086957              0.0000
## Carex_sempervirens        0.69565217              0.0625
## Cynosurus_cristatus       0.02173913              0.3750
##                       Campanula_scheuchzeri Carex_sempervirens
## Aposeris_foetida                 0.24444444         0.40000000
## Arnica_montana                   0.68181818         0.50000000
## Aster_bellidiastrum              0.79545455         0.65909091
## Bartsia_alpina                   0.76086957         0.69565217
## Bromus_erectus_sstr              0.00000000         0.06250000
## Campanula_scheuchzeri            1.00000000         0.67058824
## Carex_sempervirens               0.67058824         1.00000000
## Cynosurus_cristatus              0.04705882         0.05882353
##                       Cynosurus_cristatus
## Aposeris_foetida               0.42222222
## Arnica_montana                 0.22727273
## Aster_bellidiastrum            0.04545455
## Bartsia_alpina                 0.02173913
## Bromus_erectus_sstr            0.37500000
## Campanula_scheuchzeri          0.04705882
## Carex_sempervirens             0.05882353
## Cynosurus_cristatus            1.00000000

Pairwise co-occurrence Analysis with calculation of the C-score index using the function ecospat.Cscore()

This function allows to apply a pairwise null model analysis to a presence-absence community matrix to determine which species associations are significant across the study area. The strength of associations is quantified by the C-score index and a 'fixed-equiprobable' null model algorithm is applied.

It is recomended to use at least 10000 permutatiobns for the test. As an example we used nperm = 100, to reduce the computational time.

data<- ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
nperm <- 100
outpath <- getwd()
ecospat.Cscore(data, nperm, outpath)
## Computing observed co-occurence matrix 
## ............. 
## ............. 
## ............. 
## Computing permutations 
## ............. 
## 100  permutations to go 
## ............. 
## 50  permutations to go 
## ............. 
## Computing P-values 
## ............. 
## Exporting dataset 
## ............. 
## ............. 
## .............

plot of chunk Cscore

## $ObsCscoreTot
## [1] 2675.468
## 
## $SimCscoreTot
## [1] 2466.257
## 
## $PVal.less
## [1] 1
## 
## $PVal.greater
## [1] 0.00990099
## 
## $SES.Tot
## [1] 52.74909

The function returns the C-score index for the observed community (ObsCscoreTot), p.value (PValTot) and standardized effect size (SES.Tot). It saves also a table in the working directory where the same metrics are calculated for each species pair (only the table with species pairs with significant p-values is saved in this version)

Data Preparation

Correlation Plot of Variables with ecospat.cor.plot()

data <- ecospat.testData[,4:8]
ecospat.cor.plot(data)

plot of chunk cor-plot A scatter plot of matrices, with bivariate scatter plots below the diagonal, histograms on the diagonal, and the Pearson correlation above the diagonal. Useful for descriptive statistics of small data sets (better with less than 10 variables).

Calibration And Evaluation Dataset

data <- ecospat.testData
caleval <- ecospat.caleval (data = ecospat.testData[53], xy = data[2:3],
                            row.num = 1:nrow(data), nrep = 2, ratio = 0.7,
                            disaggregate = 0.2, pseudoabs = 100, npres = 10,
                            replace = FALSE)
caleval
## $eval
##    yeval yeval
## 1    159   129
## 2     NA    NA
## 3     NA    NA
## 4    297    33
## 5    295   152
## 6     21   238
## 7     75   258
## 8     71    94
## 9    155   245
## 10   286    11
## 11   211   234
## 12   133    36
## 13    43    27
## 14   221    85
## 15    34   188
## 16   212   236
## 17   271   219
## 18   253   273
## 19   123    37
## 20    55   262
## 21   113   232
## 22   251   266
## 23   220   155
## 24     4   214
## 25   192   241
## 26   248   256
## 27     8   268
## 28   293   217
## 29   225    75
## 30   266   189
## 31   294    20
## 32   233   251
## 33   184   265
## 
## $cal
##    ycal ycal
## 1    NA  108
## 2     6  227
## 3    NA   NA
## 4    NA   NA
## 5    12   NA
## 6   202   NA
## 7    NA   NA
## 8   205   51
## 9   154  267
## 10  249  199
## 11   56  243
## 12  265  169
## 13  196  181
## 14  222  201
## 15  106  133
## 16  171  290
## 17  178   53
## 18  264    3
## 19   49  180
## 20  300  230
## 21  139   56
## 22    5  255
## 23  231  244
## 24  237  250
## 25  274   79
## 26  200   23
## 27  156  182
## 28  260  157
## 29  157  286
## 30  198  198
## 31   14  247
## 32  140   22
## 33   57   17
## 34  275  100
## 35  145  259
## 36  232  274
## 37  150    5
## 38  292   24
## 39   17  233
## 40  229    2
## 41  268  204
## 42   67  240
## 43  290  246
## 44   23   44
## 45  116  254
## 46  255  134
## 47  296  283
## 48  189  168
## 49  288   71
## 50  210   95
## 51  121  261
## 52   31  206
## 53  256  253
## 54  269  229
## 55  110   15
## 56  291  106
## 57   45  279
## 58   30   16
## 59  276  203
## 60  166  224
## 61  241  272
## 62  217  186
## 63  115  193
## 64  204  270
## 65  299  185
## 66  242   84
## 67  252  114
## 68  214  121
## 69  281  239
## 70  235  223
## 71   20  154
## 72   18  139
## 73  223   30
## 74   44  263
## 75  147  120
## 76  278  289
## 77  177  228

We obtained an evaluation and calibration dataset with a desired ratio of disaggregation.

Core Niche Modelling

Model Evaluation

Presence-only Evaluation Indices- Boyce Index

The argument fit is a vector containing the predicted suitability values

fit <- ecospat.testData$glm_Saxifraga_oppositifolia

The argument obs is a vector containing the predicted suitability values of the validation points (presence records)

obs<-ecospat.testData$glm_Saxifraga_oppositifolia[which(ecospat.testData$Saxifraga_oppositifolia==1)]

Calculate and plot Boyce Index with ecospat.boyce

ecospat.boyce (fit, obs, nclass = 0, window.w = "default", res = 100, 
               PEplot = TRUE)$Spearman.cor

plot of chunk boyce

## [1] 0.91

Here the boyce index is 0.91. If the rank of predicted expected ratio would be completely ordered along habitat suitability axis then boyce index would be 1.

Accuracy of Community Prediction

Indices of accuracy of community predictions ecospat.CommunityEval()

eval<-ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
pred<-ecospat.testData[c(73:92)]
ecospat.CommunityEval (eval, pred, proba=T, ntir=5)
## trial 1 on 5 
## trial 2 on 5 
## trial 3 on 5 
## trial 4 on 5 
## trial 5 on 5
## $deviation.rich.pred
##       1  2  3   4   5
## 1    -2  0 -2   1  -3
## 2    -7 -5 -5  -8  -5
## 3    -5 -2 -5  -7  -5
## 4    -8 -5 -4  -4  -2
## 5    -9 -7 -4 -10 -11
## 6     3 -2  1  -3  -1
## 7    -6 -1 -3  -5  -6
## 8    -6 -6 -8  -5  -4
## 9     4  1  4   4   2
## 10   -2 -6 -4  -5  -1
## 11  -11 -7 -8  -9 -10
## 12    2  0  1  -1   0
## 13    0 -1 -1   2   0
## 14   -4 -4 -1  -3  -1
## 15   -3 -1 -3  -3   2
## 16   -3  1 -2  -4  -3
## 17   -3 -6 -4   0  -2
## 18   -3 -3 -4  -4  -3
## 19    5  4  3   4   2
## 20   -5 -6 -6  -6  -5
## 21   -3 -5 -3  -4  -1
## 22   -3 -5 -5  -6  -6
## 23   -6 -5 -6  -5  -8
## 24    1  2  4   3  -1
## 25   -5 -3 -5  -9  -2
## 26    0  2 -1   0   1
## 27   -7 -2 -5  -7  -6
## 28   -5 -2 -2   1  -2
## 29    1  3  2   2   1
## 30   -5 -5 -5  -5  -6
## 31   -2 -3 -2  -4  -1
## 32   -1 -1  3   3  -1
## 33   -1 -2 -1  -1  -1
## 34   -6 -6 -5  -1  -4
## 35   -2  4  4   2   2
## 36   -5 -4 -1  -2  -4
## 37    4  4  5   4   2
## 38   -3 -6 -5  -4  -4
## 39    0 -1 -2   1   1
## 40    1  0  2  -1   2
## 41    2  1  3   3   1
## 42    3  3  4   1  -1
## 43   -3  0  0   1   0
## 44    2  2  2   6   2
## 45    3  3  1   1  -3
## 46   -2 -2 -1  -1   1
## 47    1 -2 -4  -1  -2
## 48   -3  0  1   1  -2
## 49    1 -2  0  -1   0
## 50    4  1  8   2   2
## 51    7  5  6   6   5
## 52   -2 -2 -1  -3  -6
## 53   -3  4  0  -2  -1
## 54    3  3  5  -2   5
## 55   -3 -2 -5  -5  -6
## 56   -2 -8 -5  -8  -4
## 57   -2  2 -2   0   0
## 58   -1  2 -4  -2  -1
## 59    0  2 -2   1  -1
## 60   -1 -3  1   3   2
## 61    4 -1  1   1  -1
## 62    2  2  2   1   3
## 63    3  3  6   4   3
## 64   -3  2  1  -3  -2
## 65    7  3  3   6   4
## 66    5  4  6   5   8
## 67    4  1  5   3   3
## 68    1  0 -2   2  -2
## 69    3  3  5   3   2
## 70    4  5  6   7   6
## 71   -5 -1 -3  -5  -4
## 72    1  1  1   0   1
## 73    0  3  6   1   2
## 74    0 -1  1   4   3
## 75   -8 -9 -9  -8  -5
## 76    5  3  7   8   4
## 77    1  6  3   2   4
## 78    1  2  3   2   2
## 79   -5 -3 -7  -7  -3
## 80    3  0 -1  -1   4
## 81    2  7  4   4   8
## 82   -1  2  5  -1   1
## 83    7  2  5   7   4
## 84   -3 -4 -1   0  -1
## 85   -3 -2 -4  -5  -3
## 86    8  8  5   6   6
## 87    3  5  5   5   5
## 88    3  4  4   5   3
## 89   -1  0  2  -1   2
## 90    0  1  4   7   6
## 91    3  6  2   2   3
## 92    3  2  6   1  -1
## 93    6  4 -1   4   3
## 94   -6 -5 -1  -2  -4
## 95    2  2  3   3   3
## 96    4  5  4   6   7
## 97   -2 -4  0  -1  -3
## 98    3  3  2   3   3
## 99    7  5  6   7   6
## 100   6  0  1   5   2
## 101   3 -2  1  -1   2
## 102   4  1  3   4   3
## 103   0  4  2  -3   4
## 104   5  7  4   4   5
## 105   5  0  4  -1   5
## 106   5  2  3   5   3
## 107   2  3  2  -1   0
## 108   5  2  4   6   0
## 109   5  6  4   5   6
## 110  -6 -7 -6  -9 -13
## 111   5  3  2   3   1
## 112   2  4  2   5   6
## 113   5  5  3   6   2
## 114  -9 -5 -7  -4  -5
## 115  -1 -2  5  -2   2
## 116  -5 -8 -6  -6  -5
## 117   3  4  4   5   8
## 118   5  6  5   7   5
## 119  -3  0 -6  -2  -3
## 120  -4 -2 -4   0  -6
## 121  -4 -1 -3  -2   1
## 122   4  4  2   7   2
## 123   3  4  3   4   6
## 124   6  2  2   1   3
## 125  -2 -4 -2  -3  -2
## 126   1 -1  1   4   4
## 127   9  5  6   8  10
## 128   3  5  3   2   1
## 129   4  3  6   9   9
## 130  -2  2  3   1   3
## 131   1  7  2   3   5
## 132   6  4  3   7   6
## 133   1  0 -1  -2   1
## 134  -3 -6 -6  -4  -2
## 135   5  7  5   6   7
## 136   4  3  3   6   1
## 137   4  2  2   2   6
## 138  -2 -2  5  -2   5
## 139  -1  0 -1  -3  -3
## 140  -3 -2 -2  -3  -3
## 141   5  5  2   5   3
## 142   4  6  3   3   3
## 143  -3 -5 -4   2  -1
## 144   8  9  6   8   4
## 145  -3 -2  1  -4   0
## 146   2 -1  2   1   1
## 147  -1 -3  2  -1   1
## 148   3  3  4   5   3
## 149   3  7  7   5   5
## 150  -2 -6 -5  -1  -6
## 151  -2  2  0   2  -1
## 152  -1  0 -1  -6  -6
## 153   2  2  6   7   6
## 154  -1 -4 -3  -1  -3
## 155   2  1  1   0  -2
## 156  -3 -6 -2  -2  -5
## 157  -3 -3 -3  -5  -5
## 158   6  3  2   5   1
## 159   6  4  3   5   3
## 160   0 -1 -4   0  -1
## 161  -3 -2 -5  -3   1
## 162   3  1 -1  -1  -2
## 163   3  0  4   2   1
## 164   1 -2 -2  -1  -1
## 165   1  0  4   2   2
## 166  -3 -5 -5  -7  -2
## 167   0  1  2  -1   1
## 168  -7 -2 -2  -1  -2
## 169  -1 -3 -6  -3  -5
## 170   6  3  2   4   6
## 171   1 -2 -3   0  -2
## 172  -3 -1  0  -1  -3
## 173   4  4  3   5   5
## 174  -4 -1 -4   0  -2
## 175   5  5  4   3   3
## 176   1 -1  1   1   2
## 177  -1 -2 -2  -2  -1
## 178   3  4  5   2   5
## 179   2  2  7   4   6
## 180  -2 -4 -2  -2  -4
## 181  -4 -2 -5  -2  -4
## 182   4  2  0   4   4
## 183  -3  1  2   3   2
## 184   4  0  2   1   1
## 185  -3 -2  1   3   0
## 186  -3 -4 -1  -4  -2
## 187  -1  0 -4   1   2
## 188   0 -1  0  -2  -2
## 189   3  4  2   1   1
## 190   6  3  1  -1   2
## 191   3  3  2   2  -1
## 192   1  0 -3  -4   0
## 193  -4  1 -5  -1  -5
## 194   2  1  2   2   2
## 195   0  3  2   0   2
## 196  -5 -3 -1  -5  -3
## 197   4  2  4   1   2
## 198  -1 -2 -4   0  -1
## 199  -3 -1 -2  -3  -1
## 200  -1 -2 -6  -5  -2
## 201  -3  0 -4  -2   0
## 202   3  5  1   5   2
## 203  -1 -3  0  -1  -2
## 204   1 -1  1  -2   2
## 205  -2  2  1   1  -5
## 206   1 -1 -2  -3  -1
## 207   5  4  0   3   5
## 208   3 -1  2   5   2
## 209   1  2  3   2   2
## 210  -4 -4 -2  -2  -4
## 211   0 -1 -2  -2   0
## 212   0 -1  1   1   0
## 213   3  4  3   2   3
## 214  -3 -1 -2  -3  -4
## 215   0 -1  2   5   2
## 216   1  1  0   0  -1
## 217   1 -3 -2   1  -2
## 218   1  2  1   1   2
## 219   1  0 -1   1   3
## 220  -1  3  0   0  -1
## 221   0 -2 -1  -1  -1
## 222   2 -4 -2  -5   1
## 223  -1 -3 -2  -2   1
## 224   2 -1  2  -1   0
## 225   2  0  0   3   0
## 226   3  2  0   2   3
## 227  -1  3 -1   3   2
## 228  -2  0 -3  -4  -3
## 229  -4 -2 -3  -4  -2
## 230   1  2 -3   2  -1
## 231   2  1  4   1   4
## 232   2  4 -1  -2   3
## 233   1  1  0   1   1
## 234   2  1 -2   1  -3
## 235  -2 -5 -3  -3  -4
## 236  -4 -2 -1  -3  -4
## 237  -2 -4 -2  -2  -1
## 238  -2 -5 -5  -3  -3
## 239  -2  0  0   1  -3
## 240  -2 -2 -4  -1   0
## 241  -2  0 -7  -2  -1
## 242  -4 -4 -1  -2  -3
## 243   1  2  0   1   0
## 244  -1 -1  2  -2   1
## 245  -2 -4 -3  -4  -4
## 246   0 -1  0  -2  -1
## 247  -2  0 -1  -2  -3
## 248   0 -2 -2  -2   0
## 249   0  2  2   2   1
## 250  -2 -3  0  -1   1
## 251   1  0 -1   0   0
## 252  -2 -3 -2  -2   0
## 253   0 -3 -2  -2  -2
## 254  -4 -3 -2  -4  -4
## 255  -2 -5 -5  -1  -2
## 256   1 -3 -1  -2  -3
## 257   1 -2  4   1   0
## 258  -2 -1 -1  -2  -3
## 259  -3 -4 -1  -1  -1
## 260  -1  2 -1   0  -1
## 261  -1 -3 -1  -3  -1
## 262  -2 -5 -6  -1  -2
## 263  -2  0 -1  -4  -2
## 264  -3 -2 -3  -3  -2
## 265  -6  1  1  -2  -1
## 266  -1 -4 -2  -3  -2
## 267  -1 -2 -1  -1   1
## 268  -2 -1 -3  -2  -2
## 269  -2 -4 -1  -3  -5
## 270  -3 -2 -4  -2  -4
## 271  -4 -4 -3  -2  -4
## 272   0 -3 -4  -1  -1
## 273  -2 -1 -1  -5   0
## 274  -2 -4 -4  -4  -2
## 275  -2 -1 -1  -3  -1
## 276  -2 -3 -6  -2  -5
## 277   1  2  1   0   3
## 278  -4 -7 -5  -3  -2
## 279   1 -3 -5   2  -2
## 280  10  9  7   8   8
## 281   0  0 -1  -1  -1
## 282   4  2  5   6   7
## 283  -1 -4 -2   0  -1
## 284  -2  1  3   1  -3
## 285  -1 -2 -2   0  -1
## 286  -3 -2  2   0  -1
## 287   3  3 -1   2   2
## 288   0  0  0  -2  -2
## 289   2  2  1   0   0
## 290  -2 -6 -3   0  -4
## 291   1  0 -1   1  -1
## 292  -3  0  1  -2   2
## 293  -2  3  1  -1   2
## 294   1  3  3   0   2
## 295   3  3  3   2   3
## 296   0  0  2  -1  -1
## 297   0  1 -1  -1   0
## 298  -1 -1  0   2  -1
## 299  -1  0 -1  -2   0
## 300   1  0  0  -1   0
## 
## $overprediction
##              1          2          3          4          5
## 1   0.23529412 0.11764706 0.23529412 0.05882353 0.23529412
## 2   0.50000000 0.37500000 0.31250000 0.50000000 0.31250000
## 3   0.46666667 0.20000000 0.33333333 0.46666667 0.40000000
## 4   0.53333333 0.40000000 0.33333333 0.26666667 0.26666667
## 5   0.50000000 0.44444444 0.22222222 0.55555556 0.61111111
## 6   0.00000000 0.40000000 0.20000000 0.40000000 0.30000000
## 7   0.40000000 0.33333333 0.33333333 0.40000000 0.46666667
## 8   0.40000000 0.40000000 0.53333333 0.33333333 0.33333333
## 9   0.10000000 0.20000000 0.20000000 0.20000000 0.20000000
## 10  0.26666667 0.46666667 0.40000000 0.40000000 0.13333333
## 11  0.55000000 0.35000000 0.40000000 0.45000000 0.50000000
## 12  0.12500000 0.25000000 0.25000000 0.25000000 0.25000000
## 13  0.20000000 0.20000000 0.20000000 0.00000000 0.20000000
## 14  0.30769231 0.30769231 0.23076923 0.30769231 0.30769231
## 15  0.44444444 0.33333333 0.55555556 0.33333333 0.33333333
## 16  0.50000000 0.20000000 0.40000000 0.40000000 0.50000000
## 17  0.35714286 0.42857143 0.50000000 0.07142857 0.21428571
## 18  0.30769231 0.30769231 0.38461538 0.38461538 0.30769231
## 19  0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 20  0.53846154 0.53846154 0.46153846 0.53846154 0.46153846
## 21  0.41666667 0.50000000 0.50000000 0.41666667 0.16666667
## 22  0.46153846 0.53846154 0.53846154 0.53846154 0.46153846
## 23  0.43750000 0.37500000 0.43750000 0.37500000 0.50000000
## 24  0.40000000 0.30000000 0.30000000 0.30000000 0.30000000
## 25  0.37500000 0.18750000 0.31250000 0.56250000 0.25000000
## 26  0.21428571 0.07142857 0.35714286 0.14285714 0.14285714
## 27  0.35000000 0.10000000 0.25000000 0.35000000 0.30000000
## 28  0.46153846 0.30769231 0.38461538 0.23076923 0.38461538
## 29  0.25000000 0.16666667 0.25000000 0.08333333 0.25000000
## 30  0.42857143 0.35714286 0.50000000 0.35714286 0.57142857
## 31  0.40000000 0.50000000 0.40000000 0.40000000 0.40000000
## 32  0.33333333 0.22222222 0.11111111 0.33333333 0.22222222
## 33  0.23076923 0.23076923 0.15384615 0.30769231 0.23076923
## 34  0.46153846 0.46153846 0.46153846 0.30769231 0.38461538
## 35  0.20000000 0.10000000 0.10000000 0.10000000 0.10000000
## 36  0.58333333 0.33333333 0.33333333 0.41666667 0.41666667
## 37  0.14285714 0.14285714 0.00000000 0.00000000 0.28571429
## 38  0.38461538 0.53846154 0.38461538 0.46153846 0.46153846
## 39  0.50000000 0.30000000 0.40000000 0.30000000 0.40000000
## 40  0.20000000 0.30000000 0.10000000 0.20000000 0.30000000
## 41  0.22222222 0.11111111 0.11111111 0.11111111 0.22222222
## 42  0.40000000 0.20000000 0.20000000 0.40000000 0.30000000
## 43  0.33333333 0.25000000 0.33333333 0.16666667 0.16666667
## 44  0.20000000 0.20000000 0.10000000 0.10000000 0.30000000
## 45  0.10000000 0.20000000 0.10000000 0.20000000 0.40000000
## 46  0.33333333 0.33333333 0.25000000 0.25000000 0.08333333
## 47  0.14285714 0.28571429 0.35714286 0.28571429 0.35714286
## 48  0.33333333 0.33333333 0.16666667 0.33333333 0.41666667
## 49  0.33333333 0.41666667 0.16666667 0.16666667 0.25000000
## 50  0.12500000 0.12500000 0.00000000 0.25000000 0.00000000
## 51  0.11111111 0.11111111 0.00000000 0.00000000 0.11111111
## 52  0.20000000 0.33333333 0.26666667 0.33333333 0.46666667
## 53  0.36363636 0.00000000 0.27272727 0.36363636 0.36363636
## 54  0.12500000 0.12500000 0.00000000 0.37500000 0.12500000
## 55  0.26666667 0.26666667 0.40000000 0.40000000 0.46666667
## 56  0.25000000 0.50000000 0.37500000 0.50000000 0.31250000
## 57  0.36363636 0.18181818 0.45454545 0.27272727 0.45454545
## 58  0.41666667 0.08333333 0.41666667 0.33333333 0.25000000
## 59  0.33333333 0.11111111 0.33333333 0.11111111 0.33333333
## 60  0.42857143 0.28571429 0.14285714 0.07142857 0.21428571
## 61  0.00000000 0.40000000 0.30000000 0.20000000 0.20000000
## 62  0.09090909 0.09090909 0.18181818 0.09090909 0.09090909
## 63  0.27272727 0.09090909 0.18181818 0.18181818 0.09090909
## 64  0.28571429 0.07142857 0.07142857 0.35714286 0.28571429
## 65  0.10000000 0.10000000 0.40000000 0.00000000 0.20000000
## 66  0.22222222 0.11111111 0.00000000 0.11111111 0.11111111
## 67  0.22222222 0.33333333 0.11111111 0.44444444 0.22222222
## 68  0.11111111 0.33333333 0.33333333 0.11111111 0.33333333
## 69  0.18181818 0.18181818 0.09090909 0.18181818 0.36363636
## 70  0.12500000 0.12500000 0.00000000 0.12500000 0.00000000
## 71  0.50000000 0.21428571 0.35714286 0.42857143 0.42857143
## 72  0.40000000 0.30000000 0.30000000 0.30000000 0.30000000
## 73  0.11111111 0.11111111 0.11111111 0.22222222 0.11111111
## 74  0.36363636 0.36363636 0.09090909 0.09090909 0.36363636
## 75  0.40000000 0.45000000 0.45000000 0.40000000 0.25000000
## 76  0.16666667 0.16666667 0.00000000 0.00000000 0.16666667
## 77  0.25000000 0.25000000 0.25000000 0.25000000 0.12500000
## 78  0.22222222 0.11111111 0.22222222 0.22222222 0.11111111
## 79  0.33333333 0.27777778 0.44444444 0.38888889 0.27777778
## 80  0.15384615 0.30769231 0.38461538 0.23076923 0.15384615
## 81  0.12500000 0.00000000 0.12500000 0.25000000 0.12500000
## 82  0.25000000 0.33333333 0.00000000 0.33333333 0.16666667
## 83  0.00000000 0.37500000 0.12500000 0.00000000 0.37500000
## 84  0.22222222 0.33333333 0.16666667 0.05555556 0.16666667
## 85  0.23529412 0.23529412 0.29411765 0.35294118 0.29411765
## 86  0.00000000 0.10000000 0.10000000 0.10000000 0.00000000
## 87  0.22222222 0.22222222 0.33333333 0.33333333 0.22222222
## 88  0.10000000 0.10000000 0.30000000 0.20000000 0.10000000
## 89  0.33333333 0.41666667 0.25000000 0.41666667 0.25000000
## 90  0.27272727 0.27272727 0.36363636 0.00000000 0.18181818
## 91  0.20000000 0.20000000 0.30000000 0.30000000 0.20000000
## 92  0.44444444 0.11111111 0.11111111 0.22222222 0.44444444
## 93  0.00000000 0.00000000 0.37500000 0.37500000 0.12500000
## 94  0.42857143 0.42857143 0.28571429 0.28571429 0.35714286
## 95  0.50000000 0.50000000 0.25000000 0.25000000 0.25000000
## 96  0.10000000 0.10000000 0.10000000 0.00000000 0.10000000
## 97  0.38461538 0.46153846 0.15384615 0.46153846 0.38461538
## 98  0.18181818 0.18181818 0.18181818 0.18181818 0.09090909
## 99  0.11111111 0.44444444 0.00000000 0.11111111 0.11111111
## 100 0.00000000 0.16666667 0.25000000 0.16666667 0.33333333
## 101 0.15384615 0.46153846 0.30769231 0.30769231 0.38461538
## 102 0.08333333 0.16666667 0.08333333 0.16666667 0.16666667
## 103 0.25000000 0.08333333 0.08333333 0.50000000 0.16666667
## 104 0.25000000 0.25000000 0.00000000 0.25000000 0.25000000
## 105 0.07692308 0.23076923 0.07692308 0.30769231 0.00000000
## 106 0.07692308 0.23076923 0.15384615 0.00000000 0.07692308
## 107 0.14285714 0.07142857 0.14285714 0.28571429 0.28571429
## 108 0.20000000 0.40000000 0.10000000 0.10000000 0.20000000
## 109 0.33333333 0.22222222 0.33333333 0.11111111 0.11111111
## 110 0.30000000 0.35000000 0.30000000 0.45000000 0.65000000
## 111 0.08333333 0.25000000 0.16666667 0.25000000 0.16666667
## 112 0.50000000 0.30000000 0.20000000 0.10000000 0.10000000
## 113 0.16666667 0.16666667 0.33333333 0.00000000 0.16666667
## 114 0.58823529 0.35294118 0.47058824 0.35294118 0.35294118
## 115 0.41666667 0.25000000 0.16666667 0.50000000 0.25000000
## 116 0.26315789 0.42105263 0.31578947 0.31578947 0.26315789
## 117 0.33333333 0.22222222 0.11111111 0.00000000 0.11111111
## 118 0.14285714 0.00000000 0.14285714 0.00000000 0.14285714
## 119 0.23529412 0.17647059 0.35294118 0.23529412 0.17647059
## 120 0.41176471 0.29411765 0.35294118 0.17647059 0.47058824
## 121 0.35714286 0.42857143 0.42857143 0.42857143 0.28571429
## 122 0.11111111 0.11111111 0.33333333 0.33333333 0.33333333
## 123 0.27272727 0.18181818 0.27272727 0.18181818 0.09090909
## 124 0.00000000 0.14285714 0.14285714 0.28571429 0.14285714
## 125 0.22222222 0.27777778 0.22222222 0.22222222 0.22222222
## 126 0.41666667 0.25000000 0.41666667 0.16666667 0.25000000
## 127 0.11111111 0.11111111 0.22222222 0.11111111 0.00000000
## 128 0.22222222 0.11111111 0.22222222 0.44444444 0.11111111
## 129 0.10000000 0.00000000 0.00000000 0.00000000 0.10000000
## 130 0.50000000 0.40000000 0.20000000 0.30000000 0.30000000
## 131 0.40000000 0.10000000 0.10000000 0.10000000 0.20000000
## 132 0.10000000 0.20000000 0.30000000 0.10000000 0.10000000
## 133 0.20000000 0.13333333 0.26666667 0.40000000 0.13333333
## 134 0.40000000 0.53333333 0.53333333 0.40000000 0.26666667
## 135 0.11111111 0.33333333 0.11111111 0.22222222 0.22222222
## 136 0.08333333 0.16666667 0.16666667 0.00000000 0.25000000
## 137 0.09090909 0.18181818 0.36363636 0.18181818 0.09090909
## 138 0.46153846 0.38461538 0.00000000 0.30769231 0.07692308
## 139 0.31250000 0.25000000 0.31250000 0.31250000 0.31250000
## 140 0.23529412 0.17647059 0.29411765 0.17647059 0.23529412
## 141 0.08333333 0.16666667 0.16666667 0.00000000 0.16666667
## 142 0.25000000 0.08333333 0.16666667 0.16666667 0.16666667
## 143 0.31250000 0.37500000 0.37500000 0.06250000 0.18750000
## 144 0.10000000 0.00000000 0.00000000 0.00000000 0.30000000
## 145 0.40000000 0.33333333 0.13333333 0.33333333 0.26666667
## 146 0.13333333 0.20000000 0.13333333 0.20000000 0.13333333
## 147 0.12500000 0.18750000 0.12500000 0.25000000 0.06250000
## 148 0.16666667 0.25000000 0.08333333 0.16666667 0.16666667
## 149 0.30000000 0.00000000 0.20000000 0.10000000 0.20000000
## 150 0.16666667 0.38888889 0.33333333 0.11111111 0.38888889
## 151 0.42857143 0.14285714 0.21428571 0.14285714 0.28571429
## 152 0.18750000 0.18750000 0.18750000 0.43750000 0.50000000
## 153 0.18181818 0.09090909 0.00000000 0.00000000 0.00000000
## 154 0.17647059 0.29411765 0.23529412 0.17647059 0.17647059
## 155 0.13333333 0.20000000 0.13333333 0.20000000 0.33333333
## 156 0.15000000 0.30000000 0.10000000 0.10000000 0.25000000
## 157 0.15000000 0.15000000 0.15000000 0.25000000 0.25000000
## 158 0.09090909 0.09090909 0.27272727 0.00000000 0.18181818
## 159 0.18181818 0.27272727 0.36363636 0.18181818 0.09090909
## 160 0.05882353 0.17647059 0.23529412 0.17647059 0.17647059
## 161 0.37500000 0.25000000 0.31250000 0.25000000 0.18750000
## 162 0.00000000 0.13333333 0.20000000 0.26666667 0.20000000
## 163 0.06666667 0.20000000 0.06666667 0.06666667 0.13333333
## 164 0.12500000 0.25000000 0.31250000 0.25000000 0.18750000
## 165 0.06250000 0.18750000 0.00000000 0.12500000 0.06250000
## 166 0.22222222 0.38888889 0.27777778 0.44444444 0.22222222
## 167 0.15384615 0.23076923 0.23076923 0.30769231 0.23076923
## 168 0.38888889 0.16666667 0.11111111 0.16666667 0.22222222
## 169 0.10526316 0.21052632 0.31578947 0.21052632 0.31578947
## 170 0.00000000 0.07692308 0.07692308 0.00000000 0.07692308
## 171 0.06250000 0.25000000 0.25000000 0.25000000 0.18750000
## 172 0.40000000 0.26666667 0.20000000 0.20000000 0.33333333
## 173 0.20000000 0.20000000 0.20000000 0.10000000 0.20000000
## 174 0.22222222 0.16666667 0.33333333 0.11111111 0.22222222
## 175 0.08333333 0.08333333 0.16666667 0.25000000 0.25000000
## 176 0.14285714 0.21428571 0.21428571 0.21428571 0.14285714
## 177 0.05882353 0.23529412 0.11764706 0.17647059 0.17647059
## 178 0.27272727 0.27272727 0.09090909 0.27272727 0.18181818
## 179 0.16666667 0.25000000 0.08333333 0.00000000 0.08333333
## 180 0.23529412 0.35294118 0.23529412 0.17647059 0.35294118
## 181 0.20000000 0.10000000 0.25000000 0.10000000 0.20000000
## 182 0.07142857 0.21428571 0.21428571 0.14285714 0.07142857
## 183 0.40000000 0.20000000 0.00000000 0.06666667 0.00000000
## 184 0.00000000 0.20000000 0.06666667 0.06666667 0.06666667
## 185 0.33333333 0.26666667 0.20000000 0.06666667 0.20000000
## 186 0.21052632 0.21052632 0.10526316 0.21052632 0.10526316
## 187 0.26666667 0.20000000 0.40000000 0.13333333 0.13333333
## 188 0.11764706 0.17647059 0.11764706 0.17647059 0.29411765
## 189 0.06666667 0.00000000 0.20000000 0.20000000 0.13333333
## 190 0.08333333 0.33333333 0.16666667 0.50000000 0.33333333
## 191 0.06666667 0.13333333 0.00000000 0.13333333 0.33333333
## 192 0.05882353 0.11764706 0.35294118 0.41176471 0.17647059
## 193 0.21052632 0.00000000 0.26315789 0.10526316 0.26315789
## 194 0.14285714 0.21428571 0.07142857 0.21428571 0.07142857
## 195 0.30769231 0.00000000 0.07692308 0.30769231 0.07692308
## 196 0.26315789 0.15789474 0.10526316 0.31578947 0.21052632
## 197 0.00000000 0.08333333 0.08333333 0.16666667 0.25000000
## 198 0.16666667 0.22222222 0.27777778 0.05555556 0.11111111
## 199 0.22222222 0.16666667 0.22222222 0.27777778 0.11111111
## 200 0.11111111 0.22222222 0.38888889 0.27777778 0.11111111
## 201 0.16666667 0.05555556 0.22222222 0.16666667 0.11111111
## 202 0.16666667 0.00000000 0.25000000 0.08333333 0.08333333
## 203 0.25000000 0.43750000 0.12500000 0.25000000 0.37500000
## 204 0.12500000 0.12500000 0.00000000 0.18750000 0.06250000
## 205 0.17647059 0.00000000 0.05882353 0.11764706 0.35294118
## 206 0.05555556 0.11111111 0.16666667 0.27777778 0.16666667
## 207 0.00000000 0.00000000 0.14285714 0.00000000 0.00000000
## 208 0.00000000 0.26666667 0.13333333 0.00000000 0.13333333
## 209 0.28571429 0.14285714 0.07142857 0.07142857 0.14285714
## 210 0.21052632 0.21052632 0.10526316 0.10526316 0.26315789
## 211 0.11111111 0.11111111 0.11111111 0.11111111 0.11111111
## 212 0.06250000 0.12500000 0.06250000 0.12500000 0.06250000
## 213 0.06666667 0.00000000 0.06666667 0.13333333 0.13333333
## 214 0.17647059 0.11764706 0.17647059 0.23529412 0.23529412
## 215 0.20000000 0.26666667 0.13333333 0.00000000 0.13333333
## 216 0.06250000 0.12500000 0.12500000 0.25000000 0.25000000
## 217 0.18750000 0.31250000 0.31250000 0.12500000 0.37500000
## 218 0.06666667 0.06666667 0.13333333 0.06666667 0.13333333
## 219 0.20000000 0.20000000 0.26666667 0.06666667 0.06666667
## 220 0.13333333 0.06666667 0.06666667 0.06666667 0.26666667
## 221 0.05882353 0.11764706 0.11764706 0.11764706 0.17647059
## 222 0.05882353 0.35294118 0.17647059 0.35294118 0.00000000
## 223 0.10526316 0.15789474 0.15789474 0.15789474 0.00000000
## 224 0.06250000 0.25000000 0.06250000 0.18750000 0.12500000
## 225 0.06250000 0.00000000 0.18750000 0.00000000 0.12500000
## 226 0.00000000 0.00000000 0.07142857 0.07142857 0.07142857
## 227 0.28571429 0.14285714 0.21428571 0.00000000 0.21428571
## 228 0.15789474 0.05263158 0.21052632 0.21052632 0.15789474
## 229 0.26315789 0.10526316 0.21052632 0.21052632 0.10526316
## 230 0.06250000 0.06250000 0.31250000 0.00000000 0.12500000
## 231 0.06666667 0.06666667 0.00000000 0.20000000 0.06666667
## 232 0.13333333 0.00000000 0.13333333 0.33333333 0.06666667
## 233 0.06250000 0.12500000 0.12500000 0.06250000 0.12500000
## 234 0.05882353 0.11764706 0.23529412 0.05882353 0.17647059
## 235 0.15789474 0.26315789 0.21052632 0.21052632 0.26315789
## 236 0.29411765 0.23529412 0.17647059 0.29411765 0.29411765
## 237 0.22222222 0.22222222 0.16666667 0.22222222 0.16666667
## 238 0.15789474 0.26315789 0.26315789 0.15789474 0.15789474
## 239 0.23529412 0.11764706 0.11764706 0.05882353 0.29411765
## 240 0.16666667 0.16666667 0.22222222 0.16666667 0.05555556
## 241 0.15789474 0.05263158 0.36842105 0.15789474 0.10526316
## 242 0.26315789 0.26315789 0.10526316 0.15789474 0.21052632
## 243 0.00000000 0.00000000 0.17647059 0.11764706 0.11764706
## 244 0.11764706 0.17647059 0.05882353 0.17647059 0.05882353
## 245 0.10000000 0.20000000 0.15000000 0.20000000 0.20000000
## 246 0.05263158 0.05263158 0.05263158 0.10526316 0.05263158
## 247 0.10526316 0.05263158 0.05263158 0.10526316 0.21052632
## 248 0.11764706 0.17647059 0.17647059 0.17647059 0.11764706
## 249 0.06250000 0.06250000 0.00000000 0.06250000 0.12500000
## 250 0.11764706 0.17647059 0.00000000 0.11764706 0.05882353
## 251 0.11764706 0.11764706 0.17647059 0.17647059 0.11764706
## 252 0.10000000 0.15000000 0.10000000 0.10000000 0.00000000
## 253 0.05263158 0.21052632 0.15789474 0.15789474 0.10526316
## 254 0.21052632 0.15789474 0.10526316 0.26315789 0.21052632
## 255 0.22222222 0.27777778 0.27777778 0.11111111 0.11111111
## 256 0.00000000 0.16666667 0.11111111 0.11111111 0.22222222
## 257 0.18750000 0.18750000 0.00000000 0.12500000 0.18750000
## 258 0.16666667 0.11111111 0.11111111 0.16666667 0.22222222
## 259 0.16666667 0.22222222 0.05555556 0.11111111 0.16666667
## 260 0.05555556 0.00000000 0.05555556 0.11111111 0.11111111
## 261 0.16666667 0.22222222 0.16666667 0.27777778 0.11111111
## 262 0.10000000 0.25000000 0.30000000 0.05000000 0.10000000
## 263 0.10000000 0.00000000 0.05000000 0.20000000 0.10000000
## 264 0.15789474 0.10526316 0.15789474 0.15789474 0.10526316
## 265 0.35294118 0.05882353 0.00000000 0.17647059 0.05882353
## 266 0.05263158 0.21052632 0.10526316 0.15789474 0.10526316
## 267 0.05555556 0.11111111 0.11111111 0.05555556 0.00000000
## 268 0.11111111 0.05555556 0.22222222 0.11111111 0.11111111
## 269 0.10526316 0.21052632 0.05263158 0.15789474 0.26315789
## 270 0.15789474 0.10526316 0.21052632 0.10526316 0.21052632
## 271 0.21052632 0.21052632 0.15789474 0.10526316 0.21052632
## 272 0.05263158 0.21052632 0.21052632 0.05263158 0.10526316
## 273 0.16666667 0.11111111 0.16666667 0.27777778 0.11111111
## 274 0.10526316 0.21052632 0.21052632 0.21052632 0.10526316
## 275 0.16666667 0.11111111 0.05555556 0.16666667 0.11111111
## 276 0.42857143 0.35714286 0.57142857 0.35714286 0.42857143
## 277 0.25000000 0.25000000 0.33333333 0.41666667 0.08333333
## 278 0.30769231 0.53846154 0.38461538 0.23076923 0.30769231
## 279 0.30769231 0.53846154 0.61538462 0.30769231 0.38461538
## 280 0.00000000 0.11111111 0.11111111 0.00000000 0.22222222
## 281 0.05263158 0.05263158 0.10526316 0.10526316 0.10526316
## 282 0.15384615 0.23076923 0.15384615 0.00000000 0.00000000
## 283 0.11111111 0.22222222 0.16666667 0.11111111 0.11111111
## 284 0.28571429 0.14285714 0.07142857 0.28571429 0.28571429
## 285 0.17647059 0.23529412 0.29411765 0.11764706 0.05882353
## 286 0.23529412 0.23529412 0.05882353 0.17647059 0.11764706
## 287 0.00000000 0.06666667 0.20000000 0.13333333 0.06666667
## 288 0.17647059 0.11764706 0.11764706 0.17647059 0.23529412
## 289 0.12500000 0.06250000 0.12500000 0.12500000 0.12500000
## 290 0.16666667 0.33333333 0.22222222 0.11111111 0.22222222
## 291 0.11764706 0.17647059 0.17647059 0.11764706 0.11764706
## 292 0.23529412 0.17647059 0.05882353 0.17647059 0.00000000
## 293 0.25000000 0.00000000 0.06250000 0.18750000 0.00000000
## 294 0.12500000 0.00000000 0.06250000 0.12500000 0.06250000
## 295 0.00000000 0.06250000 0.00000000 0.06250000 0.06250000
## 296 0.05882353 0.11764706 0.05882353 0.11764706 0.11764706
## 297 0.17647059 0.05882353 0.11764706 0.11764706 0.11764706
## 298 0.30000000 0.30000000 0.40000000 0.10000000 0.20000000
## 299 0.05555556 0.05555556 0.05555556 0.16666667 0.05555556
## 300 0.00000000 0.05555556 0.11111111 0.16666667 0.05555556
## 
## $underprediction
##              1          2          3          4          5
## 1   0.66666667 0.66666667 0.66666667 0.66666667 0.33333333
## 2   0.25000000 0.25000000 0.00000000 0.00000000 0.00000000
## 3   0.40000000 0.20000000 0.00000000 0.00000000 0.20000000
## 4   0.00000000 0.20000000 0.20000000 0.00000000 0.40000000
## 5   0.00000000 0.50000000 0.00000000 0.00000000 0.00000000
## 6   0.30000000 0.20000000 0.30000000 0.10000000 0.20000000
## 7   0.00000000 0.80000000 0.40000000 0.20000000 0.20000000
## 8   0.00000000 0.00000000 0.00000000 0.00000000 0.20000000
## 9   0.50000000 0.30000000 0.60000000 0.60000000 0.40000000
## 10  0.40000000 0.20000000 0.40000000 0.20000000 0.20000000
## 11         NaN        NaN        NaN        NaN        NaN
## 12  0.25000000 0.16666667 0.25000000 0.08333333 0.16666667
## 13  0.20000000 0.10000000 0.10000000 0.20000000 0.20000000
## 14  0.00000000 0.00000000 0.28571429 0.14285714 0.42857143
## 15  0.09090909 0.18181818 0.18181818 0.00000000 0.45454545
## 16  0.20000000 0.30000000 0.20000000 0.00000000 0.20000000
## 17  0.33333333 0.00000000 0.50000000 0.16666667 0.16666667
## 18  0.14285714 0.14285714 0.14285714 0.14285714 0.14285714
## 19  0.33333333 0.26666667 0.20000000 0.26666667 0.13333333
## 20  0.28571429 0.14285714 0.00000000 0.14285714 0.14285714
## 21  0.25000000 0.12500000 0.37500000 0.12500000 0.12500000
## 22  0.42857143 0.28571429 0.28571429 0.14285714 0.00000000
## 23  0.25000000 0.25000000 0.25000000 0.25000000 0.00000000
## 24  0.50000000 0.50000000 0.70000000 0.60000000 0.20000000
## 25  0.25000000 0.00000000 0.00000000 0.00000000 0.50000000
## 26  0.50000000 0.50000000 0.66666667 0.33333333 0.50000000
## 27         NaN        NaN        NaN        NaN        NaN
## 28  0.14285714 0.28571429 0.42857143 0.57142857 0.42857143
## 29  0.50000000 0.62500000 0.62500000 0.37500000 0.50000000
## 30  0.16666667 0.00000000 0.33333333 0.00000000 0.33333333
## 31  0.20000000 0.20000000 0.20000000 0.00000000 0.30000000
## 32  0.18181818 0.09090909 0.36363636 0.54545455 0.09090909
## 33  0.28571429 0.14285714 0.14285714 0.42857143 0.28571429
## 34  0.00000000 0.00000000 0.14285714 0.42857143 0.14285714
## 35  0.00000000 0.50000000 0.50000000 0.30000000 0.30000000
## 36  0.25000000 0.00000000 0.37500000 0.37500000 0.12500000
## 37  0.38461538 0.38461538 0.38461538 0.30769231 0.30769231
## 38  0.28571429 0.14285714 0.00000000 0.28571429 0.28571429
## 39  0.50000000 0.20000000 0.20000000 0.40000000 0.50000000
## 40  0.30000000 0.30000000 0.30000000 0.10000000 0.50000000
## 41  0.36363636 0.18181818 0.36363636 0.36363636 0.27272727
## 42  0.70000000 0.50000000 0.60000000 0.50000000 0.20000000
## 43  0.12500000 0.37500000 0.50000000 0.37500000 0.25000000
## 44  0.40000000 0.40000000 0.30000000 0.70000000 0.50000000
## 45  0.40000000 0.50000000 0.20000000 0.30000000 0.10000000
## 46  0.25000000 0.25000000 0.25000000 0.25000000 0.25000000
## 47  0.50000000 0.33333333 0.16666667 0.50000000 0.50000000
## 48  0.12500000 0.50000000 0.37500000 0.62500000 0.37500000
## 49  0.62500000 0.37500000 0.25000000 0.12500000 0.37500000
## 50  0.41666667 0.16666667 0.66666667 0.33333333 0.16666667
## 51  0.72727273 0.54545455 0.54545455 0.54545455 0.54545455
## 52  0.20000000 0.60000000 0.60000000 0.40000000 0.20000000
## 53  0.11111111 0.44444444 0.33333333 0.22222222 0.33333333
## 54  0.33333333 0.33333333 0.41666667 0.08333333 0.50000000
## 55  0.20000000 0.40000000 0.20000000 0.20000000 0.20000000
## 56  0.50000000 0.00000000 0.25000000 0.00000000 0.25000000
## 57  0.22222222 0.44444444 0.33333333 0.33333333 0.55555556
## 58  0.50000000 0.37500000 0.12500000 0.25000000 0.25000000
## 59  0.27272727 0.27272727 0.09090909 0.18181818 0.18181818
## 60  0.83333333 0.16666667 0.50000000 0.66666667 0.83333333
## 61  0.40000000 0.30000000 0.40000000 0.30000000 0.10000000
## 62  0.33333333 0.33333333 0.44444444 0.22222222 0.44444444
## 63  0.66666667 0.44444444 0.88888889 0.66666667 0.44444444
## 64  0.16666667 0.50000000 0.33333333 0.33333333 0.33333333
## 65  0.80000000 0.40000000 0.70000000 0.60000000 0.60000000
## 66  0.63636364 0.45454545 0.54545455 0.54545455 0.81818182
## 67  0.54545455 0.36363636 0.54545455 0.63636364 0.45454545
## 68  0.18181818 0.27272727 0.09090909 0.27272727 0.09090909
## 69  0.55555556 0.55555556 0.66666667 0.55555556 0.66666667
## 70  0.41666667 0.50000000 0.50000000 0.66666667 0.50000000
## 71  0.33333333 0.33333333 0.33333333 0.16666667 0.33333333
## 72  0.50000000 0.40000000 0.40000000 0.30000000 0.40000000
## 73  0.09090909 0.36363636 0.63636364 0.27272727 0.27272727
## 74  0.44444444 0.33333333 0.22222222 0.55555556 0.77777778
## 75         NaN        NaN        NaN        NaN        NaN
## 76  0.42857143 0.28571429 0.50000000 0.57142857 0.35714286
## 77  0.25000000 0.66666667 0.41666667 0.33333333 0.41666667
## 78  0.27272727 0.27272727 0.45454545 0.36363636 0.27272727
## 79  0.50000000 1.00000000 0.50000000 0.00000000 1.00000000
## 80  0.71428571 0.57142857 0.57142857 0.28571429 0.85714286
## 81  0.25000000 0.58333333 0.41666667 0.50000000 0.75000000
## 82  0.25000000 0.75000000 0.62500000 0.37500000 0.37500000
## 83  0.58333333 0.41666667 0.50000000 0.58333333 0.58333333
## 84  0.50000000 1.00000000 1.00000000 0.50000000 1.00000000
## 85  0.33333333 0.66666667 0.33333333 0.33333333 0.66666667
## 86  0.80000000 0.90000000 0.60000000 0.70000000 0.60000000
## 87  0.45454545 0.63636364 0.72727273 0.72727273 0.63636364
## 88  0.40000000 0.50000000 0.70000000 0.70000000 0.40000000
## 89  0.37500000 0.62500000 0.62500000 0.50000000 0.62500000
## 90  0.33333333 0.44444444 0.88888889 0.77777778 0.88888889
## 91  0.50000000 0.80000000 0.50000000 0.50000000 0.50000000
## 92  0.63636364 0.27272727 0.63636364 0.27272727 0.27272727
## 93  0.50000000 0.33333333 0.16666667 0.58333333 0.33333333
## 94  0.00000000 0.16666667 0.50000000 0.33333333 0.16666667
## 95  0.50000000 0.50000000 0.41666667 0.41666667 0.41666667
## 96  0.50000000 0.60000000 0.50000000 0.60000000 0.80000000
## 97  0.42857143 0.28571429 0.28571429 0.71428571 0.28571429
## 98  0.55555556 0.55555556 0.44444444 0.55555556 0.44444444
## 99  0.72727273 0.81818182 0.54545455 0.72727273 0.63636364
## 100 0.75000000 0.25000000 0.50000000 0.87500000 0.75000000
## 101 0.71428571 0.57142857 0.71428571 0.42857143 1.00000000
## 102 0.62500000 0.37500000 0.50000000 0.75000000 0.62500000
## 103 0.37500000 0.62500000 0.37500000 0.37500000 0.75000000
## 104 0.58333333 0.75000000 0.33333333 0.50000000 0.58333333
## 105 0.85714286 0.42857143 0.71428571 0.42857143 0.71428571
## 106 0.85714286 0.71428571 0.71428571 0.71428571 0.57142857
## 107 0.66666667 0.66666667 0.66666667 0.50000000 0.66666667
## 108 0.70000000 0.60000000 0.50000000 0.70000000 0.20000000
## 109 0.72727273 0.72727273 0.63636364 0.54545455 0.63636364
## 110        NaN        NaN        NaN        NaN        NaN
## 111 0.75000000 0.75000000 0.50000000 0.75000000 0.37500000
## 112 0.70000000 0.70000000 0.40000000 0.60000000 0.70000000
## 113 0.87500000 0.87500000 0.87500000 0.75000000 0.50000000
## 114 0.33333333 0.33333333 0.33333333 0.66666667 0.33333333
## 115 0.50000000 0.12500000 0.87500000 0.50000000 0.62500000
## 116 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 117 0.54545455 0.54545455 0.45454545 0.45454545 0.81818182
## 118 0.46153846 0.46153846 0.46153846 0.53846154 0.46153846
## 119 0.33333333 1.00000000 0.00000000 0.66666667 0.00000000
## 120 1.00000000 1.00000000 0.66666667 1.00000000 0.66666667
## 121 0.16666667 0.83333333 0.50000000 0.66666667 0.83333333
## 122 0.45454545 0.45454545 0.45454545 0.90909091 0.45454545
## 123 0.66666667 0.66666667 0.66666667 0.66666667 0.77777778
## 124 1.00000000 0.66666667 0.66666667 0.83333333 0.83333333
## 125 1.00000000 0.50000000 1.00000000 0.50000000 1.00000000
## 126 0.75000000 0.25000000 0.75000000 0.75000000 0.87500000
## 127 0.90909091 0.54545455 0.72727273 0.81818182 0.90909091
## 128 0.45454545 0.54545455 0.45454545 0.54545455 0.18181818
## 129 0.50000000 0.30000000 0.60000000 0.90000000 1.00000000
## 130 0.30000000 0.60000000 0.50000000 0.40000000 0.60000000
## 131 0.50000000 0.80000000 0.30000000 0.40000000 0.70000000
## 132 0.70000000 0.60000000 0.60000000 0.80000000 0.70000000
## 133 0.80000000 0.40000000 0.60000000 0.80000000 0.60000000
## 134 0.60000000 0.40000000 0.40000000 0.40000000 0.40000000
## 135 0.54545455 0.90909091 0.54545455 0.72727273 0.81818182
## 136 0.62500000 0.62500000 0.62500000 0.75000000 0.50000000
## 137 0.55555556 0.44444444 0.66666667 0.44444444 0.77777778
## 138 0.57142857 0.42857143 0.71428571 0.28571429 0.85714286
## 139 1.00000000 1.00000000 1.00000000 0.50000000 0.50000000
## 140 0.33333333 0.33333333 1.00000000 0.00000000 0.33333333
## 141 0.75000000 0.87500000 0.50000000 0.62500000 0.62500000
## 142 0.87500000 0.87500000 0.62500000 0.62500000 0.62500000
## 143 0.50000000 0.25000000 0.50000000 0.75000000 0.50000000
## 144 0.90000000 0.90000000 0.60000000 0.80000000 0.70000000
## 145 0.60000000 0.60000000 0.60000000 0.20000000 0.80000000
## 146 0.80000000 0.40000000 0.80000000 0.80000000 0.60000000
## 147 0.25000000 0.00000000 1.00000000 0.75000000 0.50000000
## 148 0.62500000 0.75000000 0.62500000 0.87500000 0.62500000
## 149 0.60000000 0.70000000 0.90000000 0.60000000 0.70000000
## 150 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000
## 151 0.66666667 0.66666667 0.50000000 0.66666667 0.50000000
## 152 0.50000000 0.75000000 0.50000000 0.25000000 0.50000000
## 153 0.44444444 0.33333333 0.66666667 0.77777778 0.66666667
## 154 0.66666667 0.33333333 0.33333333 0.66666667 0.00000000
## 155 0.80000000 0.80000000 0.60000000 0.60000000 0.60000000
## 156        NaN        NaN        NaN        NaN        NaN
## 157        NaN        NaN        NaN        NaN        NaN
## 158 0.77777778 0.44444444 0.55555556 0.55555556 0.33333333
## 159 0.88888889 0.77777778 0.77777778 0.77777778 0.44444444
## 160 0.33333333 0.66666667 0.00000000 1.00000000 0.66666667
## 161 0.75000000 0.50000000 0.00000000 0.25000000 1.00000000
## 162 0.60000000 0.60000000 0.40000000 0.60000000 0.20000000
## 163 0.80000000 0.60000000 1.00000000 0.60000000 0.60000000
## 164 0.75000000 0.50000000 0.75000000 0.75000000 0.50000000
## 165 0.50000000 0.75000000 1.00000000 1.00000000 0.75000000
## 166 0.50000000 1.00000000 0.00000000 0.50000000 1.00000000
## 167 0.28571429 0.57142857 0.71428571 0.42857143 0.57142857
## 168 0.00000000 0.50000000 0.00000000 1.00000000 1.00000000
## 169 1.00000000 1.00000000 0.00000000 1.00000000 1.00000000
## 170 0.85714286 0.57142857 0.42857143 0.57142857 1.00000000
## 171 0.50000000 0.50000000 0.25000000 1.00000000 0.25000000
## 172 0.60000000 0.60000000 0.60000000 0.40000000 0.40000000
## 173 0.60000000 0.60000000 0.50000000 0.60000000 0.70000000
## 174 0.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## 175 0.75000000 0.75000000 0.75000000 0.75000000 0.75000000
## 176 0.50000000 0.33333333 0.66666667 0.66666667 0.66666667
## 177 0.00000000 0.66666667 0.00000000 0.33333333 0.66666667
## 178 0.66666667 0.77777778 0.66666667 0.55555556 0.77777778
## 179 0.50000000 0.62500000 1.00000000 0.50000000 0.87500000
## 180 0.66666667 0.66666667 0.66666667 0.33333333 0.66666667
## 181        NaN        NaN        NaN        NaN        NaN
## 182 0.83333333 0.83333333 0.50000000 1.00000000 0.83333333
## 183 0.60000000 0.80000000 0.40000000 0.80000000 0.40000000
## 184 0.80000000 0.60000000 0.60000000 0.40000000 0.40000000
## 185 0.40000000 0.40000000 0.80000000 0.80000000 0.60000000
## 186 1.00000000 0.00000000 1.00000000 0.00000000 0.00000000
## 187 0.60000000 0.60000000 0.40000000 0.60000000 0.80000000
## 188 0.66666667 0.66666667 0.66666667 0.33333333 1.00000000
## 189 0.80000000 0.80000000 1.00000000 0.80000000 0.60000000
## 190 0.87500000 0.87500000 0.37500000 0.62500000 0.75000000
## 191 0.80000000 1.00000000 0.40000000 0.80000000 0.80000000
## 192 0.66666667 0.66666667 1.00000000 1.00000000 1.00000000
## 193 0.00000000 1.00000000 0.00000000 1.00000000 0.00000000
## 194 0.66666667 0.66666667 0.50000000 0.83333333 0.50000000
## 195 0.57142857 0.42857143 0.42857143 0.57142857 0.42857143
## 196 0.00000000 0.00000000 1.00000000 1.00000000 1.00000000
## 197 0.50000000 0.37500000 0.62500000 0.37500000 0.62500000
## 198 1.00000000 1.00000000 0.50000000 0.50000000 0.50000000
## 199 0.50000000 1.00000000 1.00000000 1.00000000 0.50000000
## 200 0.50000000 1.00000000 0.50000000 0.00000000 0.00000000
## 201 0.00000000 0.50000000 0.00000000 0.50000000 1.00000000
## 202 0.62500000 0.62500000 0.50000000 0.75000000 0.37500000
## 203 0.75000000 1.00000000 0.50000000 0.75000000 1.00000000
## 204 0.75000000 0.25000000 0.25000000 0.25000000 0.75000000
## 205 0.33333333 0.66666667 0.66666667 1.00000000 0.33333333
## 206 1.00000000 0.50000000 0.50000000 1.00000000 1.00000000
## 207 0.83333333 0.66666667 0.33333333 0.50000000 0.83333333
## 208 0.60000000 0.60000000 0.80000000 1.00000000 0.80000000
## 209 0.83333333 0.66666667 0.66666667 0.50000000 0.66666667
## 210 0.00000000 0.00000000 0.00000000 0.00000000 1.00000000
## 211 1.00000000 0.50000000 0.00000000 0.00000000 1.00000000
## 212 0.25000000 0.25000000 0.50000000 0.75000000 0.25000000
## 213 0.80000000 0.80000000 0.80000000 0.80000000 1.00000000
## 214 0.00000000 0.33333333 0.33333333 0.33333333 0.00000000
## 215 0.60000000 0.60000000 0.80000000 1.00000000 0.80000000
## 216 0.50000000 0.75000000 0.50000000 1.00000000 0.75000000
## 217 1.00000000 0.50000000 0.75000000 0.75000000 1.00000000
## 218 0.40000000 0.60000000 0.60000000 0.40000000 0.80000000
## 219 0.80000000 0.60000000 0.60000000 0.40000000 0.80000000
## 220 0.20000000 0.80000000 0.20000000 0.20000000 0.60000000
## 221 0.33333333 0.00000000 0.33333333 0.33333333 0.66666667
## 222 1.00000000 0.66666667 0.33333333 0.33333333 0.33333333
## 223 1.00000000 0.00000000 1.00000000 1.00000000 1.00000000
## 224 0.75000000 0.75000000 0.75000000 0.50000000 0.50000000
## 225 0.75000000 0.00000000 0.75000000 0.75000000 0.50000000
## 226 0.50000000 0.33333333 0.16666667 0.50000000 0.66666667
## 227 0.50000000 0.83333333 0.33333333 0.50000000 0.83333333
## 228 1.00000000 1.00000000 1.00000000 0.00000000 0.00000000
## 229 1.00000000 0.00000000 1.00000000 0.00000000 0.00000000
## 230 0.50000000 0.75000000 0.50000000 0.50000000 0.25000000
## 231 0.60000000 0.40000000 0.80000000 0.80000000 1.00000000
## 232 0.80000000 0.80000000 0.20000000 0.60000000 0.80000000
## 233 0.50000000 0.75000000 0.50000000 0.50000000 0.75000000
## 234 1.00000000 1.00000000 0.66666667 0.66666667 0.00000000
## 235 1.00000000 0.00000000 1.00000000 1.00000000 1.00000000
## 236 0.33333333 0.66666667 0.66666667 0.66666667 0.33333333
## 237 1.00000000 0.00000000 0.50000000 1.00000000 1.00000000
## 238 1.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 239 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667
## 240 0.50000000 0.50000000 0.00000000 1.00000000 0.50000000
## 241 1.00000000 1.00000000 0.00000000 1.00000000 1.00000000
## 242 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## 243 0.33333333 0.66666667 1.00000000 1.00000000 0.66666667
## 244 0.33333333 0.66666667 1.00000000 0.33333333 0.66666667
## 245        NaN        NaN        NaN        NaN        NaN
## 246 1.00000000 0.00000000 1.00000000 0.00000000 0.00000000
## 247 0.00000000 1.00000000 0.00000000 0.00000000 1.00000000
## 248 0.66666667 0.33333333 0.33333333 0.33333333 0.66666667
## 249 0.25000000 0.75000000 0.50000000 0.75000000 0.75000000
## 250 0.00000000 0.00000000 0.00000000 0.33333333 0.66666667
## 251 1.00000000 0.66666667 0.66666667 1.00000000 0.66666667
## 252        NaN        NaN        NaN        NaN        NaN
## 253 1.00000000 1.00000000 1.00000000 1.00000000 0.00000000
## 254 0.00000000 0.00000000 0.00000000 1.00000000 0.00000000
## 255 1.00000000 0.00000000 0.00000000 0.50000000 0.00000000
## 256 0.50000000 0.00000000 0.50000000 0.00000000 0.50000000
## 257 1.00000000 0.25000000 1.00000000 0.75000000 0.75000000
## 258 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000
## 259 0.00000000 0.00000000 0.00000000 0.50000000 1.00000000
## 260 0.00000000 1.00000000 0.00000000 1.00000000 0.50000000
## 261 1.00000000 0.50000000 1.00000000 1.00000000 0.50000000
## 262        NaN        NaN        NaN        NaN        NaN
## 263        NaN        NaN        NaN        NaN        NaN
## 264 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 265 0.00000000 0.66666667 0.33333333 0.33333333 0.00000000
## 266 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 267 0.00000000 0.00000000 0.50000000 0.00000000 0.50000000
## 268 0.00000000 0.00000000 0.50000000 0.00000000 0.00000000
## 269 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 270 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 271 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 272 1.00000000 1.00000000 0.00000000 0.00000000 1.00000000
## 273 0.50000000 0.50000000 1.00000000 0.00000000 1.00000000
## 274 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 275 0.50000000 0.50000000 0.00000000 0.00000000 0.50000000
## 276 0.66666667 0.33333333 0.33333333 0.50000000 0.16666667
## 277 0.50000000 0.62500000 0.62500000 0.62500000 0.50000000
## 278 0.00000000 0.00000000 0.00000000 0.00000000 0.28571429
## 279 0.71428571 0.57142857 0.42857143 0.85714286 0.42857143
## 280 0.90909091 0.90909091 0.72727273 0.72727273 0.90909091
## 281 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## 282 0.85714286 0.71428571 1.00000000 0.85714286 1.00000000
## 283 0.50000000 0.00000000 0.50000000 1.00000000 0.50000000
## 284 0.33333333 0.50000000 0.66666667 0.83333333 0.16666667
## 285 0.66666667 0.66666667 1.00000000 0.66666667 0.00000000
## 286 0.33333333 0.66666667 1.00000000 1.00000000 0.33333333
## 287 0.60000000 0.80000000 0.40000000 0.80000000 0.60000000
## 288 1.00000000 0.66666667 0.66666667 0.33333333 0.66666667
## 289 1.00000000 0.75000000 0.75000000 0.50000000 0.50000000
## 290 0.50000000 0.00000000 0.50000000 1.00000000 0.00000000
## 291 1.00000000 1.00000000 0.66666667 1.00000000 0.33333333
## 292 0.33333333 1.00000000 0.66666667 0.33333333 0.66666667
## 293 0.50000000 0.75000000 0.50000000 0.50000000 0.50000000
## 294 0.75000000 0.75000000 1.00000000 0.50000000 0.75000000
## 295 0.75000000 1.00000000 0.75000000 0.75000000 1.00000000
## 296 0.33333333 0.66666667 1.00000000 0.33333333 0.33333333
## 297 1.00000000 0.66666667 0.33333333 0.33333333 0.66666667
## 298 0.20000000 0.20000000 0.40000000 0.30000000 0.10000000
## 299 0.00000000 0.50000000 0.00000000 0.50000000 0.50000000
## 300 0.50000000 0.50000000 1.00000000 1.00000000 0.50000000
## 
## $prediction.success
##        1    2    3    4    5
## 1   0.70 0.80 0.70 0.85 0.75
## 2   0.55 0.65 0.75 0.60 0.75
## 3   0.55 0.80 0.75 0.65 0.65
## 4   0.60 0.65 0.70 0.80 0.70
## 5   0.55 0.55 0.80 0.50 0.45
## 6   0.85 0.70 0.75 0.75 0.75
## 7   0.70 0.55 0.65 0.65 0.60
## 8   0.70 0.70 0.60 0.75 0.70
## 9   0.70 0.75 0.60 0.60 0.70
## 10  0.70 0.60 0.60 0.65 0.85
## 11  0.45 0.65 0.60 0.55 0.50
## 12  0.80 0.80 0.75 0.85 0.80
## 13  0.80 0.85 0.85 0.90 0.80
## 14  0.80 0.80 0.75 0.75 0.65
## 15  0.75 0.75 0.65 0.85 0.60
## 16  0.65 0.75 0.70 0.80 0.65
## 17  0.65 0.70 0.50 0.90 0.80
## 18  0.75 0.75 0.70 0.70 0.75
## 19  0.75 0.80 0.85 0.80 0.90
## 20  0.55 0.60 0.70 0.60 0.65
## 21  0.65 0.65 0.55 0.70 0.85
## 22  0.55 0.55 0.55 0.60 0.70
## 23  0.60 0.65 0.60 0.65 0.60
## 24  0.55 0.60 0.50 0.55 0.75
## 25  0.65 0.85 0.75 0.55 0.70
## 26  0.70 0.80 0.55 0.80 0.75
## 27  0.65 0.90 0.75 0.65 0.70
## 28  0.65 0.70 0.60 0.65 0.60
## 29  0.65 0.65 0.60 0.80 0.65
## 30  0.65 0.75 0.55 0.75 0.50
## 31  0.70 0.65 0.70 0.80 0.65
## 32  0.75 0.85 0.75 0.55 0.85
## 33  0.75 0.80 0.85 0.65 0.75
## 34  0.70 0.70 0.65 0.65 0.70
## 35  0.90 0.70 0.70 0.80 0.80
## 36  0.55 0.80 0.65 0.60 0.70
## 37  0.70 0.70 0.75 0.80 0.70
## 38  0.65 0.60 0.75 0.60 0.60
## 39  0.50 0.75 0.70 0.65 0.55
## 40  0.75 0.70 0.80 0.85 0.60
## 41  0.70 0.85 0.75 0.75 0.75
## 42  0.45 0.65 0.60 0.55 0.75
## 43  0.75 0.70 0.60 0.75 0.80
## 44  0.70 0.70 0.80 0.60 0.60
## 45  0.75 0.65 0.85 0.75 0.75
## 46  0.70 0.70 0.75 0.75 0.85
## 47  0.75 0.70 0.70 0.65 0.60
## 48  0.75 0.60 0.75 0.55 0.60
## 49  0.55 0.60 0.80 0.85 0.70
## 50  0.70 0.85 0.60 0.70 0.90
## 51  0.55 0.65 0.70 0.70 0.65
## 52  0.80 0.60 0.65 0.65 0.60
## 53  0.75 0.80 0.70 0.70 0.65
## 54  0.75 0.75 0.75 0.80 0.65
## 55  0.75 0.70 0.65 0.65 0.60
## 56  0.70 0.60 0.65 0.60 0.70
## 57  0.70 0.70 0.60 0.70 0.50
## 58  0.55 0.80 0.70 0.70 0.75
## 59  0.70 0.80 0.80 0.85 0.75
## 60  0.45 0.75 0.75 0.75 0.60
## 61  0.80 0.65 0.65 0.75 0.85
## 62  0.80 0.80 0.70 0.85 0.75
## 63  0.55 0.75 0.50 0.60 0.75
## 64  0.75 0.80 0.85 0.65 0.70
## 65  0.55 0.75 0.45 0.70 0.60
## 66  0.55 0.70 0.70 0.65 0.50
## 67  0.60 0.65 0.65 0.45 0.65
## 68  0.85 0.70 0.80 0.80 0.80
## 69  0.65 0.65 0.65 0.65 0.50
## 70  0.70 0.65 0.70 0.55 0.70
## 71  0.55 0.75 0.65 0.65 0.60
## 72  0.55 0.65 0.65 0.70 0.65
## 73  0.90 0.75 0.60 0.75 0.80
## 74  0.60 0.65 0.85 0.70 0.45
## 75  0.60 0.55 0.55 0.60 0.75
## 76  0.65 0.75 0.65 0.60 0.70
## 77  0.75 0.50 0.65 0.70 0.70
## 78  0.75 0.80 0.65 0.70 0.80
## 79  0.65 0.65 0.55 0.65 0.65
## 80  0.65 0.60 0.55 0.75 0.60
## 81  0.80 0.65 0.70 0.60 0.50
## 82  0.75 0.50 0.75 0.65 0.75
## 83  0.65 0.60 0.65 0.65 0.50
## 84  0.75 0.60 0.75 0.90 0.75
## 85  0.75 0.70 0.70 0.65 0.65
## 86  0.60 0.50 0.65 0.60 0.70
## 87  0.65 0.55 0.45 0.45 0.55
## 88  0.75 0.70 0.50 0.55 0.75
## 89  0.65 0.50 0.60 0.55 0.60
## 90  0.70 0.65 0.40 0.65 0.50
## 91  0.65 0.50 0.60 0.60 0.65
## 92  0.45 0.80 0.60 0.75 0.65
## 93  0.70 0.80 0.75 0.50 0.75
## 94  0.70 0.65 0.65 0.70 0.70
## 95  0.50 0.50 0.65 0.65 0.65
## 96  0.70 0.65 0.70 0.70 0.55
## 97  0.60 0.60 0.80 0.45 0.65
## 98  0.65 0.65 0.70 0.65 0.75
## 99  0.55 0.35 0.70 0.55 0.60
## 100 0.70 0.80 0.65 0.55 0.50
## 101 0.65 0.50 0.55 0.65 0.40
## 102 0.70 0.75 0.75 0.60 0.65
## 103 0.70 0.70 0.80 0.55 0.60
## 104 0.55 0.45 0.80 0.60 0.55
## 105 0.65 0.70 0.70 0.65 0.75
## 106 0.65 0.60 0.65 0.75 0.75
## 107 0.70 0.75 0.70 0.65 0.60
## 108 0.55 0.50 0.70 0.60 0.80
## 109 0.45 0.50 0.50 0.65 0.60
## 110 0.70 0.65 0.70 0.55 0.35
## 111 0.65 0.55 0.70 0.55 0.75
## 112 0.40 0.50 0.70 0.65 0.60
## 113 0.55 0.55 0.45 0.70 0.70
## 114 0.45 0.65 0.55 0.60 0.65
## 115 0.55 0.80 0.55 0.50 0.60
## 116 0.75 0.60 0.70 0.70 0.75
## 117 0.55 0.60 0.70 0.75 0.50
## 118 0.65 0.70 0.65 0.65 0.65
## 119 0.75 0.70 0.70 0.70 0.85
## 120 0.50 0.60 0.60 0.70 0.50
## 121 0.70 0.45 0.55 0.50 0.55
## 122 0.70 0.70 0.60 0.35 0.60
## 123 0.55 0.60 0.55 0.60 0.60
## 124 0.70 0.70 0.70 0.55 0.65
## 125 0.70 0.70 0.70 0.75 0.70
## 126 0.45 0.75 0.45 0.60 0.50
## 127 0.45 0.65 0.50 0.50 0.50
## 128 0.65 0.65 0.65 0.50 0.85
## 129 0.70 0.85 0.70 0.55 0.45
## 130 0.60 0.50 0.65 0.65 0.55
## 131 0.55 0.55 0.80 0.75 0.55
## 132 0.60 0.60 0.55 0.55 0.60
## 133 0.65 0.80 0.65 0.50 0.75
## 134 0.55 0.50 0.50 0.60 0.70
## 135 0.65 0.35 0.65 0.50 0.45
## 136 0.70 0.65 0.65 0.70 0.65
## 137 0.70 0.70 0.50 0.70 0.60
## 138 0.50 0.60 0.75 0.70 0.65
## 139 0.55 0.60 0.55 0.65 0.65
## 140 0.75 0.80 0.60 0.85 0.75
## 141 0.65 0.55 0.70 0.75 0.65
## 142 0.50 0.60 0.65 0.65 0.65
## 143 0.65 0.65 0.60 0.80 0.75
## 144 0.50 0.55 0.70 0.60 0.50
## 145 0.55 0.60 0.75 0.70 0.60
## 146 0.70 0.75 0.70 0.65 0.75
## 147 0.85 0.85 0.70 0.65 0.85
## 148 0.65 0.55 0.70 0.55 0.65
## 149 0.55 0.65 0.45 0.65 0.55
## 150 0.80 0.60 0.65 0.85 0.60
## 151 0.50 0.70 0.70 0.70 0.65
## 152 0.75 0.70 0.75 0.60 0.50
## 153 0.70 0.80 0.70 0.65 0.70
## 154 0.75 0.70 0.75 0.75 0.85
## 155 0.70 0.65 0.75 0.70 0.60
## 156 0.85 0.70 0.90 0.90 0.75
## 157 0.85 0.85 0.85 0.75 0.75
## 158 0.60 0.75 0.60 0.75 0.75
## 159 0.50 0.50 0.45 0.55 0.75
## 160 0.90 0.75 0.80 0.70 0.75
## 161 0.55 0.70 0.75 0.75 0.65
## 162 0.85 0.75 0.75 0.65 0.80
## 163 0.75 0.70 0.70 0.80 0.75
## 164 0.75 0.70 0.60 0.65 0.75
## 165 0.85 0.70 0.80 0.70 0.80
## 166 0.75 0.55 0.75 0.55 0.70
## 167 0.80 0.65 0.60 0.65 0.65
## 168 0.65 0.80 0.90 0.75 0.70
## 169 0.85 0.75 0.70 0.75 0.65
## 170 0.70 0.75 0.80 0.80 0.60
## 171 0.85 0.70 0.75 0.60 0.80
## 172 0.55 0.65 0.70 0.75 0.65
## 173 0.60 0.60 0.65 0.65 0.55
## 174 0.80 0.75 0.60 0.80 0.70
## 175 0.65 0.65 0.60 0.55 0.55
## 176 0.75 0.75 0.65 0.65 0.70
## 177 0.95 0.70 0.90 0.80 0.75
## 178 0.55 0.50 0.65 0.60 0.55
## 179 0.70 0.60 0.55 0.80 0.60
## 180 0.70 0.60 0.70 0.80 0.60
## 181 0.80 0.90 0.75 0.90 0.80
## 182 0.70 0.60 0.70 0.60 0.70
## 183 0.55 0.65 0.90 0.75 0.90
## 184 0.80 0.70 0.80 0.85 0.85
## 185 0.65 0.70 0.65 0.75 0.70
## 186 0.75 0.80 0.85 0.80 0.90
## 187 0.65 0.70 0.60 0.75 0.70
## 188 0.80 0.75 0.80 0.80 0.60
## 189 0.75 0.80 0.60 0.65 0.75
## 190 0.60 0.45 0.75 0.45 0.50
## 191 0.75 0.65 0.90 0.70 0.55
## 192 0.85 0.80 0.55 0.50 0.70
## 193 0.80 0.95 0.75 0.85 0.75
## 194 0.70 0.65 0.80 0.60 0.80
## 195 0.60 0.85 0.80 0.60 0.80
## 196 0.75 0.85 0.85 0.65 0.75
## 197 0.80 0.80 0.70 0.75 0.60
## 198 0.75 0.70 0.70 0.90 0.85
## 199 0.75 0.75 0.70 0.65 0.85
## 200 0.85 0.70 0.60 0.75 0.90
## 201 0.85 0.90 0.80 0.80 0.80
## 202 0.65 0.75 0.65 0.65 0.80
## 203 0.65 0.45 0.80 0.65 0.50
## 204 0.75 0.85 0.95 0.80 0.80
## 205 0.80 0.90 0.85 0.75 0.65
## 206 0.85 0.85 0.80 0.65 0.75
## 207 0.75 0.80 0.80 0.85 0.75
## 208 0.85 0.65 0.70 0.75 0.70
## 209 0.55 0.70 0.75 0.80 0.70
## 210 0.80 0.80 0.90 0.90 0.70
## 211 0.80 0.85 0.90 0.90 0.80
## 212 0.90 0.85 0.85 0.75 0.90
## 213 0.75 0.80 0.75 0.70 0.65
## 214 0.85 0.85 0.80 0.75 0.80
## 215 0.70 0.65 0.70 0.75 0.70
## 216 0.85 0.75 0.80 0.60 0.65
## 217 0.65 0.65 0.60 0.75 0.50
## 218 0.85 0.80 0.75 0.85 0.70
## 219 0.65 0.70 0.65 0.85 0.75
## 220 0.85 0.75 0.90 0.90 0.65
## 221 0.90 0.90 0.85 0.85 0.75
## 222 0.80 0.60 0.80 0.65 0.95
## 223 0.85 0.85 0.80 0.80 0.95
## 224 0.80 0.65 0.80 0.75 0.80
## 225 0.80 1.00 0.70 0.85 0.80
## 226 0.85 0.90 0.90 0.80 0.75
## 227 0.65 0.65 0.75 0.85 0.60
## 228 0.80 0.90 0.75 0.80 0.85
## 229 0.70 0.90 0.75 0.80 0.90
## 230 0.85 0.80 0.65 0.90 0.85
## 231 0.80 0.85 0.80 0.65 0.70
## 232 0.70 0.80 0.85 0.60 0.75
## 233 0.85 0.75 0.80 0.85 0.75
## 234 0.80 0.75 0.70 0.85 0.85
## 235 0.80 0.75 0.75 0.75 0.70
## 236 0.70 0.70 0.75 0.65 0.70
## 237 0.70 0.80 0.80 0.70 0.75
## 238 0.80 0.75 0.75 0.85 0.85
## 239 0.70 0.80 0.80 0.85 0.65
## 240 0.80 0.80 0.80 0.75 0.90
## 241 0.80 0.90 0.65 0.80 0.85
## 242 0.70 0.70 0.85 0.80 0.75
## 243 0.95 0.90 0.70 0.75 0.80
## 244 0.85 0.75 0.80 0.80 0.85
## 245 0.90 0.80 0.85 0.80 0.80
## 246 0.90 0.95 0.90 0.90 0.95
## 247 0.90 0.90 0.95 0.90 0.75
## 248 0.80 0.80 0.80 0.80 0.80
## 249 0.90 0.80 0.90 0.80 0.75
## 250 0.90 0.85 1.00 0.85 0.85
## 251 0.75 0.80 0.75 0.70 0.80
## 252 0.90 0.85 0.90 0.90 1.00
## 253 0.90 0.75 0.80 0.80 0.90
## 254 0.80 0.85 0.90 0.70 0.80
## 255 0.70 0.75 0.75 0.85 0.90
## 256 0.95 0.85 0.85 0.90 0.75
## 257 0.65 0.80 0.80 0.75 0.70
## 258 0.80 0.85 0.85 0.80 0.75
## 259 0.85 0.80 0.95 0.85 0.75
## 260 0.95 0.90 0.95 0.80 0.85
## 261 0.75 0.75 0.75 0.65 0.85
## 262 0.90 0.75 0.70 0.95 0.90
## 263 0.90 1.00 0.95 0.80 0.90
## 264 0.85 0.90 0.85 0.85 0.90
## 265 0.70 0.85 0.95 0.80 0.95
## 266 0.95 0.80 0.90 0.85 0.90
## 267 0.95 0.90 0.85 0.95 0.95
## 268 0.90 0.95 0.75 0.90 0.90
## 269 0.90 0.80 0.95 0.85 0.75
## 270 0.85 0.90 0.80 0.90 0.80
## 271 0.80 0.80 0.85 0.90 0.80
## 272 0.90 0.75 0.80 0.95 0.85
## 273 0.80 0.85 0.75 0.75 0.80
## 274 0.90 0.80 0.80 0.80 0.90
## 275 0.80 0.85 0.95 0.85 0.85
## 276 0.50 0.65 0.50 0.60 0.65
## 277 0.65 0.60 0.55 0.50 0.75
## 278 0.80 0.65 0.75 0.85 0.70
## 279 0.55 0.45 0.45 0.50 0.60
## 280 0.50 0.45 0.55 0.60 0.40
## 281 0.90 0.90 0.85 0.85 0.85
## 282 0.60 0.60 0.55 0.70 0.65
## 283 0.85 0.80 0.80 0.80 0.85
## 284 0.70 0.75 0.75 0.55 0.75
## 285 0.75 0.70 0.60 0.80 0.95
## 286 0.75 0.70 0.80 0.70 0.85
## 287 0.85 0.75 0.75 0.70 0.80
## 288 0.70 0.80 0.80 0.80 0.70
## 289 0.70 0.80 0.75 0.80 0.80
## 290 0.80 0.70 0.75 0.80 0.80
## 291 0.75 0.70 0.75 0.75 0.85
## 292 0.75 0.70 0.85 0.80 0.90
## 293 0.70 0.85 0.85 0.75 0.90
## 294 0.75 0.85 0.75 0.80 0.80
## 295 0.85 0.75 0.85 0.80 0.75
## 296 0.90 0.80 0.80 0.85 0.85
## 297 0.70 0.85 0.85 0.85 0.80
## 298 0.75 0.75 0.60 0.80 0.85
## 299 0.95 0.90 0.95 0.80 0.90
## 300 0.95 0.90 0.80 0.75 0.90
## 
## $sensitivity
##             1         2         3         4         5
## 1   0.2000000 0.3333333 0.2000000 0.5000000 0.3333333
## 2   0.2727273 0.3333333 0.4444444 0.3333333 0.4444444
## 3   0.3000000 0.5714286 0.5000000 0.4166667 0.4000000
## 4   0.3846154 0.4000000 0.4444444 0.5555556 0.4285714
## 5   0.1818182 0.1111111 0.3333333 0.1666667 0.1538462
## 6   1.0000000 0.6666667 0.7777778 0.6923077 0.7272727
## 7   0.4545455 0.1666667 0.3750000 0.4000000 0.3636364
## 8   0.4545455 0.4545455 0.3846154 0.5000000 0.4444444
## 9   0.8333333 0.7777778 0.6666667 0.6666667 0.7500000
## 10  0.4285714 0.3636364 0.3333333 0.4000000 0.6666667
## 11  0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 12  0.9000000 0.8333333 0.8181818 0.8461538 0.8333333
## 13  0.8000000 0.8181818 0.8181818 1.0000000 0.8000000
## 14  0.6363636 0.6363636 0.6250000 0.6000000 0.5000000
## 15  0.7142857 0.7500000 0.6428571 0.7857143 0.6666667
## 16  0.6153846 0.7777778 0.6666667 0.7142857 0.6153846
## 17  0.4444444 0.5000000 0.3000000 0.8333333 0.6250000
## 18  0.6000000 0.6000000 0.5454545 0.5454545 0.6000000
## 19  1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 20  0.4166667 0.4615385 0.5384615 0.4615385 0.5000000
## 21  0.5454545 0.5384615 0.4545455 0.5833333 0.7777778
## 22  0.4000000 0.4166667 0.4166667 0.4615385 0.5384615
## 23  0.3000000 0.3333333 0.3000000 0.3333333 0.3333333
## 24  0.5555556 0.6250000 0.5000000 0.5714286 0.7272727
## 25  0.3333333 0.5714286 0.4444444 0.3076923 0.3333333
## 26  0.5000000 0.7500000 0.2857143 0.6666667 0.6000000
## 27  0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 28  0.5000000 0.5555556 0.4444444 0.5000000 0.4444444
## 29  0.5714286 0.6000000 0.5000000 0.8333333 0.5714286
## 30  0.4545455 0.5454545 0.3636364 0.5454545 0.3333333
## 31  0.6666667 0.6153846 0.6666667 0.7142857 0.6363636
## 32  0.7500000 0.8333333 0.8750000 0.6250000 0.8333333
## 33  0.6250000 0.6666667 0.7500000 0.5000000 0.6250000
## 34  0.5384615 0.5384615 0.5000000 0.5000000 0.5454545
## 35  0.8333333 0.8333333 0.8333333 0.8750000 0.8750000
## 36  0.4615385 0.6666667 0.5555556 0.5000000 0.5833333
## 37  0.8888889 0.8888889 1.0000000 1.0000000 0.8181818
## 38  0.5000000 0.4615385 0.5833333 0.4545455 0.4545455
## 39  0.5000000 0.7272727 0.6666667 0.6666667 0.5555556
## 40  0.7777778 0.7000000 0.8750000 0.8181818 0.6250000
## 41  0.7777778 0.9000000 0.8750000 0.8750000 0.8000000
## 42  0.4285714 0.7142857 0.6666667 0.5555556 0.7272727
## 43  0.6363636 0.6250000 0.5000000 0.7142857 0.7500000
## 44  0.7500000 0.7500000 0.8750000 0.7500000 0.6250000
## 45  0.8571429 0.7142857 0.8888889 0.7777778 0.6923077
## 46  0.6000000 0.6000000 0.6666667 0.6666667 0.8571429
## 47  0.6000000 0.5000000 0.5000000 0.4285714 0.3750000
## 48  0.6363636 0.5000000 0.7142857 0.4285714 0.5000000
## 49  0.4285714 0.5000000 0.7500000 0.7777778 0.6250000
## 50  0.8750000 0.9090909 1.0000000 0.8000000 1.0000000
## 51  0.7500000 0.8333333 1.0000000 1.0000000 0.8333333
## 52  0.5714286 0.2857143 0.3333333 0.3750000 0.3636364
## 53  0.6666667 1.0000000 0.6666667 0.6363636 0.6000000
## 54  0.8888889 0.8888889 1.0000000 0.7857143 0.8571429
## 55  0.5000000 0.4285714 0.4000000 0.4000000 0.3636364
## 56  0.3333333 0.3333333 0.3333333 0.3333333 0.3750000
## 57  0.6363636 0.7142857 0.5454545 0.6666667 0.4444444
## 58  0.4444444 0.8333333 0.5833333 0.6000000 0.6666667
## 59  0.7272727 0.8888889 0.7692308 0.9000000 0.7500000
## 60  0.1428571 0.5555556 0.6000000 0.6666667 0.2500000
## 61  1.0000000 0.6363636 0.6666667 0.7777778 0.8181818
## 62  0.8571429 0.8571429 0.7142857 0.8750000 0.8333333
## 63  0.5000000 0.8333333 0.3333333 0.6000000 0.8333333
## 64  0.5555556 0.7500000 0.8000000 0.4444444 0.5000000
## 65  0.6666667 0.8571429 0.4285714 1.0000000 0.6666667
## 66  0.6666667 0.8571429 1.0000000 0.8333333 0.6666667
## 67  0.7142857 0.7000000 0.8333333 0.5000000 0.7500000
## 68  0.9000000 0.7272727 0.7692308 0.8888889 0.7692308
## 69  0.6666667 0.6666667 0.7500000 0.6666667 0.4285714
## 70  0.8750000 0.8571429 1.0000000 0.8000000 1.0000000
## 71  0.3636364 0.5714286 0.4444444 0.4545455 0.4000000
## 72  0.5555556 0.6666667 0.6666667 0.7000000 0.6666667
## 73  0.9090909 0.8750000 0.8000000 0.8000000 0.8888889
## 74  0.5555556 0.6000000 0.8750000 0.8000000 0.3333333
## 75  0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 76  0.8888889 0.9090909 1.0000000 1.0000000 0.9000000
## 77  0.8181818 0.6666667 0.7777778 0.8000000 0.8750000
## 78  0.8000000 0.8888889 0.7500000 0.7777778 0.8888889
## 79  0.1428571 0.0000000 0.1111111 0.2222222 0.0000000
## 80  0.5000000 0.4285714 0.3750000 0.6250000 0.3333333
## 81  0.9000000 1.0000000 0.8750000 0.7500000 0.7500000
## 82  0.6666667 0.3333333 1.0000000 0.5555556 0.7142857
## 83  1.0000000 0.7000000 0.8571429 1.0000000 0.6250000
## 84  0.2000000 0.0000000 0.0000000 0.5000000 0.0000000
## 85  0.3333333 0.2000000 0.2857143 0.2500000 0.1666667
## 86  1.0000000 0.5000000 0.8000000 0.7500000 1.0000000
## 87  0.7500000 0.6666667 0.5000000 0.5000000 0.6666667
## 88  0.8571429 0.8333333 0.5000000 0.6000000 0.8571429
## 89  0.5555556 0.3750000 0.5000000 0.4444444 0.5000000
## 90  0.6666667 0.6250000 0.2000000 1.0000000 0.3333333
## 91  0.7142857 0.5000000 0.6250000 0.6250000 0.7142857
## 92  0.5000000 0.8888889 0.8000000 0.8000000 0.6666667
## 93  1.0000000 1.0000000 0.7692308 0.6250000 0.8888889
## 94  0.5000000 0.4545455 0.4285714 0.5000000 0.5000000
## 95  0.6000000 0.6000000 0.7777778 0.7777778 0.7777778
## 96  0.8333333 0.8000000 0.8333333 1.0000000 0.6666667
## 97  0.4444444 0.4545455 0.7142857 0.2500000 0.5000000
## 98  0.6666667 0.6666667 0.7142857 0.6666667 0.8333333
## 99  0.7500000 0.3333333 1.0000000 0.7500000 0.8000000
## 100 1.0000000 0.7500000 0.5714286 0.3333333 0.3333333
## 101 0.5000000 0.3333333 0.3333333 0.5000000 0.0000000
## 102 0.7500000 0.7142857 0.8000000 0.5000000 0.6000000
## 103 0.6250000 0.7500000 0.8333333 0.4545455 0.5000000
## 104 0.7142857 0.6000000 1.0000000 0.7500000 0.7142857
## 105 0.5000000 0.5714286 0.6666667 0.5000000 1.0000000
## 106 0.5000000 0.4000000 0.5000000 1.0000000 0.7500000
## 107 0.5000000 0.6666667 0.5000000 0.4285714 0.3333333
## 108 0.6000000 0.5000000 0.8333333 0.7500000 0.8000000
## 109 0.5000000 0.6000000 0.5714286 0.8333333 0.8000000
## 110 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 111 0.6666667 0.4000000 0.6666667 0.4000000 0.7142857
## 112 0.3750000 0.5000000 0.7500000 0.8000000 0.7500000
## 113 0.3333333 0.3333333 0.2000000 1.0000000 0.6666667
## 114 0.1666667 0.2500000 0.2000000 0.1428571 0.2500000
## 115 0.4444444 0.7000000 0.3333333 0.4000000 0.5000000
## 116 0.1666667 0.1111111 0.1428571 0.1428571 0.1666667
## 117 0.6250000 0.7142857 0.8571429 1.0000000 0.6666667
## 118 0.8750000 1.0000000 0.8750000 1.0000000 0.8750000
## 119 0.3333333 0.0000000 0.3333333 0.2000000 0.5000000
## 120 0.0000000 0.0000000 0.1428571 0.0000000 0.1111111
## 121 0.5000000 0.1428571 0.3333333 0.2500000 0.2000000
## 122 0.8571429 0.8571429 0.6666667 0.2500000 0.6666667
## 123 0.5000000 0.6000000 0.5000000 0.6000000 0.6666667
## 124       NaN 0.5000000 0.5000000 0.2000000 0.3333333
## 125 0.0000000 0.1666667 0.0000000 0.2000000 0.0000000
## 126 0.2857143 0.6666667 0.2857143 0.5000000 0.2500000
## 127 0.5000000 0.8333333 0.6000000 0.6666667 1.0000000
## 128 0.7500000 0.8333333 0.7500000 0.5555556 0.9000000
## 129 0.8333333 1.0000000 1.0000000 1.0000000 0.0000000
## 130 0.5833333 0.5000000 0.7142857 0.6666667 0.5714286
## 131 0.5555556 0.6666667 0.8750000 0.8571429 0.6000000
## 132 0.7500000 0.6666667 0.5714286 0.6666667 0.7500000
## 133 0.2500000 0.6000000 0.3333333 0.1428571 0.5000000
## 134 0.2500000 0.2727273 0.2727273 0.3333333 0.4285714
## 135 0.8333333 0.2500000 0.8333333 0.6000000 0.5000000
## 136 0.7500000 0.6000000 0.6000000 1.0000000 0.5714286
## 137 0.8000000 0.7142857 0.4285714 0.7142857 0.6666667
## 138 0.3333333 0.4444444 1.0000000 0.5555556 0.5000000
## 139 0.0000000 0.0000000 0.0000000 0.2857143 0.2857143
## 140 0.3333333 0.4000000 0.0000000 0.5000000 0.3333333
## 141 0.6666667 0.3333333 0.6666667 1.0000000 0.6000000
## 142 0.2500000 0.5000000 0.6000000 0.6000000 0.6000000
## 143 0.2857143 0.3333333 0.2500000 0.5000000 0.4000000
## 144 0.5000000 1.0000000 1.0000000 1.0000000 0.5000000
## 145 0.2500000 0.2857143 0.5000000 0.4444444 0.2000000
## 146 0.3333333 0.5000000 0.3333333 0.2500000 0.5000000
## 147 0.6000000 0.5714286 0.0000000 0.2000000 0.6666667
## 148 0.6000000 0.4000000 0.7500000 0.3333333 0.6000000
## 149 0.5714286 1.0000000 0.3333333 0.8000000 0.6000000
## 150 0.2500000 0.1250000 0.1428571 0.3333333 0.1250000
## 151 0.2500000 0.5000000 0.5000000 0.5000000 0.4285714
## 152 0.4000000 0.2500000 0.4000000 0.3000000 0.2000000
## 153 0.7142857 0.8571429 1.0000000 1.0000000 1.0000000
## 154 0.2500000 0.2857143 0.3333333 0.2500000 0.5000000
## 155 0.3333333 0.2500000 0.5000000 0.4000000 0.2857143
## 156 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 157 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 158 0.6666667 0.8333333 0.5714286 1.0000000 0.7500000
## 159 0.3333333 0.4000000 0.3333333 0.5000000 0.8333333
## 160 0.6666667 0.2500000 0.4285714 0.0000000 0.2500000
## 161 0.1428571 0.3333333 0.4444444 0.4285714 0.0000000
## 162 1.0000000 0.5000000 0.5000000 0.3333333 0.5714286
## 163 0.5000000 0.4000000 0.0000000 0.6666667 0.5000000
## 164 0.3333333 0.3333333 0.1666667 0.2000000 0.4000000
## 165 0.6666667 0.2500000       NaN 0.0000000 0.5000000
## 166 0.2000000 0.0000000 0.2857143 0.1111111 0.0000000
## 167 0.7142857 0.5000000 0.4000000 0.5000000 0.5000000
## 168 0.2222222 0.2500000 0.5000000 0.0000000 0.0000000
## 169 0.0000000 0.0000000 0.1428571 0.0000000 0.0000000
## 170 1.0000000 0.7500000 0.8000000 1.0000000 0.0000000
## 171 0.6666667 0.3333333 0.4285714 0.0000000 0.5000000
## 172 0.2500000 0.3333333 0.4000000 0.5000000 0.3750000
## 173 0.6666667 0.6666667 0.7142857 0.8000000 0.6000000
## 174 0.3333333 0.0000000 0.0000000 0.0000000 0.0000000
## 175 0.6666667 0.6666667 0.5000000 0.4000000 0.4000000
## 176 0.6000000 0.5714286 0.4000000 0.4000000 0.5000000
## 177 0.7500000 0.2000000 0.6000000 0.4000000 0.2500000
## 178 0.5000000 0.4000000 0.7500000 0.5714286 0.5000000
## 179 0.6666667 0.5000000 0.0000000 1.0000000 0.5000000
## 180 0.2000000 0.1428571 0.2000000 0.4000000 0.1428571
## 181 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 182 0.5000000 0.2500000 0.5000000 0.0000000 0.5000000
## 183 0.2500000 0.2500000 1.0000000 0.5000000 1.0000000
## 184 1.0000000 0.4000000 0.6666667 0.7500000 0.7500000
## 185 0.3750000 0.4285714 0.2500000 0.5000000 0.4000000
## 186 0.0000000 0.2000000 0.0000000 0.2000000 0.3333333
## 187 0.3333333 0.4000000 0.3333333 0.5000000 0.3333333
## 188 0.3333333 0.2500000 0.3333333 0.4000000 0.0000000
## 189 0.5000000 1.0000000 0.0000000 0.2500000 0.5000000
## 190 0.5000000 0.2000000 0.7142857 0.3333333 0.3333333
## 191 0.5000000 0.0000000 1.0000000 0.3333333 0.1666667
## 192 0.5000000 0.3333333 0.0000000 0.0000000 0.0000000
## 193 0.2000000       NaN 0.1666667 0.0000000 0.1666667
## 194 0.5000000 0.4000000 0.7500000 0.2500000 0.7500000
## 195 0.4285714 1.0000000 0.8000000 0.4285714 0.8000000
## 196 0.1666667 0.2500000 0.0000000 0.0000000 0.0000000
## 197 1.0000000 0.8333333 0.7500000 0.7142857 0.5000000
## 198 0.0000000 0.0000000 0.1666667 0.5000000 0.3333333
## 199 0.2000000 0.0000000 0.0000000 0.0000000 0.3333333
## 200 0.3333333 0.0000000 0.1250000 0.2857143 0.5000000
## 201 0.4000000 0.5000000 0.3333333 0.2500000 0.0000000
## 202 0.6000000 1.0000000 0.5714286 0.6666667 0.8333333
## 203 0.2000000 0.0000000 0.5000000 0.2000000 0.0000000
## 204 0.3333333 0.6000000 1.0000000 0.5000000 0.5000000
## 205 0.4000000 1.0000000 0.5000000 0.0000000 0.2500000
## 206 0.0000000 0.3333333 0.2500000 0.0000000 0.0000000
## 207 1.0000000 1.0000000 0.6666667 1.0000000 1.0000000
## 208 1.0000000 0.3333333 0.3333333       NaN 0.3333333
## 209 0.2000000 0.5000000 0.6666667 0.7500000 0.5000000
## 210 0.2000000 0.2000000 0.3333333 0.3333333 0.0000000
## 211 0.0000000 0.3333333 0.5000000 0.5000000 0.0000000
## 212 0.7500000 0.6000000 0.6666667 0.3333333 0.7500000
## 213 0.5000000 1.0000000 0.5000000 0.3333333 0.0000000
## 214 0.5000000 0.5000000 0.4000000 0.3333333 0.4285714
## 215 0.4000000 0.3333333 0.3333333       NaN 0.3333333
## 216 0.6666667 0.3333333 0.5000000 0.0000000 0.2000000
## 217 0.0000000 0.2857143 0.1666667 0.3333333 0.0000000
## 218 0.7500000 0.6666667 0.5000000 0.7500000 0.3333333
## 219 0.2500000 0.4000000 0.3333333 0.7500000 0.5000000
## 220 0.6666667 0.5000000 0.8000000 0.8000000 0.3333333
## 221 0.6666667 0.6000000 0.5000000 0.5000000 0.2500000
## 222 0.0000000 0.1428571 0.4000000 0.2500000 1.0000000
## 223 0.0000000 0.2500000 0.0000000 0.0000000       NaN
## 224 0.5000000 0.2000000 0.5000000 0.4000000 0.5000000
## 225 0.5000000 1.0000000 0.2500000 1.0000000 0.5000000
## 226 1.0000000 1.0000000 0.8333333 0.7500000 0.6666667
## 227 0.4285714 0.3333333 0.5714286 1.0000000 0.2500000
## 228 0.0000000 0.0000000 0.0000000 0.2000000 0.2500000
## 229 0.0000000 0.3333333 0.0000000 0.2000000 0.3333333
## 230 0.6666667 0.5000000 0.2857143 1.0000000 0.6000000
## 231 0.6666667 0.7500000 1.0000000 0.2500000 0.0000000
## 232 0.3333333 1.0000000 0.6666667 0.2857143 0.5000000
## 233 0.6666667 0.3333333 0.5000000 0.6666667 0.3333333
## 234 0.0000000 0.0000000 0.2000000 0.5000000 0.5000000
## 235 0.0000000 0.1666667 0.0000000 0.0000000 0.0000000
## 236 0.2857143 0.2000000 0.2500000 0.1666667 0.2857143
## 237 0.0000000 0.3333333 0.2500000 0.0000000 0.0000000
## 238 0.0000000 0.1666667 0.1666667 0.2500000 0.2500000
## 239 0.2000000 0.3333333 0.3333333 0.5000000 0.1666667
## 240 0.2500000 0.2500000 0.3333333 0.0000000 0.5000000
## 241 0.0000000 0.0000000 0.1250000 0.0000000 0.0000000
## 242 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 243 1.0000000 1.0000000 0.0000000 0.0000000 0.3333333
## 244 0.5000000 0.2500000 0.0000000 0.4000000 0.5000000
## 245 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 246 0.0000000 0.5000000 0.0000000 0.3333333 0.5000000
## 247 0.3333333 0.0000000 0.5000000 0.3333333 0.0000000
## 248 0.3333333 0.4000000 0.4000000 0.4000000 0.3333333
## 249 0.7500000 0.5000000 1.0000000 0.5000000 0.3333333
## 250 0.6000000 0.5000000 1.0000000 0.5000000 0.5000000
## 251 0.0000000 0.3333333 0.2500000 0.0000000 0.3333333
## 252 0.0000000 0.0000000 0.0000000 0.0000000       NaN
## 253 0.0000000 0.0000000 0.0000000 0.0000000 0.3333333
## 254 0.2000000 0.2500000 0.3333333 0.0000000 0.2000000
## 255 0.0000000 0.2857143 0.2857143 0.3333333 0.5000000
## 256 1.0000000 0.4000000 0.3333333 0.5000000 0.2000000
## 257 0.0000000 0.5000000       NaN 0.3333333 0.2500000
## 258 0.2500000 0.3333333 0.3333333 0.2500000 0.2000000
## 259 0.4000000 0.3333333 0.6666667 0.3333333 0.0000000
## 260 0.6666667       NaN 0.6666667 0.0000000 0.3333333
## 261 0.0000000 0.2000000 0.0000000 0.0000000 0.3333333
## 262 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 263 0.0000000       NaN 0.0000000 0.0000000 0.0000000
## 264 0.2500000 0.3333333 0.2500000 0.2500000 0.3333333
## 265 0.3333333 0.5000000 1.0000000 0.4000000 0.7500000
## 266 0.5000000 0.2000000 0.3333333 0.2500000 0.3333333
## 267 0.6666667 0.5000000 0.3333333 0.6666667 1.0000000
## 268 0.5000000 0.6666667 0.2000000 0.5000000 0.5000000
## 269 0.3333333 0.2000000 0.5000000 0.2500000 0.1666667
## 270 0.2500000 0.3333333 0.2000000 0.3333333 0.2000000
## 271 0.2000000 0.2000000 0.2500000 0.3333333 0.2000000
## 272 0.0000000 0.0000000 0.2000000 0.5000000 0.0000000
## 273 0.2500000 0.3333333 0.0000000 0.2857143 0.0000000
## 274 0.3333333 0.2000000 0.2000000 0.2000000 0.3333333
## 275 0.2500000 0.3333333 0.6666667 0.4000000 0.3333333
## 276 0.2500000 0.4444444 0.3333333 0.3750000 0.4545455
## 277 0.5714286 0.5000000 0.4285714 0.3750000 0.8000000
## 278 0.6363636 0.5000000 0.5833333 0.7000000 0.5555556
## 279 0.3333333 0.3000000 0.3333333 0.2000000 0.4444444
## 280 1.0000000 0.5000000 0.7500000 1.0000000 0.3333333
## 281 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 282 0.3333333 0.4000000 0.0000000 1.0000000       NaN
## 283 0.3333333 0.3333333 0.2500000 0.0000000 0.3333333
## 284 0.5000000 0.6000000 0.6666667 0.2000000 0.5555556
## 285 0.2500000 0.2000000 0.0000000 0.3333333 0.7500000
## 286 0.3333333 0.2000000 0.0000000 0.0000000 0.5000000
## 287 1.0000000 0.5000000 0.5000000 0.3333333 0.6666667
## 288 0.0000000 0.3333333 0.3333333 0.4000000 0.2000000
## 289 0.0000000 0.5000000 0.3333333 0.5000000 0.5000000
## 290 0.2500000 0.2500000 0.2000000 0.0000000 0.3333333
## 291 0.0000000 0.0000000 0.2500000 0.0000000 0.5000000
## 292 0.3333333 0.0000000 0.5000000 0.4000000 1.0000000
## 293 0.3333333 1.0000000 0.6666667 0.4000000 1.0000000
## 294 0.3333333 1.0000000 0.0000000 0.5000000 0.5000000
## 295 1.0000000 0.0000000 1.0000000 0.5000000 0.0000000
## 296 0.6666667 0.3333333 0.0000000 0.5000000 0.5000000
## 297 0.0000000 0.5000000 0.5000000 0.5000000 0.3333333
## 298 0.7272727 0.7272727 0.6000000 0.8750000 0.8181818
## 299 0.6666667 0.5000000 0.6666667 0.2500000 0.5000000
## 300 1.0000000 0.5000000 0.0000000 0.0000000 0.5000000
## 
## $specificity
##             1         2         3         4         5
## 1   0.8666667 0.8823529 0.8666667 0.8888889 0.9285714
## 2   0.8888889 0.9090909 1.0000000 1.0000000 1.0000000
## 3   0.8000000 0.9230769 1.0000000 1.0000000 0.9000000
## 4   1.0000000 0.9000000 0.9090909 1.0000000 0.8461538
## 5   1.0000000 0.9090909 1.0000000 1.0000000 1.0000000
## 6   0.7692308 0.7500000 0.7272727 0.8571429 0.7777778
## 7   1.0000000 0.7142857 0.8333333 0.9000000 0.8888889
## 8   1.0000000 1.0000000 1.0000000 1.0000000 0.9090909
## 9   0.6428571 0.7272727 0.5714286 0.5714286 0.6666667
## 10  0.8461538 0.8888889 0.8181818 0.9000000 0.9285714
## 11  1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 12  0.7000000 0.7500000 0.6666667 0.8571429 0.7500000
## 13  0.8000000 0.8888889 0.8888889 0.8333333 0.8000000
## 14  1.0000000 1.0000000 0.8333333 0.9000000 0.7500000
## 15  0.8333333 0.7500000 0.6666667 1.0000000 0.5454545
## 16  0.7142857 0.7272727 0.7500000 1.0000000 0.7142857
## 17  0.8181818 1.0000000 0.7000000 0.9285714 0.9166667
## 18  0.9000000 0.9000000 0.8888889 0.8888889 0.9000000
## 19  0.5000000 0.5555556 0.6250000 0.5555556 0.7142857
## 20  0.7500000 0.8571429 1.0000000 0.8571429 0.8750000
## 21  0.7777778 0.8571429 0.6666667 0.8750000 0.9090909
## 22  0.7000000 0.7500000 0.7500000 0.8571429 1.0000000
## 23  0.9000000 0.9090909 0.9000000 0.9090909 1.0000000
## 24  0.5454545 0.5833333 0.5000000 0.5384615 0.7777778
## 25  0.9090909 1.0000000 1.0000000 1.0000000 0.8571429
## 26  0.7857143 0.8125000 0.6923077 0.8571429 0.8000000
## 27  1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 28  0.8750000 0.8181818 0.7272727 0.7142857 0.7272727
## 29  0.6923077 0.6666667 0.6428571 0.7857143 0.6923077
## 30  0.8888889 1.0000000 0.7777778 1.0000000 0.7500000
## 31  0.7500000 0.7142857 0.7500000 1.0000000 0.6666667
## 32  0.7500000 0.8750000 0.6666667 0.5000000 0.8750000
## 33  0.8333333 0.9090909 0.9166667 0.7500000 0.8333333
## 34  1.0000000 1.0000000 0.8750000 0.7500000 0.8888889
## 35  1.0000000 0.6428571 0.6428571 0.7500000 0.7500000
## 36  0.7142857 1.0000000 0.7272727 0.7000000 0.8750000
## 37  0.5454545 0.5454545 0.5833333 0.6363636 0.5555556
## 38  0.8000000 0.8571429 1.0000000 0.7777778 0.7777778
## 39  0.5000000 0.7777778 0.7500000 0.6363636 0.5454545
## 40  0.7272727 0.7000000 0.7500000 0.8888889 0.5833333
## 41  0.6363636 0.8000000 0.6666667 0.6666667 0.7000000
## 42  0.4615385 0.6153846 0.5714286 0.5454545 0.7777778
## 43  0.8888889 0.7500000 0.6666667 0.7692308 0.8333333
## 44  0.6666667 0.6666667 0.7500000 0.5625000 0.5833333
## 45  0.6923077 0.6153846 0.8181818 0.7272727 0.8571429
## 46  0.8000000 0.8000000 0.8181818 0.8181818 0.8461538
## 47  0.8000000 0.8333333 0.9000000 0.7692308 0.7500000
## 48  0.8888889 0.6666667 0.7692308 0.6153846 0.7000000
## 49  0.6153846 0.7000000 0.8333333 0.9090909 0.7500000
## 50  0.5833333 0.7777778 0.5000000 0.6000000 0.8000000
## 51  0.5000000 0.5714286 0.6000000 0.6000000 0.5714286
## 52  0.9230769 0.7692308 0.7857143 0.8333333 0.8888889
## 53  0.8750000 0.7333333 0.7272727 0.7777778 0.7000000
## 54  0.6363636 0.6363636 0.6153846 0.8333333 0.5384615
## 55  0.9166667 0.8461538 0.9000000 0.9000000 0.8888889
## 56  0.8571429 1.0000000 0.9090909 1.0000000 0.9166667
## 57  0.7777778 0.6923077 0.6666667 0.7272727 0.5454545
## 58  0.6363636 0.7857143 0.8750000 0.8000000 0.8181818
## 59  0.6666667 0.7272727 0.8571429 0.8000000 0.7500000
## 60  0.6153846 0.9090909 0.8000000 0.7647059 0.6875000
## 61  0.7142857 0.6666667 0.6363636 0.7272727 0.8888889
## 62  0.7692308 0.7692308 0.6923077 0.8333333 0.7142857
## 63  0.5714286 0.7142857 0.5294118 0.6000000 0.7142857
## 64  0.9090909 0.8125000 0.8666667 0.8181818 0.8333333
## 65  0.5294118 0.6923077 0.4615385 0.6250000 0.5714286
## 66  0.5000000 0.6153846 0.6000000 0.5714286 0.4705882
## 67  0.5384615 0.6000000 0.5714286 0.4166667 0.5833333
## 68  0.8000000 0.6666667 0.8571429 0.7272727 0.8571429
## 69  0.6428571 0.6428571 0.6250000 0.6428571 0.5384615
## 70  0.5833333 0.5384615 0.5714286 0.4666667 0.5714286
## 71  0.7777778 0.8461538 0.8181818 0.8888889 0.8000000
## 72  0.5454545 0.6363636 0.6363636 0.7000000 0.6363636
## 73  0.8888889 0.6666667 0.5333333 0.7000000 0.7272727
## 74  0.6363636 0.7000000 0.8333333 0.6666667 0.5000000
## 75  1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 76  0.4545455 0.5555556 0.4615385 0.4285714 0.5000000
## 77  0.6666667 0.4285714 0.5454545 0.6000000 0.5833333
## 78  0.7000000 0.7272727 0.5833333 0.6363636 0.7272727
## 79  0.9230769 0.8666667 0.9090909 1.0000000 0.8666667
## 80  0.6875000 0.6923077 0.6666667 0.8333333 0.6470588
## 81  0.7000000 0.5333333 0.5833333 0.5000000 0.4375000
## 82  0.8181818 0.5714286 0.7058824 0.7272727 0.7692308
## 83  0.5333333 0.5000000 0.5384615 0.5333333 0.4166667
## 84  0.9333333 0.8571429 0.8823529 0.9444444 0.8823529
## 85  0.9285714 0.8666667 0.9230769 0.9166667 0.8571429
## 86  0.5555556 0.5000000 0.6000000 0.5625000 0.6250000
## 87  0.5833333 0.5000000 0.4285714 0.4285714 0.5000000
## 88  0.6923077 0.6428571 0.5000000 0.5333333 0.6923077
## 89  0.7272727 0.5833333 0.6428571 0.6363636 0.6428571
## 90  0.7272727 0.6666667 0.4666667 0.6111111 0.5294118
## 91  0.6153846 0.5000000 0.5833333 0.5833333 0.6153846
## 92  0.4166667 0.7272727 0.5333333 0.7000000 0.6250000
## 93  0.5714286 0.6666667 0.7142857 0.4166667 0.6363636
## 94  1.0000000 0.8888889 0.7692308 0.8333333 0.9000000
## 95  0.4000000 0.4000000 0.5454545 0.5454545 0.5454545
## 96  0.6428571 0.6000000 0.6428571 0.6250000 0.5294118
## 97  0.7272727 0.7777778 0.8461538 0.5833333 0.8000000
## 98  0.6428571 0.6428571 0.6923077 0.6428571 0.7142857
## 99  0.5000000 0.3571429 0.6000000 0.5000000 0.5333333
## 100 0.6666667 0.8333333 0.6923077 0.5882353 0.5714286
## 101 0.6875000 0.6363636 0.6428571 0.7500000 0.5333333
## 102 0.6875000 0.7692308 0.7333333 0.6250000 0.6666667
## 103 0.7500000 0.6875000 0.7857143 0.6666667 0.6250000
## 104 0.4615385 0.4000000 0.6666667 0.5000000 0.4615385
## 105 0.6666667 0.7692308 0.7058824 0.7500000 0.7222222
## 106 0.6666667 0.6666667 0.6875000 0.7222222 0.7500000
## 107 0.7500000 0.7647059 0.7500000 0.7692308 0.7142857
## 108 0.5333333 0.5000000 0.6428571 0.5625000 0.8000000
## 109 0.4285714 0.4666667 0.4615385 0.5714286 0.5333333
## 110 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 111 0.6470588 0.6000000 0.7142857 0.6000000 0.7692308
## 112 0.4166667 0.5000000 0.6666667 0.6000000 0.5625000
## 113 0.5882353 0.5882353 0.5333333 0.6666667 0.7142857
## 114 0.8750000 0.9166667 0.9000000 0.8461538 0.9166667
## 115 0.6363636 0.9000000 0.5882353 0.6000000 0.6428571
## 116 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 117 0.5000000 0.5384615 0.6153846 0.6428571 0.4705882
## 118 0.5000000 0.5384615 0.5000000 0.5000000 0.5000000
## 119 0.9285714 0.8235294 1.0000000 0.8666667 1.0000000
## 120 0.7692308 0.8000000 0.8461538 0.8235294 0.8181818
## 121 0.9000000 0.6153846 0.7272727 0.6666667 0.6666667
## 122 0.6153846 0.6153846 0.5454545 0.3750000 0.5454545
## 123 0.5714286 0.6000000 0.5714286 0.6000000 0.5882353
## 124 0.7000000 0.7500000 0.7500000 0.6666667 0.7058824
## 125 0.8750000 0.9285714 0.8750000 0.9333333 0.8750000
## 126 0.5384615 0.8181818 0.5384615 0.6250000 0.5625000
## 127 0.4444444 0.5714286 0.4666667 0.4705882 0.4736842
## 128 0.5833333 0.5714286 0.5833333 0.4545455 0.8000000
## 129 0.6428571 0.7692308 0.6250000 0.5263158 0.4736842
## 130 0.6250000 0.5000000 0.6153846 0.6363636 0.5384615
## 131 0.5454545 0.5294118 0.7500000 0.6923077 0.5333333
## 132 0.5625000 0.5714286 0.5384615 0.5294118 0.5625000
## 133 0.7500000 0.8666667 0.7857143 0.6923077 0.8125000
## 134 0.7500000 0.7777778 0.7777778 0.8181818 0.8461538
## 135 0.5714286 0.3750000 0.5714286 0.4666667 0.4375000
## 136 0.6875000 0.6666667 0.6666667 0.6666667 0.6923077
## 137 0.6666667 0.6923077 0.5384615 0.6923077 0.5882353
## 138 0.6363636 0.7272727 0.7222222 0.8181818 0.6666667
## 139 0.7333333 0.7500000 0.7333333 0.8461538 0.8461538
## 140 0.9285714 0.9333333 0.8000000 1.0000000 0.9285714
## 141 0.6470588 0.5882353 0.7142857 0.7058824 0.6666667
## 142 0.5625000 0.6111111 0.6666667 0.6666667 0.6666667
## 143 0.8461538 0.9090909 0.8333333 0.8333333 0.8666667
## 144 0.5000000 0.5263158 0.6250000 0.5555556 0.5000000
## 145 0.7500000 0.7692308 0.8125000 0.9090909 0.7333333
## 146 0.7647059 0.8571429 0.7647059 0.7500000 0.8125000
## 147 0.9333333 1.0000000 0.7777778 0.8000000 0.8823529
## 148 0.6666667 0.6000000 0.6875000 0.5882353 0.6666667
## 149 0.5384615 0.5882353 0.4705882 0.6000000 0.5333333
## 150 0.9375000 0.9166667 0.9230769 0.9411765 0.9166667
## 151 0.6666667 0.7500000 0.7857143 0.7500000 0.7692308
## 152 0.8666667 0.8125000 0.8666667 0.9000000 0.8000000
## 153 0.6923077 0.7692308 0.6470588 0.6111111 0.6470588
## 154 0.8750000 0.9230769 0.9285714 0.8750000 1.0000000
## 155 0.7647059 0.7500000 0.8125000 0.8000000 0.7692308
## 156 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 157 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 158 0.5882353 0.7142857 0.6153846 0.6875000 0.7500000
## 159 0.5294118 0.5333333 0.5000000 0.5625000 0.7142857
## 160 0.9411765 0.8750000 1.0000000 0.8235294 0.8750000
## 161 0.7692308 0.8571429 1.0000000 0.9230769 0.7647059
## 162 0.8333333 0.8125000 0.8571429 0.7857143 0.9230769
## 163 0.7777778 0.8000000 0.7368421 0.8235294 0.8125000
## 164 0.8235294 0.8571429 0.7857143 0.8000000 0.8666667
## 165 0.8823529 0.8125000 0.8000000 0.7777778 0.8333333
## 166 0.9333333 0.8461538 1.0000000 0.9090909 0.8750000
## 167 0.8461538 0.7142857 0.6666667 0.7500000 0.7142857
## 168 1.0000000 0.9375000 1.0000000 0.8823529 0.8750000
## 169 0.9444444 0.9375000 1.0000000 0.9375000 0.9285714
## 170 0.6842105 0.7500000 0.8000000 0.7647059 0.6315789
## 171 0.8823529 0.8571429 0.9230769 0.7500000 0.9285714
## 172 0.7500000 0.7857143 0.8000000 0.8571429 0.8333333
## 173 0.5714286 0.5714286 0.6153846 0.6000000 0.5333333
## 174 1.0000000 0.8823529 0.8571429 0.8888889 0.8750000
## 175 0.6470588 0.6470588 0.6250000 0.6000000 0.6000000
## 176 0.8000000 0.8461538 0.7333333 0.7333333 0.7500000
## 177 1.0000000 0.8666667 1.0000000 0.9333333 0.8750000
## 178 0.5714286 0.5333333 0.6250000 0.6153846 0.5625000
## 179 0.7142857 0.6428571 0.5789474 0.7500000 0.6111111
## 180 0.8666667 0.8461538 0.8666667 0.9333333 0.8461538
## 181 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 182 0.7222222 0.6875000 0.7857143 0.6666667 0.7222222
## 183 0.7500000 0.7500000 0.8823529 0.7777778 0.8823529
## 184 0.7894737 0.8000000 0.8235294 0.8750000 0.8750000
## 185 0.8333333 0.8461538 0.7500000 0.7777778 0.8000000
## 186 0.9375000 1.0000000 0.9444444 1.0000000 1.0000000
## 187 0.7857143 0.8000000 0.8181818 0.8125000 0.7647059
## 188 0.8823529 0.8750000 0.8823529 0.9333333 0.8000000
## 189 0.7777778 0.7894737 0.7058824 0.7500000 0.8125000
## 190 0.6111111 0.5333333 0.7692308 0.5454545 0.5714286
## 191 0.7777778 0.7222222 0.8823529 0.7647059 0.7142857
## 192 0.8888889 0.8823529 0.7857143 0.7692308 0.8235294
## 193 1.0000000 0.9500000 1.0000000 0.9444444 1.0000000
## 194 0.7500000 0.7333333 0.8125000 0.6875000 0.8125000
## 195 0.6923077 0.8125000 0.8000000 0.6923077 0.8000000
## 196 1.0000000 1.0000000 0.9444444 0.9285714 0.9375000
## 197 0.7500000 0.7857143 0.6875000 0.7692308 0.6428571
## 198 0.8823529 0.8750000 0.9285714 0.9444444 0.9411765
## 199 0.9333333 0.8823529 0.8750000 0.8666667 0.9411765
## 200 0.9411765 0.8750000 0.9166667 1.0000000 1.0000000
## 201 1.0000000 0.9444444 1.0000000 0.9375000 0.8888889
## 202 0.6666667 0.7058824 0.6923077 0.6470588 0.7857143
## 203 0.8000000 0.6923077 0.8750000 0.8000000 0.7142857
## 204 0.8235294 0.9333333 0.9411765 0.9285714 0.8333333
## 205 0.9333333 0.8947368 0.8888889 0.8333333 0.9166667
## 206 0.8947368 0.9411765 0.9375000 0.8666667 0.8823529
## 207 0.7368421 0.7777778 0.8571429 0.8235294 0.7368421
## 208 0.8333333 0.7857143 0.7647059 0.7500000 0.7647059
## 209 0.6666667 0.7500000 0.7647059 0.8125000 0.7500000
## 210 1.0000000 1.0000000 1.0000000 1.0000000 0.9333333
## 211 0.8888889 0.9411765 1.0000000 1.0000000 0.8888889
## 212 0.9375000 0.9333333 0.8823529 0.8235294 0.9375000
## 213 0.7777778 0.7894737 0.7777778 0.7647059 0.7222222
## 214 1.0000000 0.9375000 0.9333333 0.9285714 1.0000000
## 215 0.8000000 0.7857143 0.7647059 0.7500000 0.7647059
## 216 0.8823529 0.8235294 0.8750000 0.7500000 0.8000000
## 217 0.7647059 0.8461538 0.7857143 0.8235294 0.7142857
## 218 0.8750000 0.8235294 0.8125000 0.8750000 0.7647059
## 219 0.7500000 0.8000000 0.7857143 0.8750000 0.7777778
## 220 0.9285714 0.7777778 0.9333333 0.9333333 0.7857143
## 221 0.9411765 1.0000000 0.9375000 0.9375000 0.8750000
## 222 0.8421053 0.8461538 0.9333333 0.9166667 0.9444444
## 223 0.9444444 1.0000000 0.9411765 0.9411765 0.9500000
## 224 0.8333333 0.8000000 0.8333333 0.8666667 0.8750000
## 225 0.8333333 1.0000000 0.8125000 0.8421053 0.8750000
## 226 0.8235294 0.8750000 0.9285714 0.8125000 0.7647059
## 227 0.7692308 0.7058824 0.8461538 0.8235294 0.6875000
## 228 0.9411765 0.9473684 0.9375000 1.0000000 1.0000000
## 229 0.9333333 1.0000000 0.9375000 1.0000000 1.0000000
## 230 0.8823529 0.8333333 0.8461538 0.8888889 0.9333333
## 231 0.8235294 0.8750000 0.7894737 0.7500000 0.7368421
## 232 0.7647059 0.7894737 0.9285714 0.7692308 0.7777778
## 233 0.8823529 0.8235294 0.8750000 0.8823529 0.8235294
## 234 0.8421053 0.8333333 0.8666667 0.8888889 1.0000000
## 235 0.9411765 1.0000000 0.9375000 0.9375000 0.9333333
## 236 0.9230769 0.8666667 0.8750000 0.8571429 0.9230769
## 237 0.8750000 1.0000000 0.9375000 0.8750000 0.8823529
## 238 0.9411765 1.0000000 1.0000000 1.0000000 1.0000000
## 239 0.8666667 0.8823529 0.8823529 0.8888889 0.8571429
## 240 0.9375000 0.9375000 1.0000000 0.8823529 0.9444444
## 241 0.9411765 0.9473684 1.0000000 0.9411765 0.9444444
## 242 0.9333333 0.9333333 0.9444444 0.9411765 0.9375000
## 243 0.9444444 0.8947368 0.8235294 0.8333333 0.8823529
## 244 0.9375000 0.8750000 0.8421053 0.9333333 0.8888889
## 245 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 246 0.9473684 1.0000000 0.9473684 1.0000000 1.0000000
## 247 1.0000000 0.9473684 1.0000000 1.0000000 0.9375000
## 248 0.8823529 0.9333333 0.9333333 0.9333333 0.8823529
## 249 0.9375000 0.8333333 0.8888889 0.8333333 0.8235294
## 250 1.0000000 1.0000000 1.0000000 0.9375000 0.8888889
## 251 0.8333333 0.8823529 0.8750000 0.8235294 0.8823529
## 252 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 253 0.9473684 0.9375000 0.9411765 0.9411765 1.0000000
## 254 1.0000000 1.0000000 1.0000000 0.9333333 1.0000000
## 255 0.8750000 1.0000000 1.0000000 0.9411765 1.0000000
## 256 0.9473684 1.0000000 0.9411765 1.0000000 0.9333333
## 257 0.7647059 0.9285714 0.8000000 0.8235294 0.8125000
## 258 0.9375000 0.9411765 0.9411765 0.9375000 0.9333333
## 259 1.0000000 1.0000000 1.0000000 0.9411765 0.8823529
## 260 1.0000000 0.9000000 1.0000000 0.8888889 0.9411765
## 261 0.8823529 0.9333333 0.8823529 0.8666667 0.9411765
## 262 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 263 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 264 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 265 1.0000000 0.8888889 0.9444444 0.9333333 1.0000000
## 266 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 267 1.0000000 1.0000000 0.9411765 1.0000000 0.9473684
## 268 1.0000000 1.0000000 0.9333333 1.0000000 1.0000000
## 269 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 270 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 271 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 272 0.9473684 0.9375000 1.0000000 1.0000000 0.9444444
## 273 0.9375000 0.9411765 0.8823529 1.0000000 0.8888889
## 274 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 275 0.9375000 0.9411765 1.0000000 1.0000000 0.9411765
## 276 0.6666667 0.8181818 0.7500000 0.7500000 0.8888889
## 277 0.6923077 0.6428571 0.6153846 0.5833333 0.7333333
## 278 1.0000000 1.0000000 1.0000000 1.0000000 0.8181818
## 279 0.6428571 0.6000000 0.6250000 0.6000000 0.7272727
## 280 0.4736842 0.4444444 0.5000000 0.5294118 0.4117647
## 281 0.9473684 0.9473684 0.9444444 0.9444444 0.9444444
## 282 0.6470588 0.6666667 0.6111111 0.6842105 0.6500000
## 283 0.9411765 1.0000000 0.9375000 0.8888889 0.9411765
## 284 0.8333333 0.8000000 0.7647059 0.6666667 0.9090909
## 285 0.8750000 0.8666667 0.8000000 0.8823529 1.0000000
## 286 0.9285714 0.8666667 0.8421053 0.8235294 0.9375000
## 287 0.8333333 0.7777778 0.8571429 0.7647059 0.8235294
## 288 0.8235294 0.8823529 0.8823529 0.9333333 0.8666667
## 289 0.7777778 0.8333333 0.8235294 0.8750000 0.8750000
## 290 0.9375000 1.0000000 0.9333333 0.8888889 1.0000000
## 291 0.8333333 0.8235294 0.8750000 0.8333333 0.9375000
## 292 0.9285714 0.8235294 0.8888889 0.9333333 0.8947368
## 293 0.8571429 0.8421053 0.8823529 0.8666667 0.8888889
## 294 0.8235294 0.8421053 0.7894737 0.8750000 0.8333333
## 295 0.8421053 0.7894737 0.8421053 0.8333333 0.7894737
## 296 0.9411765 0.8823529 0.8421053 0.9375000 0.9375000
## 297 0.8235294 0.8888889 0.9375000 0.9375000 0.8823529
## 298 0.7777778 0.7777778 0.6000000 0.7500000 0.8888889
## 299 1.0000000 0.9444444 1.0000000 0.9375000 0.9444444
## 300 0.9473684 0.9444444 0.8888889 0.8823529 0.9444444
## 
## $kappa
##               1           2           3           4           5
## 1    0.07692308  0.21568627  0.07692308  0.31818182  0.30555556
## 2    0.15094340  0.25531915  0.46808511  0.28571429  0.46808511
## 3    0.10000000  0.52941176  0.50000000  0.36363636  0.30000000
## 4    0.30434783  0.30000000  0.36842105  0.57894737  0.29411765
## 5    0.16666667  0.02173913  0.41176471  0.13793103  0.11290323
## 6    0.70000000  0.40000000  0.50000000  0.50000000  0.50000000
## 7    0.42857143 -0.12500000  0.22222222  0.30000000  0.23809524
## 8    0.42857143  0.42857143  0.30434783  0.50000000  0.36842105
## 9    0.40000000  0.50000000  0.20000000  0.20000000  0.40000000
## 10   0.29411765  0.23809524  0.15789474  0.30000000  0.62500000
## 11   0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 12   0.60000000  0.58333333  0.48979592  0.68085106  0.58333333
## 13   0.60000000  0.70000000  0.70000000  0.80000000  0.60000000
## 14   0.61165049  0.61165049  0.46808511  0.50000000  0.25531915
## 15   0.47916667  0.48979592  0.27083333  0.68750000  0.20792079
## 16   0.30000000  0.50000000  0.40000000  0.60000000  0.30000000
## 17   0.27083333  0.44444444  0.00000000  0.76190476  0.56521739
## 18   0.50000000  0.50000000  0.41747573  0.41747573  0.50000000
## 19   0.50000000  0.57894737  0.66666667  0.57894737  0.76470588
## 20   0.15094340  0.26605505  0.44954128  0.26605505  0.33962264
## 21   0.31372549  0.33962264  0.11764706  0.42307692  0.69387755
## 22   0.10000000  0.15094340  0.15094340  0.26605505  0.44954128
## 23   0.20000000  0.25531915  0.20000000  0.25531915  0.28571429
## 24   0.10000000  0.20000000  0.00000000  0.10000000  0.50000000
## 25   0.25531915  0.63414634  0.46808511  0.23728814  0.21052632
## 26   0.28571429  0.47368421 -0.02272727  0.52380952  0.37500000
## 27   0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 28   0.33962264  0.38144330  0.17525773  0.20454545  0.17525773
## 29   0.25531915  0.22222222  0.13043478  0.56521739  0.25531915
## 30   0.32692308  0.51923077  0.13461538  0.51923077  0.07407407
## 31   0.40000000  0.30000000  0.40000000  0.60000000  0.30000000
## 32   0.48979592  0.69387755  0.50980392  0.11764706  0.69387755
## 33   0.46808511  0.58762887  0.68085106  0.25531915  0.46808511
## 34   0.44954128  0.44954128  0.33962264  0.25531915  0.41747573
## 35   0.80000000  0.40000000  0.40000000  0.60000000  0.60000000
## 36   0.15094340  0.61538462  0.28571429  0.20000000  0.42307692
## 37   0.41747573  0.41747573  0.52830189  0.61165049  0.38144330
## 38   0.30000000  0.26605505  0.52830189  0.22330097  0.22330097
## 39   0.00000000  0.50000000  0.40000000  0.30000000  0.10000000
## 40   0.50000000  0.40000000  0.60000000  0.70000000  0.20000000
## 41   0.40594059  0.70000000  0.50980392  0.50980392  0.50000000
## 42  -0.10000000  0.30000000  0.20000000  0.10000000  0.50000000
## 43   0.50980392  0.37500000  0.16666667  0.46808511  0.58333333
## 44   0.40000000  0.40000000  0.60000000  0.20000000  0.20000000
## 45   0.50000000  0.30000000  0.70000000  0.50000000  0.50000000
## 46   0.40000000  0.40000000  0.48979592  0.48979592  0.68085106
## 47   0.37500000  0.34782609  0.40000000  0.20454545  0.13043478
## 48   0.50980392  0.16666667  0.46808511  0.04255319  0.20000000
## 49   0.04255319  0.20000000  0.58333333  0.69387755  0.37500000
## 50   0.42307692  0.69387755  0.28571429  0.40000000  0.80000000
## 51   0.15094340  0.32692308  0.42857143  0.42857143  0.32692308
## 52   0.52941176  0.05882353  0.12500000  0.22222222  0.23809524
## 53   0.50980392  0.57894737  0.39393939  0.40594059  0.30000000
## 54   0.50980392  0.50980392  0.52830189  0.56521739  0.33962264
## 55   0.44444444  0.29411765  0.30000000  0.30000000  0.23809524
## 56   0.21052632  0.28571429  0.25531915  0.28571429  0.31818182
## 57   0.40594059  0.38144330  0.20792079  0.39393939 -0.01010101
## 58   0.08163265  0.56521739  0.42307692  0.40000000  0.48979592
## 59   0.39393939  0.60396040  0.58762887  0.70000000  0.48979592
## 60  -0.25000000  0.47916667  0.37500000  0.30555556 -0.05263158
## 61   0.60000000  0.30000000  0.30000000  0.50000000  0.70000000
## 62   0.58762887  0.58762887  0.38144330  0.69387755  0.47916667
## 63   0.06250000  0.47916667 -0.07526882  0.15789474  0.47916667
## 64   0.47916667  0.47368421  0.62500000  0.27083333  0.34782609
## 65   0.10000000  0.50000000 -0.10000000  0.40000000  0.20000000
## 66   0.13461538  0.41747573  0.42857143  0.32692308  0.06542056
## 67   0.22330097  0.30000000  0.32692308 -0.07843137  0.31372549
## 68   0.70000000  0.39393939  0.58762887  0.60396040  0.58762887
## 69   0.27083333  0.27083333  0.25531915  0.27083333 -0.03092784
## 70   0.42307692  0.33962264  0.44444444  0.18181818  0.44444444
## 71   0.13461538  0.43181818  0.27083333  0.32692308  0.20000000
## 72   0.10000000  0.30000000  0.30000000  0.40000000  0.30000000
## 73   0.79797980  0.50980392  0.23809524  0.50000000  0.60396040
## 74   0.19191919  0.30000000  0.69387755  0.36842105 -0.14583333
## 75   0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 76   0.32692308  0.47916667  0.37500000  0.31034483  0.40000000
## 77   0.48979592  0.07407407  0.31372549  0.40000000  0.42307692
## 78   0.50000000  0.60396040  0.31372549  0.40594059  0.60396040
## 79   0.07894737 -0.16666667  0.02173913  0.23913043 -0.16666667
## 80   0.14634146  0.12087912  0.04255319  0.46808511 -0.01265823
## 81   0.60000000  0.36363636  0.42307692  0.23076923  0.10714286
## 82   0.48979592 -0.08695652  0.41860465  0.28571429  0.46808511
## 83   0.36363636  0.20000000  0.33962264  0.36363636  0.03846154
## 84   0.16666667 -0.17647059 -0.13636364  0.44444444 -0.13636364
## 85   0.30555556  0.07692308  0.24050633  0.18604651  0.02777778
## 86   0.20000000  0.00000000  0.30000000  0.20000000  0.40000000
## 87   0.31372549  0.13461538 -0.05769231 -0.05769231  0.13461538
## 88   0.50000000  0.40000000  0.00000000  0.10000000  0.50000000
## 89   0.28571429 -0.04166667  0.13043478  0.08163265  0.13043478
## 90   0.39393939  0.28571429 -0.26315789  0.23913043 -0.07526882
## 91   0.30000000  0.00000000  0.20000000  0.20000000  0.30000000
## 92  -0.07843137  0.60396040  0.23809524  0.50000000  0.28571429
## 93   0.44444444  0.61538462  0.46808511  0.03846154  0.50980392
## 94   0.44444444  0.32692308  0.20454545  0.34782609  0.40000000
## 95   0.00000000  0.00000000  0.31372549  0.31372549  0.31372549
## 96   0.40000000  0.30000000  0.40000000  0.40000000  0.10000000
## 97   0.17525773  0.22330097  0.56043956 -0.17021277  0.30000000
## 98   0.27083333  0.27083333  0.38144330  0.27083333  0.47916667
## 99   0.15094340 -0.25000000  0.42857143  0.15094340  0.23809524
## 100  0.28571429  0.58333333  0.25531915 -0.04651163 -0.08695652
## 101  0.14634146 -0.03092784 -0.02272727  0.25531915 -0.41176471
## 102  0.31818182  0.46808511  0.44444444  0.09090909  0.22222222
## 103  0.37500000  0.31818182  0.56521739  0.11764706  0.09090909
## 104  0.15094340  0.00000000  0.61538462  0.23076923  0.15094340
## 105  0.07894737  0.34065934  0.24050633  0.25531915  0.34210526
## 106  0.07894737  0.05882353  0.14634146  0.34210526  0.39024390
## 107  0.21052632  0.30555556  0.21052632  0.20454545  0.04761905
## 108  0.10000000  0.00000000  0.40000000  0.20000000  0.60000000
## 109 -0.05769231  0.04761905  0.02912621  0.32692308  0.23809524
## 110  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 111  0.18604651  0.00000000  0.34782609  0.00000000  0.46808511
## 112 -0.20000000  0.00000000  0.40000000  0.30000000  0.20000000
## 113 -0.04651163 -0.04651163 -0.22222222  0.28571429  0.34782609
## 114  0.03508772  0.18604651  0.10000000 -0.01265823  0.18604651
## 115  0.08163265  0.60000000 -0.04651163  0.00000000  0.13043478
## 116  0.21875000  0.12087912  0.17808219  0.17808219  0.21875000
## 117  0.11764706  0.22330097  0.41747573  0.51923077  0.06542056
## 118  0.33962264  0.44954128  0.33962264  0.37500000  0.33962264
## 119  0.30555556 -0.17647059  0.35483871  0.07692308  0.58333333
## 120 -0.26582278 -0.23076923 -0.01265823 -0.17647059 -0.07526882
## 121  0.40000000 -0.25000000  0.06250000 -0.08695652 -0.12500000
## 122  0.41747573  0.41747573  0.20792079 -0.22641509  0.20792079
## 123  0.06250000  0.15789474  0.06250000  0.15789474  0.13978495
## 124  0.00000000  0.21052632  0.21052632 -0.12500000  0.02777778
## 125 -0.15384615  0.11764706 -0.15384615  0.16666667 -0.15384615
## 126 -0.17021277  0.48979592 -0.17021277  0.09090909 -0.13636364
## 127 -0.01851852  0.32692308  0.04761905  0.06542056  0.08256881
## 128  0.31372549  0.32692308  0.31372549  0.00990099  0.70000000
## 129  0.40000000  0.70000000  0.40000000  0.10000000 -0.10000000
## 130  0.20000000  0.00000000  0.30000000  0.30000000  0.10000000
## 131  0.10000000  0.10000000  0.60000000  0.50000000  0.10000000
## 132  0.20000000  0.20000000  0.10000000  0.10000000  0.20000000
## 133  0.00000000  0.46666667  0.12500000 -0.17647059  0.28571429
## 134  0.00000000  0.04761905  0.04761905  0.15789474  0.29411765
## 135  0.32692308 -0.22641509  0.32692308  0.04761905 -0.03773585
## 136  0.31818182  0.22222222  0.22222222  0.28571429  0.25531915
## 137  0.36842105  0.38144330 -0.03092784  0.38144330  0.13978495
## 138 -0.03092784  0.17525773  0.34210526  0.38144330  0.07894737
## 139 -0.28571429 -0.25000000 -0.28571429  0.14634146  0.14634146
## 140  0.30555556  0.38461538 -0.23076923  0.58333333  0.30555556
## 141  0.18604651 -0.04651163  0.34782609  0.41860465  0.22222222
## 142 -0.13636364  0.04761905  0.22222222  0.22222222  0.22222222
## 143  0.14634146  0.25531915  0.09090909  0.23076923  0.28571429
## 144  0.00000000  0.10000000  0.40000000  0.20000000  0.00000000
## 145  0.00000000  0.05882353  0.28571429  0.36842105 -0.06666667
## 146  0.07692308  0.37500000  0.07692308  0.00000000  0.28571429
## 147  0.57142857  0.63414634 -0.15384615  0.00000000  0.48275862
## 148  0.22222222  0.00000000  0.31818182 -0.04651163  0.22222222
## 149  0.10000000  0.30000000 -0.10000000  0.30000000  0.10000000
## 150  0.23076923  0.04761905  0.07894737  0.31818182  0.04761905
## 151 -0.08695652  0.21052632  0.28571429  0.21052632  0.20454545
## 152  0.28571429  0.06250000  0.28571429  0.20000000  0.00000000
## 153  0.38144330  0.58762887  0.35483871  0.23913043  0.35483871
## 154  0.13793103  0.24050633  0.30555556  0.13793103  0.58333333
## 155  0.07692308  0.00000000  0.28571429  0.20000000  0.05882353
## 156  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 157  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 158  0.13978495  0.47916667  0.17525773  0.46808511  0.48979592
## 159 -0.07526882 -0.05263158 -0.14583333  0.04255319  0.47916667
## 160  0.60784314  0.13793103  0.49367089 -0.17647059  0.13793103
## 161 -0.09756098  0.21052632  0.46808511  0.39024390 -0.20689655
## 162  0.50000000  0.28571429  0.37500000  0.12500000  0.52941176
## 163  0.16666667  0.20000000 -0.09090909  0.38461538  0.28571429
## 164  0.13793103  0.21052632 -0.05263158  0.00000000  0.28571429
## 165  0.48275862  0.06250000  0.00000000 -0.15384615  0.23076923
## 166  0.16666667 -0.18421053  0.34210526  0.02173913 -0.15384615
## 167  0.56043956  0.20454545  0.05882353  0.25531915  0.20454545
## 168  0.23913043  0.23076923  0.61538462 -0.13636364 -0.15384615
## 169 -0.07142857 -0.08695652  0.17808219 -0.08695652 -0.09375000
## 170  0.17808219  0.39024390  0.52941176  0.49367089 -0.09589041
## 171  0.48275862  0.21052632  0.39024390 -0.25000000  0.47368421
## 172  0.00000000  0.12500000  0.20000000  0.37500000  0.22222222
## 173  0.20000000  0.20000000  0.30000000  0.30000000  0.10000000
## 174  0.41176471 -0.13636364 -0.17647059 -0.11111111 -0.15384615
## 175  0.18604651  0.18604651  0.09090909  0.00000000  0.00000000
## 176  0.37500000  0.43181818  0.12500000  0.12500000  0.21052632
## 177  0.82758621  0.07692308  0.69230769  0.38461538  0.13793103
## 178  0.06250000 -0.05263158  0.25531915  0.17525773  0.04255319
## 179  0.34782609  0.13043478 -0.09756098  0.54545455  0.04761905
## 180  0.07692308 -0.01265823  0.07692308  0.38461538 -0.01265823
## 181  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 182  0.11764706 -0.05263158  0.28571429 -0.17647059  0.11764706
## 183  0.00000000  0.00000000  0.69230769  0.16666667  0.69230769
## 184  0.27272727  0.20000000  0.38461538  0.57142857  0.57142857
## 185  0.22222222  0.29411765  0.00000000  0.16666667  0.20000000
## 186 -0.08695652  0.27272727 -0.07142857  0.27272727  0.45945946
## 187  0.12500000  0.20000000  0.15789474  0.28571429  0.07692308
## 188  0.21568627  0.13793103  0.21568627  0.38461538 -0.23076923
## 189  0.16666667  0.27272727 -0.23076923  0.00000000  0.28571429
## 190  0.04761905 -0.22222222  0.46808511 -0.12244898 -0.08695652
## 191  0.16666667 -0.16666667  0.69230769  0.07692308 -0.12500000
## 192  0.31818182  0.21568627 -0.25000000 -0.26582278 -0.17647059
## 193  0.27272727  0.00000000  0.21875000 -0.07142857  0.21875000
## 194  0.21052632  0.12500000  0.47368421 -0.05263158  0.47368421
## 195  0.12087912  0.63414634  0.52941176  0.12087912  0.52941176
## 196  0.21875000  0.34782609 -0.07142857 -0.09375000 -0.08695652
## 197  0.54545455  0.56521739  0.31818182  0.46808511  0.13043478
## 198 -0.13636364 -0.15384615  0.11764706  0.44444444  0.31818182
## 199  0.16666667 -0.13636364 -0.15384615 -0.16666667  0.31818182
## 200  0.31818182 -0.15384615  0.04761905  0.34210526  0.61538462
## 201  0.50000000  0.44444444  0.41176471  0.23076923 -0.11111111
## 202  0.22222222  0.41860465  0.25531915  0.18604651  0.56521739
## 203  0.00000000 -0.34146341  0.37500000  0.00000000 -0.31578947
## 204  0.13793103  0.57142857  0.82758621  0.47368421  0.23076923
## 205  0.38461538  0.45945946  0.31818182 -0.13636364  0.18604651
## 206 -0.07142857  0.31818182  0.23076923 -0.16666667 -0.13636364
## 207  0.21875000  0.41176471  0.52380952  0.58333333  0.21875000
## 208  0.50000000  0.12500000  0.07692308  0.00000000  0.07692308
## 209 -0.12500000  0.21052632  0.30555556  0.47368421  0.21052632
## 210  0.27272727  0.27272727  0.45945946  0.45945946 -0.09090909
## 211 -0.11111111  0.31818182  0.61538462  0.61538462 -0.11111111
## 212  0.68750000  0.57142857  0.48275862  0.13793103  0.68750000
## 213  0.16666667  0.27272727  0.16666667  0.07692308 -0.16666667
## 214  0.58333333  0.48275862  0.38461538  0.30555556  0.49367089
## 215  0.20000000  0.12500000  0.07692308  0.00000000  0.07692308
## 216  0.48275862  0.13793103  0.37500000 -0.25000000  0.00000000
## 217 -0.20689655  0.14634146 -0.05263158  0.13793103 -0.31578947
## 218  0.57142857  0.38461538  0.28571429  0.57142857  0.07692308
## 219  0.00000000  0.20000000  0.12500000  0.57142857  0.16666667
## 220  0.62500000  0.16666667  0.73333333  0.73333333  0.12500000
## 221  0.60784314  0.69230769  0.48275862  0.48275862  0.13793103
## 222 -0.08108108 -0.01265823  0.38461538  0.18604651  0.77272727
## 223 -0.07142857  0.34782609 -0.08108108 -0.08108108  0.00000000
## 224  0.23076923  0.00000000  0.23076923  0.28571429  0.37500000
## 225  0.23076923  1.00000000  0.06250000  0.34782609  0.37500000
## 226  0.58333333  0.73684211  0.76190476  0.47368421  0.30555556
## 227  0.20454545  0.02777778  0.43181818  0.58333333 -0.05263158
## 228 -0.08108108 -0.05263158 -0.08695652  0.27272727  0.34782609
## 229 -0.09090909  0.45945946 -0.08695652  0.27272727  0.45945946
## 230  0.48275862  0.23076923  0.14634146  0.61538462  0.57142857
## 231  0.38461538  0.57142857  0.27272727  0.00000000 -0.09090909
## 232  0.07692308  0.27272727  0.62500000  0.05882353  0.16666667
## 233  0.48275862  0.13793103  0.37500000  0.48275862  0.13793103
## 234 -0.08108108 -0.13636364  0.07692308  0.31818182  0.58333333
## 235 -0.08108108  0.21875000 -0.08695652 -0.08695652 -0.09090909
## 236  0.24050633  0.07692308  0.13793103  0.02777778  0.24050633
## 237 -0.15384615  0.41176471  0.23076923 -0.15384615 -0.13636364
## 238 -0.08108108  0.21875000  0.21875000  0.34782609  0.34782609
## 239  0.07692308  0.21568627  0.21568627  0.31818182  0.02777778
## 240  0.23076923  0.23076923  0.41176471 -0.13636364  0.44444444
## 241 -0.08108108 -0.05263158  0.14634146 -0.08108108 -0.07142857
## 242 -0.09090909 -0.09090909 -0.07142857 -0.08108108 -0.08695652
## 243  0.77272727  0.45945946 -0.17647059 -0.13636364  0.21568627
## 244  0.48275862  0.13793103 -0.08108108  0.38461538  0.31818182
## 245  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 246 -0.05263158  0.64285714 -0.05263158  0.45945946  0.64285714
## 247  0.45945946 -0.05263158  0.64285714  0.45945946 -0.08695652
## 248  0.21568627  0.38461538  0.38461538  0.38461538  0.21568627
## 249  0.68750000  0.23076923  0.61538462  0.23076923  0.13793103
## 250  0.69230769  0.58333333  1.00000000  0.48275862  0.31818182
## 251 -0.13636364  0.21568627  0.13793103 -0.17647059  0.21568627
## 252  0.00000000  0.00000000  0.00000000  0.00000000         NaN
## 253 -0.05263158 -0.08695652 -0.08108108 -0.08108108  0.45945946
## 254  0.27272727  0.34782609  0.45945946 -0.09090909  0.27272727
## 255 -0.15384615  0.34210526  0.34210526  0.31818182  0.61538462
## 256  0.64285714  0.50000000  0.31818182  0.61538462  0.16666667
## 257 -0.20689655  0.47368421  0.00000000  0.13793103  0.06250000
## 258  0.23076923  0.31818182  0.31818182  0.23076923  0.16666667
## 259  0.50000000  0.41176471  0.77272727  0.31818182 -0.13636364
## 260  0.77272727  0.00000000  0.77272727 -0.11111111  0.31818182
## 261 -0.13636364  0.16666667 -0.13636364 -0.16666667  0.31818182
## 262  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 263  0.00000000         NaN  0.00000000  0.00000000  0.00000000
## 264  0.34782609  0.45945946  0.34782609  0.34782609  0.45945946
## 265  0.35483871  0.31818182  0.77272727  0.38461538  0.82758621
## 266  0.64285714  0.27272727  0.45945946  0.34782609  0.45945946
## 267  0.77272727  0.61538462  0.31818182  0.77272727  0.64285714
## 268  0.61538462  0.77272727  0.16666667  0.61538462  0.61538462
## 269  0.45945946  0.27272727  0.64285714  0.34782609  0.21875000
## 270  0.34782609  0.45945946  0.27272727  0.45945946  0.27272727
## 271  0.27272727  0.27272727  0.34782609  0.45945946  0.27272727
## 272 -0.05263158 -0.08695652  0.27272727  0.64285714 -0.07142857
## 273  0.23076923  0.31818182 -0.13636364  0.34210526 -0.11111111
## 274  0.45945946  0.27272727  0.27272727  0.27272727  0.45945946
## 275  0.23076923  0.31818182  0.77272727  0.50000000  0.31818182
## 276 -0.08695652  0.27083333  0.07407407  0.13043478  0.32692308
## 277  0.25531915  0.13043478  0.04255319 -0.04166667  0.44444444
## 278  0.61165049  0.37500000  0.52830189  0.70000000  0.38144330
## 279 -0.02272727 -0.10000000 -0.03773585 -0.17647059  0.17525773
## 280  0.08256881 -0.01851852  0.15094340  0.25233645 -0.12149533
## 281 -0.05263158 -0.05263158 -0.07142857 -0.07142857 -0.07142857
## 282 -0.01265823  0.05882353 -0.18421053  0.17808219  0.00000000
## 283  0.31818182  0.41176471  0.23076923 -0.11111111  0.31818182
## 284  0.34782609  0.37500000  0.30555556 -0.12500000  0.47916667
## 285  0.13793103  0.07692308 -0.23076923  0.21568627  0.82758621
## 286  0.30555556  0.07692308 -0.08108108 -0.17647059  0.48275862
## 287  0.50000000  0.16666667  0.37500000  0.07692308  0.38461538
## 288 -0.17647059  0.21568627  0.21568627  0.38461538  0.07692308
## 289 -0.15384615  0.23076923  0.13793103  0.37500000  0.37500000
## 290  0.23076923  0.28571429  0.16666667 -0.11111111  0.41176471
## 291 -0.13636364 -0.17647059  0.13793103 -0.13636364  0.48275862
## 292  0.30555556 -0.17647059  0.31818182  0.38461538  0.45945946
## 293  0.21052632  0.34782609  0.48275862  0.28571429  0.61538462
## 294  0.13793103  0.34782609 -0.08695652  0.37500000  0.23076923
## 295  0.34782609 -0.08695652  0.34782609  0.23076923 -0.08695652
## 296  0.60784314  0.21568627 -0.08108108  0.48275862  0.48275862
## 297 -0.17647059  0.31818182  0.48275862  0.48275862  0.21568627
## 298  0.50000000  0.50000000  0.20000000  0.60000000  0.70000000
## 299  0.77272727  0.44444444  0.77272727  0.23076923  0.44444444
## 300  0.64285714  0.44444444 -0.11111111 -0.13636364  0.44444444
## 
## $TSS
##               1           2           3           4           5
## 1    0.06666667  0.21568627  0.06666667  0.38888889  0.26190476
## 2    0.16161616  0.24242424  0.44444444  0.33333333  0.44444444
## 3    0.10000000  0.49450549  0.50000000  0.41666667  0.30000000
## 4    0.38461538  0.30000000  0.35353535  0.55555556  0.27472527
## 5    0.18181818  0.02020202  0.33333333  0.16666667  0.15384615
## 6    0.76923077  0.41666667  0.50505051  0.54945055  0.50505051
## 7    0.45454545 -0.11904762  0.20833333  0.30000000  0.25252525
## 8    0.45454545  0.45454545  0.38461538  0.50000000  0.35353535
## 9    0.47619048  0.50505051  0.23809524  0.23809524  0.41666667
## 10   0.27472527  0.25252525  0.15151515  0.30000000  0.59523810
## 11   0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 12   0.60000000  0.58333333  0.48484848  0.70329670  0.58333333
## 13   0.60000000  0.70707071  0.70707071  0.83333333  0.60000000
## 14   0.63636364  0.63636364  0.45833333  0.50000000  0.25000000
## 15   0.54761905  0.50000000  0.30952381  0.78571429  0.21212121
## 16   0.32967033  0.50505051  0.41666667  0.71428571  0.32967033
## 17   0.26262626  0.50000000  0.00000000  0.76190476  0.54166667
## 18   0.50000000  0.50000000  0.43434343  0.43434343  0.50000000
## 19   0.50000000  0.55555556  0.62500000  0.55555556  0.71428571
## 20   0.16666667  0.31868132  0.53846154  0.31868132  0.37500000
## 21   0.32323232  0.39560440  0.12121212  0.45833333  0.68686869
## 22   0.10000000  0.16666667  0.16666667  0.31868132  0.53846154
## 23   0.20000000  0.24242424  0.20000000  0.24242424  0.33333333
## 24   0.10101010  0.20833333  0.00000000  0.10989011  0.50505051
## 25   0.24242424  0.57142857  0.44444444  0.30769231  0.19047619
## 26   0.28571429  0.56250000 -0.02197802  0.52380952  0.40000000
## 27   0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 28   0.37500000  0.37373737  0.17171717  0.21428571  0.17171717
## 29   0.26373626  0.26666667  0.14285714  0.61904762  0.26373626
## 30   0.34343434  0.54545455  0.14141414  0.54545455  0.08333333
## 31   0.41666667  0.32967033  0.41666667  0.71428571  0.30303030
## 32   0.50000000  0.70833333  0.54166667  0.12500000  0.70833333
## 33   0.45833333  0.57575758  0.66666667  0.25000000  0.45833333
## 34   0.53846154  0.53846154  0.37500000  0.25000000  0.43434343
## 35   0.83333333  0.47619048  0.47619048  0.62500000  0.62500000
## 36   0.17582418  0.66666667  0.28282828  0.20000000  0.45833333
## 37   0.43434343  0.43434343  0.58333333  0.63636364  0.37373737
## 38   0.30000000  0.31868132  0.58333333  0.23232323  0.23232323
## 39   0.00000000  0.50505051  0.41666667  0.30303030  0.10101010
## 40   0.50505051  0.40000000  0.62500000  0.70707071  0.20833333
## 41   0.41414141  0.70000000  0.54166667  0.54166667  0.50000000
## 42  -0.10989011  0.32967033  0.23809524  0.10101010  0.50505051
## 43   0.52525253  0.37500000  0.16666667  0.48351648  0.58333333
## 44   0.41666667  0.41666667  0.62500000  0.31250000  0.20833333
## 45   0.54945055  0.32967033  0.70707071  0.50505051  0.54945055
## 46   0.40000000  0.40000000  0.48484848  0.48484848  0.70329670
## 47   0.40000000  0.33333333  0.40000000  0.19780220  0.12500000
## 48   0.52525253  0.16666667  0.48351648  0.04395604  0.20000000
## 49   0.04395604  0.20000000  0.58333333  0.68686869  0.37500000
## 50   0.45833333  0.68686869  0.50000000  0.40000000  0.80000000
## 51   0.25000000  0.40476190  0.60000000  0.60000000  0.40476190
## 52   0.49450549  0.05494505  0.11904762  0.20833333  0.25252525
## 53   0.54166667  0.73333333  0.39393939  0.41414141  0.30000000
## 54   0.52525253  0.52525253  0.61538462  0.61904762  0.39560440
## 55   0.41666667  0.27472527  0.30000000  0.30000000  0.25252525
## 56   0.19047619  0.33333333  0.24242424  0.33333333  0.29166667
## 57   0.41414141  0.40659341  0.21212121  0.39393939 -0.01010101
## 58   0.08080808  0.61904762  0.45833333  0.40000000  0.48484848
## 59   0.39393939  0.61616162  0.62637363  0.70000000  0.50000000
## 60  -0.24175824  0.46464646  0.40000000  0.43137255 -0.06250000
## 61   0.71428571  0.30303030  0.30303030  0.50505051  0.70707071
## 62   0.62637363  0.62637363  0.40659341  0.70833333  0.54761905
## 63   0.07142857  0.54761905 -0.13725490  0.20000000  0.54761905
## 64   0.46464646  0.56250000  0.66666667  0.26262626  0.33333333
## 65   0.19607843  0.54945055 -0.10989011  0.62500000  0.23809524
## 66   0.16666667  0.47252747  0.60000000  0.40476190  0.13725490
## 67   0.25274725  0.30000000  0.40476190 -0.08333333  0.33333333
## 68   0.70000000  0.39393939  0.62637363  0.61616162  0.62637363
## 69   0.30952381  0.30952381  0.37500000  0.30952381 -0.03296703
## 70   0.45833333  0.39560440  0.57142857  0.26666667  0.57142857
## 71   0.14141414  0.41758242  0.26262626  0.34343434  0.20000000
## 72   0.10101010  0.30303030  0.30303030  0.40000000  0.30303030
## 73   0.79797980  0.54166667  0.33333333  0.50000000  0.61616162
## 74   0.19191919  0.30000000  0.70833333  0.46666667 -0.16666667
## 75   0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 76   0.34343434  0.46464646  0.46153846  0.42857143  0.40000000
## 77   0.48484848  0.09523810  0.32323232  0.40000000  0.45833333
## 78   0.50000000  0.61616162  0.33333333  0.41414141  0.61616162
## 79   0.06593407 -0.13333333  0.02020202  0.22222222 -0.13333333
## 80   0.18750000  0.12087912  0.04166667  0.45833333 -0.01960784
## 81   0.60000000  0.53333333  0.45833333  0.25000000  0.18750000
## 82   0.48484848 -0.09523810  0.70588235  0.28282828  0.48351648
## 83   0.53333333  0.20000000  0.39560440  0.53333333  0.04166667
## 84   0.13333333 -0.14285714 -0.11764706  0.44444444 -0.11764706
## 85   0.26190476  0.06666667  0.20879121  0.16666667  0.02380952
## 86   0.55555556  0.00000000  0.40000000  0.31250000  0.62500000
## 87   0.33333333  0.16666667 -0.07142857 -0.07142857  0.16666667
## 88   0.54945055  0.47619048  0.00000000  0.13333333  0.54945055
## 89   0.28282828 -0.04166667  0.14285714  0.08080808  0.14285714
## 90   0.39393939  0.29166667 -0.33333333  0.61111111 -0.13725490
## 91   0.32967033  0.00000000  0.20833333  0.20833333  0.32967033
## 92  -0.08333333  0.61616162  0.33333333  0.50000000  0.29166667
## 93   0.57142857  0.66666667  0.48351648  0.04166667  0.52525253
## 94   0.50000000  0.34343434  0.19780220  0.33333333  0.40000000
## 95   0.00000000  0.00000000  0.32323232  0.32323232  0.32323232
## 96   0.47619048  0.40000000  0.47619048  0.62500000  0.19607843
## 97   0.17171717  0.23232323  0.56043956 -0.16666667  0.30000000
## 98   0.30952381  0.30952381  0.40659341  0.30952381  0.54761905
## 99   0.25000000 -0.30952381  0.60000000  0.25000000  0.33333333
## 100  0.66666667  0.58333333  0.26373626 -0.07843137 -0.09523810
## 101  0.18750000 -0.03030303 -0.02380952  0.25000000 -0.46666667
## 102  0.43750000  0.48351648  0.53333333  0.12500000  0.26666667
## 103  0.37500000  0.43750000  0.61904762  0.12121212  0.12500000
## 104  0.17582418  0.00000000  0.66666667  0.25000000  0.17582418
## 105  0.16666667  0.34065934  0.37254902  0.25000000  0.72222222
## 106  0.16666667  0.06666667  0.18750000  0.72222222  0.50000000
## 107  0.25000000  0.43137255  0.25000000  0.19780220  0.04761905
## 108  0.13333333  0.00000000  0.47619048  0.31250000  0.60000000
## 109 -0.07142857  0.06666667  0.03296703  0.40476190  0.33333333
## 110  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 111  0.31372549  0.00000000  0.38095238  0.00000000  0.48351648
## 112 -0.20833333  0.00000000  0.41666667  0.40000000  0.31250000
## 113 -0.07843137 -0.07843137 -0.26666667  0.66666667  0.38095238
## 114  0.04166667  0.16666667  0.10000000 -0.01098901  0.16666667
## 115  0.08080808  0.60000000 -0.07843137  0.00000000  0.14285714
## 116  0.16666667  0.11111111  0.14285714  0.14285714  0.16666667
## 117  0.12500000  0.25274725  0.47252747  0.64285714  0.13725490
## 118  0.37500000  0.53846154  0.37500000  0.50000000  0.37500000
## 119  0.26190476 -0.17647059  0.33333333  0.06666667  0.50000000
## 120 -0.23076923 -0.20000000 -0.01098901 -0.17647059 -0.07070707
## 121  0.40000000 -0.24175824  0.06060606 -0.08333333 -0.13333333
## 122  0.47252747  0.47252747  0.21212121 -0.37500000  0.21212121
## 123  0.07142857  0.20000000  0.07142857  0.20000000  0.25490196
## 124         NaN  0.25000000  0.25000000 -0.13333333  0.03921569
## 125 -0.12500000  0.09523810 -0.12500000  0.13333333 -0.12500000
## 126 -0.17582418  0.48484848 -0.17582418  0.12500000 -0.18750000
## 127 -0.05555556  0.40476190  0.06666667  0.13725490  0.47368421
## 128  0.33333333  0.40476190  0.33333333  0.01010101  0.70000000
## 129  0.47619048  0.76923077  0.62500000  0.52631579 -0.52631579
## 130  0.20833333  0.00000000  0.32967033  0.30303030  0.10989011
## 131  0.10101010  0.19607843  0.62500000  0.54945055  0.13333333
## 132  0.31250000  0.23809524  0.10989011  0.19607843  0.31250000
## 133  0.00000000  0.46666667  0.11904762 -0.16483516  0.31250000
## 134  0.00000000  0.05050505  0.05050505  0.15151515  0.27472527
## 135  0.40476190 -0.37500000  0.40476190  0.06666667 -0.06250000
## 136  0.43750000  0.26666667  0.26666667  0.66666667  0.26373626
## 137  0.46666667  0.40659341 -0.03296703  0.40659341  0.25490196
## 138 -0.03030303  0.17171717  0.72222222  0.37373737  0.16666667
## 139 -0.26666667 -0.25000000 -0.26666667  0.13186813  0.13186813
## 140  0.26190476  0.33333333 -0.20000000  0.50000000  0.26190476
## 141  0.31372549 -0.07843137  0.38095238  0.70588235  0.26666667
## 142 -0.18750000  0.11111111  0.26666667  0.26666667  0.26666667
## 143  0.13186813  0.24242424  0.08333333  0.33333333  0.26666667
## 144  0.00000000  0.52631579  0.62500000  0.55555556  0.00000000
## 145  0.00000000  0.05494505  0.31250000  0.35353535 -0.06666667
## 146  0.09803922  0.35714286  0.09803922  0.00000000  0.31250000
## 147  0.53333333  0.57142857 -0.22222222  0.00000000  0.54901961
## 148  0.26666667  0.00000000  0.43750000 -0.07843137  0.26666667
## 149  0.10989011  0.58823529 -0.19607843  0.40000000  0.13333333
## 150  0.18750000  0.04166667  0.06593407  0.27450980  0.04166667
## 151 -0.08333333  0.25000000  0.28571429  0.25000000  0.19780220
## 152  0.26666667  0.06250000  0.26666667  0.20000000  0.00000000
## 153  0.40659341  0.62637363  0.64705882  0.61111111  0.64705882
## 154  0.12500000  0.20879121  0.26190476  0.12500000  0.50000000
## 155  0.09803922  0.00000000  0.31250000  0.20000000  0.05494505
## 156  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 157  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 158  0.25490196  0.54761905  0.18681319  0.68750000  0.50000000
## 159 -0.13725490 -0.06666667 -0.16666667  0.06250000  0.54761905
## 160  0.60784314  0.12500000  0.42857143 -0.17647059  0.12500000
## 161 -0.08791209  0.19047619  0.44444444  0.35164835 -0.23529412
## 162  0.83333333  0.31250000  0.35714286  0.11904762  0.49450549
## 163  0.27777778  0.20000000 -0.26315789  0.49019608  0.31250000
## 164  0.15686275  0.19047619 -0.04761905  0.00000000  0.26666667
## 165  0.54901961  0.06250000         NaN -0.22222222  0.33333333
## 166  0.13333333 -0.15384615  0.28571429  0.02020202 -0.12500000
## 167  0.56043956  0.21428571  0.06666667  0.25000000  0.21428571
## 168  0.22222222  0.18750000  0.50000000 -0.11764706 -0.12500000
## 169 -0.05555556 -0.06250000  0.14285714 -0.06250000 -0.07142857
## 170  0.68421053  0.50000000  0.60000000  0.76470588 -0.36842105
## 171  0.54901961  0.19047619  0.35164835 -0.25000000  0.42857143
## 172  0.00000000  0.11904762  0.20000000  0.35714286  0.20833333
## 173  0.23809524  0.23809524  0.32967033  0.40000000  0.13333333
## 174  0.33333333 -0.11764706 -0.14285714 -0.11111111 -0.12500000
## 175  0.31372549  0.31372549  0.12500000  0.00000000  0.00000000
## 176  0.40000000  0.41758242  0.13333333  0.13333333  0.25000000
## 177  0.75000000  0.06666667  0.60000000  0.33333333  0.12500000
## 178  0.07142857 -0.06666667  0.37500000  0.18681319  0.06250000
## 179  0.38095238  0.14285714 -0.42105263  0.75000000  0.11111111
## 180  0.06666667 -0.01098901  0.06666667  0.33333333 -0.01098901
## 181  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 182  0.22222222 -0.06250000  0.28571429 -0.33333333  0.22222222
## 183  0.00000000  0.00000000  0.88235294  0.27777778  0.88235294
## 184  0.78947368  0.20000000  0.49019608  0.62500000  0.62500000
## 185  0.20833333  0.27472527  0.00000000  0.27777778  0.20000000
## 186 -0.06250000  0.20000000 -0.05555556  0.20000000  0.33333333
## 187  0.11904762  0.20000000  0.15151515  0.31250000  0.09803922
## 188  0.21568627  0.12500000  0.21568627  0.33333333 -0.20000000
## 189  0.27777778  0.78947368 -0.29411765  0.00000000  0.31250000
## 190  0.11111111 -0.26666667  0.48351648 -0.12121212 -0.09523810
## 191  0.27777778 -0.27777778  0.88235294  0.09803922 -0.11904762
## 192  0.38888889  0.21568627 -0.21428571 -0.23076923 -0.17647059
## 193  0.20000000         NaN  0.16666667 -0.05555556  0.16666667
## 194  0.25000000  0.13333333  0.56250000 -0.06250000  0.56250000
## 195  0.12087912  0.81250000  0.60000000  0.12087912  0.60000000
## 196  0.16666667  0.25000000 -0.05555556 -0.07142857 -0.06250000
## 197  0.75000000  0.61904762  0.43750000  0.48351648  0.14285714
## 198 -0.11764706 -0.12500000  0.09523810  0.44444444  0.27450980
## 199  0.13333333 -0.11764706 -0.12500000 -0.13333333  0.27450980
## 200  0.27450980 -0.12500000  0.04166667  0.28571429  0.50000000
## 201  0.40000000  0.44444444  0.33333333  0.18750000 -0.11111111
## 202  0.26666667  0.70588235  0.26373626  0.31372549  0.61904762
## 203  0.00000000 -0.30769231  0.37500000  0.00000000 -0.28571429
## 204  0.15686275  0.53333333  0.94117647  0.42857143  0.33333333
## 205  0.33333333  0.89473684  0.38888889 -0.16666667  0.16666667
## 206 -0.10526316  0.27450980  0.18750000 -0.13333333 -0.11764706
## 207  0.73684211  0.77777778  0.52380952  0.82352941  0.73684211
## 208  0.83333333  0.11904762  0.09803922         NaN  0.09803922
## 209 -0.13333333  0.25000000  0.43137255  0.56250000  0.25000000
## 210  0.20000000  0.20000000  0.33333333  0.33333333 -0.06666667
## 211 -0.11111111  0.27450980  0.50000000  0.50000000 -0.11111111
## 212  0.68750000  0.53333333  0.54901961  0.15686275  0.68750000
## 213  0.27777778  0.78947368  0.27777778  0.09803922 -0.27777778
## 214  0.50000000  0.43750000  0.33333333  0.26190476  0.42857143
## 215  0.20000000  0.11904762  0.09803922         NaN  0.09803922
## 216  0.54901961  0.15686275  0.37500000 -0.25000000  0.00000000
## 217 -0.23529412  0.13186813 -0.04761905  0.15686275 -0.28571429
## 218  0.62500000  0.49019608  0.31250000  0.62500000  0.09803922
## 219  0.00000000  0.20000000  0.11904762  0.62500000  0.27777778
## 220  0.59523810  0.27777778  0.73333333  0.73333333  0.11904762
## 221  0.60784314  0.60000000  0.43750000  0.43750000  0.12500000
## 222 -0.15789474 -0.01098901  0.33333333  0.16666667  0.94444444
## 223 -0.05555556  0.25000000 -0.05882353 -0.05882353         NaN
## 224  0.33333333  0.00000000  0.33333333  0.26666667  0.37500000
## 225  0.33333333  1.00000000  0.06250000  0.84210526  0.37500000
## 226  0.82352941  0.87500000  0.76190476  0.56250000  0.43137255
## 227  0.19780220  0.03921569  0.41758242  0.82352941 -0.06250000
## 228 -0.05882353 -0.05263158 -0.06250000  0.20000000  0.25000000
## 229 -0.06666667  0.33333333 -0.06250000  0.20000000  0.33333333
## 230  0.54901961  0.33333333  0.13186813  0.88888889  0.53333333
## 231  0.49019608  0.62500000  0.78947368  0.00000000 -0.26315789
## 232  0.09803922  0.78947368  0.59523810  0.05494505  0.27777778
## 233  0.54901961  0.15686275  0.37500000  0.54901961  0.15686275
## 234 -0.15789474 -0.16666667  0.06666667  0.38888889  0.50000000
## 235 -0.05882353  0.16666667 -0.06250000 -0.06250000 -0.06666667
## 236  0.20879121  0.06666667  0.12500000  0.02380952  0.20879121
## 237 -0.12500000  0.33333333  0.18750000 -0.12500000 -0.11764706
## 238 -0.05882353  0.16666667  0.16666667  0.25000000  0.25000000
## 239  0.06666667  0.21568627  0.21568627  0.38888889  0.02380952
## 240  0.18750000  0.18750000  0.33333333 -0.11764706  0.44444444
## 241 -0.05882353 -0.05263158  0.12500000 -0.05882353 -0.05555556
## 242 -0.06666667 -0.06666667 -0.05555556 -0.05882353 -0.06250000
## 243  0.94444444  0.89473684 -0.17647059 -0.16666667  0.21568627
## 244  0.43750000  0.12500000 -0.15789474  0.33333333  0.38888889
## 245  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 246 -0.05263158  0.50000000 -0.05263158  0.33333333  0.50000000
## 247  0.33333333 -0.05263158  0.50000000  0.33333333 -0.06250000
## 248  0.21568627  0.33333333  0.33333333  0.33333333  0.21568627
## 249  0.68750000  0.33333333  0.88888889  0.33333333  0.15686275
## 250  0.60000000  0.50000000  1.00000000  0.43750000  0.38888889
## 251 -0.16666667  0.21568627  0.12500000 -0.17647059  0.21568627
## 252  0.00000000  0.00000000  0.00000000  0.00000000         NaN
## 253 -0.05263158 -0.06250000 -0.05882353 -0.05882353  0.33333333
## 254  0.20000000  0.25000000  0.33333333 -0.06666667  0.20000000
## 255 -0.12500000  0.28571429  0.28571429  0.27450980  0.50000000
## 256  0.94736842  0.40000000  0.27450980  0.50000000  0.13333333
## 257 -0.23529412  0.42857143         NaN  0.15686275  0.06250000
## 258  0.18750000  0.27450980  0.27450980  0.18750000  0.13333333
## 259  0.40000000  0.33333333  0.66666667  0.27450980 -0.11764706
## 260  0.66666667         NaN  0.66666667 -0.11111111  0.27450980
## 261 -0.11764706  0.13333333 -0.11764706 -0.13333333  0.27450980
## 262  0.00000000  0.00000000  0.00000000  0.00000000  0.00000000
## 263  0.00000000         NaN  0.00000000  0.00000000  0.00000000
## 264  0.25000000  0.33333333  0.25000000  0.25000000  0.33333333
## 265  0.33333333  0.38888889  0.94444444  0.33333333  0.75000000
## 266  0.50000000  0.20000000  0.33333333  0.25000000  0.33333333
## 267  0.66666667  0.50000000  0.27450980  0.66666667  0.94736842
## 268  0.50000000  0.66666667  0.13333333  0.50000000  0.50000000
## 269  0.33333333  0.20000000  0.50000000  0.25000000  0.16666667
## 270  0.25000000  0.33333333  0.20000000  0.33333333  0.20000000
## 271  0.20000000  0.20000000  0.25000000  0.33333333  0.20000000
## 272 -0.05263158 -0.06250000  0.20000000  0.50000000 -0.05555556
## 273  0.18750000  0.27450980 -0.11764706  0.28571429 -0.11111111
## 274  0.33333333  0.20000000  0.20000000  0.20000000  0.33333333
## 275  0.18750000  0.27450980  0.66666667  0.40000000  0.27450980
## 276 -0.08333333  0.26262626  0.08333333  0.12500000  0.34343434
## 277  0.26373626  0.14285714  0.04395604 -0.04166667  0.53333333
## 278  0.63636364  0.50000000  0.58333333  0.70000000  0.37373737
## 279 -0.02380952 -0.10000000 -0.04166667 -0.20000000  0.17171717
## 280  0.47368421 -0.05555556  0.25000000  0.52941176 -0.25490196
## 281 -0.05263158 -0.05263158 -0.05555556 -0.05555556 -0.05555556
## 282 -0.01960784  0.06666667 -0.38888889  0.68421053         NaN
## 283  0.27450980  0.33333333  0.18750000 -0.11111111  0.27450980
## 284  0.33333333  0.40000000  0.43137255 -0.13333333  0.46464646
## 285  0.12500000  0.06666667 -0.20000000  0.21568627  0.75000000
## 286  0.26190476  0.06666667 -0.15789474 -0.17647059  0.43750000
## 287  0.83333333  0.27777778  0.35714286  0.09803922  0.49019608
## 288 -0.17647059  0.21568627  0.21568627  0.33333333  0.06666667
## 289 -0.22222222  0.33333333  0.15686275  0.37500000  0.37500000
## 290  0.18750000  0.25000000  0.13333333 -0.11111111  0.33333333
## 291 -0.16666667 -0.17647059  0.12500000 -0.16666667  0.43750000
## 292  0.26190476 -0.17647059  0.38888889  0.33333333  0.89473684
## 293  0.19047619  0.84210526  0.54901961  0.26666667  0.88888889
## 294  0.15686275  0.84210526 -0.21052632  0.37500000  0.33333333
## 295  0.84210526 -0.21052632  0.84210526  0.33333333 -0.21052632
## 296  0.60784314  0.21568627 -0.15789474  0.43750000  0.43750000
## 297 -0.17647059  0.38888889  0.43750000  0.43750000  0.21568627
## 298  0.50505051  0.50505051  0.20000000  0.62500000  0.70707071
## 299  0.66666667  0.44444444  0.66666667  0.18750000  0.44444444
## 300  0.94736842  0.44444444 -0.11111111 -0.11764706  0.44444444
## 
## $similarity
##             1         2         3         4         5
## 1   0.2500000 0.3333333 0.2500000 0.4000000 0.4444444
## 2   0.4000000 0.4615385 0.6153846 0.5000000 0.6153846
## 3   0.4000000 0.6666667 0.6666667 0.5882353 0.5333333
## 4   0.5555556 0.5333333 0.5714286 0.7142857 0.5000000
## 5   0.3076923 0.1818182 0.5000000 0.2857143 0.2666667
## 6   0.8235294 0.7272727 0.7368421 0.7826087 0.7619048
## 7   0.6250000 0.1818182 0.4615385 0.5333333 0.5000000
## 8   0.6250000 0.6250000 0.5555556 0.6666667 0.5714286
## 9   0.6250000 0.7368421 0.5000000 0.5000000 0.6666667
## 10  0.5000000 0.5000000 0.4285714 0.5333333 0.7272727
## 11  0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 12  0.8181818 0.8333333 0.7826087 0.8800000 0.8333333
## 13  0.8000000 0.8571429 0.8571429 0.8888889 0.8000000
## 14  0.7777778 0.7777778 0.6666667 0.7058824 0.5333333
## 15  0.8000000 0.7826087 0.7200000 0.8800000 0.6000000
## 16  0.6956522 0.7368421 0.7272727 0.8333333 0.6956522
## 17  0.5333333 0.6666667 0.3750000 0.8333333 0.7142857
## 18  0.7058824 0.7058824 0.6666667 0.6666667 0.7058824
## 19  0.8000000 0.8461538 0.8888889 0.8461538 0.9285714
## 20  0.5263158 0.6000000 0.7000000 0.6000000 0.6315789
## 21  0.6315789 0.6666667 0.5263158 0.7000000 0.8235294
## 22  0.4705882 0.5263158 0.5263158 0.6000000 0.7000000
## 23  0.4285714 0.4615385 0.4285714 0.4615385 0.5000000
## 24  0.5263158 0.5555556 0.3750000 0.4705882 0.7619048
## 25  0.4615385 0.7272727 0.6153846 0.4705882 0.4000000
## 26  0.5000000 0.6000000 0.3076923 0.6666667 0.5454545
## 27  0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 28  0.6315789 0.6250000 0.5000000 0.4615385 0.5000000
## 29  0.5333333 0.4615385 0.4285714 0.7142857 0.5333333
## 30  0.5882353 0.7058824 0.4705882 0.7058824 0.4444444
## 31  0.7272727 0.6956522 0.7272727 0.8333333 0.6666667
## 32  0.7826087 0.8695652 0.7368421 0.5263158 0.8695652
## 33  0.6666667 0.7500000 0.8000000 0.5333333 0.6666667
## 34  0.7000000 0.7000000 0.6315789 0.5333333 0.6666667
## 35  0.9090909 0.6250000 0.6250000 0.7777778 0.7777778
## 36  0.5714286 0.8000000 0.5882353 0.5555556 0.7000000
## 37  0.7272727 0.7272727 0.7619048 0.8181818 0.7500000
## 38  0.5882353 0.6000000 0.7368421 0.5555556 0.5555556
## 39  0.5000000 0.7619048 0.7272727 0.6315789 0.5263158
## 40  0.7368421 0.7000000 0.7777778 0.8571429 0.5555556
## 41  0.7000000 0.8571429 0.7368421 0.7368421 0.7619048
## 42  0.3529412 0.5882353 0.5000000 0.5263158 0.7619048
## 43  0.7368421 0.6250000 0.5000000 0.6666667 0.7500000
## 44  0.6666667 0.6666667 0.7777778 0.4285714 0.5555556
## 45  0.7058824 0.5882353 0.8421053 0.7368421 0.7826087
## 46  0.6666667 0.6666667 0.7058824 0.7058824 0.8000000
## 47  0.5454545 0.5714286 0.6250000 0.4615385 0.4285714
## 48  0.7368421 0.5000000 0.6666667 0.4000000 0.5555556
## 49  0.4000000 0.5555556 0.7500000 0.8235294 0.6250000
## 50  0.7000000 0.8695652 0.5000000 0.7272727 0.9090909
## 51  0.4000000 0.5882353 0.6250000 0.6250000 0.5882353
## 52  0.6666667 0.3333333 0.3636364 0.4615385 0.5000000
## 53  0.7619048 0.7142857 0.6666667 0.7000000 0.6315789
## 54  0.7619048 0.7619048 0.7368421 0.8461538 0.6315789
## 55  0.6153846 0.5000000 0.5333333 0.5333333 0.5000000
## 56  0.4000000 0.5000000 0.4615385 0.5000000 0.5000000
## 57  0.7000000 0.6250000 0.6000000 0.6666667 0.4444444
## 58  0.4705882 0.7142857 0.7000000 0.6666667 0.7058824
## 59  0.7272727 0.8000000 0.8333333 0.8571429 0.7826087
## 60  0.1538462 0.6666667 0.5454545 0.4444444 0.2000000
## 61  0.7500000 0.6666667 0.6315789 0.7368421 0.8571429
## 62  0.7500000 0.7500000 0.6250000 0.8235294 0.6666667
## 63  0.4000000 0.6666667 0.1666667 0.4285714 0.6666667
## 64  0.6666667 0.6000000 0.7272727 0.5333333 0.5714286
## 65  0.3076923 0.7058824 0.3529412 0.5714286 0.5000000
## 66  0.4705882 0.6666667 0.6250000 0.5882353 0.2857143
## 67  0.5555556 0.6666667 0.5882353 0.4210526 0.6315789
## 68  0.8571429 0.7272727 0.8333333 0.8000000 0.8333333
## 69  0.5333333 0.5333333 0.4615385 0.5333333 0.3750000
## 70  0.7000000 0.6315789 0.6666667 0.4705882 0.6666667
## 71  0.4705882 0.6153846 0.5333333 0.5882353 0.5000000
## 72  0.5263158 0.6315789 0.6315789 0.7000000 0.6315789
## 73  0.9090909 0.7368421 0.5000000 0.7619048 0.8000000
## 74  0.5555556 0.6315789 0.8235294 0.5714286 0.2666667
## 75  0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 76  0.6956522 0.8000000 0.6666667 0.6000000 0.7500000
## 77  0.7826087 0.4444444 0.6666667 0.7272727 0.7000000
## 78  0.7619048 0.8000000 0.6315789 0.7000000 0.8000000
## 79  0.2222222 0.0000000 0.1818182 0.3636364 0.0000000
## 80  0.3636364 0.4285714 0.4000000 0.6666667 0.2000000
## 81  0.8181818 0.5882353 0.7000000 0.6000000 0.3750000
## 82  0.7058824 0.2857143 0.5454545 0.5882353 0.6666667
## 83  0.5882353 0.6363636 0.6315789 0.5882353 0.5000000
## 84  0.2857143 0.0000000 0.0000000 0.5000000 0.0000000
## 85  0.4444444 0.2500000 0.4000000 0.3636364 0.2222222
## 86  0.3333333 0.1666667 0.5333333 0.4285714 0.5714286
## 87  0.6315789 0.4705882 0.3529412 0.3529412 0.4705882
## 88  0.7058824 0.6250000 0.3750000 0.4000000 0.7058824
## 89  0.5882353 0.3750000 0.4285714 0.4705882 0.4285714
## 90  0.6666667 0.5882353 0.1428571 0.3636364 0.1666667
## 91  0.5882353 0.2857143 0.5555556 0.5555556 0.5882353
## 92  0.4210526 0.8000000 0.5000000 0.7619048 0.6956522
## 93  0.6666667 0.8000000 0.8000000 0.5000000 0.7619048
## 94  0.6666667 0.5882353 0.4615385 0.5714286 0.6250000
## 95  0.5454545 0.5454545 0.6666667 0.6666667 0.6666667
## 96  0.6250000 0.5333333 0.6250000 0.5714286 0.3076923
## 97  0.5000000 0.5555556 0.7142857 0.2666667 0.5882353
## 98  0.5333333 0.5333333 0.6250000 0.5333333 0.6666667
## 99  0.4000000 0.2352941 0.6250000 0.4000000 0.5000000
## 100 0.4000000 0.7500000 0.5333333 0.1818182 0.2857143
## 101 0.3636364 0.3750000 0.3076923 0.5333333 0.0000000
## 102 0.5000000 0.6666667 0.6153846 0.3333333 0.4615385
## 103 0.6250000 0.5000000 0.7142857 0.5263158 0.3333333
## 104 0.5263158 0.3529412 0.8000000 0.6000000 0.5263158
## 105 0.2222222 0.5714286 0.4000000 0.5333333 0.4444444
## 106 0.2222222 0.3333333 0.3636364 0.4444444 0.5454545
## 107 0.4000000 0.4444444 0.4000000 0.4615385 0.3333333
## 108 0.4000000 0.4444444 0.6250000 0.4285714 0.8000000
## 109 0.3529412 0.3750000 0.4444444 0.5882353 0.5000000
## 110 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 111 0.3636364 0.3076923 0.5714286 0.3076923 0.6666667
## 112 0.3333333 0.3750000 0.6666667 0.5333333 0.4285714
## 113 0.1818182 0.1818182 0.1538462 0.4000000 0.5714286
## 114 0.2666667 0.3636364 0.3076923 0.2000000 0.3636364
## 115 0.4705882 0.7777778 0.1818182 0.4444444 0.4285714
## 116 0.2857143 0.2000000 0.2500000 0.2500000 0.2857143
## 117 0.5263158 0.5555556 0.6666667 0.7058824 0.2857143
## 118 0.6666667 0.7000000 0.6666667 0.6315789 0.6666667
## 119 0.4444444 0.0000000 0.5000000 0.2500000 0.6666667
## 120 0.0000000 0.0000000 0.2000000 0.0000000 0.1666667
## 121 0.6250000 0.1538462 0.4000000 0.2857143 0.1818182
## 122 0.6666667 0.6666667 0.6000000 0.1333333 0.6000000
## 123 0.4000000 0.4285714 0.4000000 0.4285714 0.3333333
## 124 0.0000000 0.4000000 0.4000000 0.1818182 0.2222222
## 125 0.0000000 0.2500000 0.0000000 0.2857143 0.0000000
## 126 0.2666667 0.7058824 0.2666667 0.3333333 0.1666667
## 127 0.1538462 0.5882353 0.3750000 0.2857143 0.1666667
## 128 0.6315789 0.5882353 0.6315789 0.5000000 0.8571429
## 129 0.6250000 0.8235294 0.5714286 0.1818182 0.0000000
## 130 0.6363636 0.4444444 0.5882353 0.6315789 0.4705882
## 131 0.5263158 0.3076923 0.7777778 0.7058824 0.4000000
## 132 0.4285714 0.5000000 0.4705882 0.3076923 0.4285714
## 133 0.2222222 0.6000000 0.3636364 0.1666667 0.4444444
## 134 0.3076923 0.3750000 0.3750000 0.4285714 0.5000000
## 135 0.5882353 0.1333333 0.5882353 0.3750000 0.2666667
## 136 0.5000000 0.4615385 0.4615385 0.4000000 0.5333333
## 137 0.5714286 0.6250000 0.3750000 0.6250000 0.3333333
## 138 0.3750000 0.5000000 0.4444444 0.6250000 0.2222222
## 139 0.0000000 0.0000000 0.0000000 0.3636364 0.3636364
## 140 0.4444444 0.5000000 0.0000000 0.6666667 0.4444444
## 141 0.3636364 0.1818182 0.5714286 0.5454545 0.4615385
## 142 0.1666667 0.2000000 0.4615385 0.4615385 0.4615385
## 143 0.3636364 0.4615385 0.3333333 0.3333333 0.4444444
## 144 0.1666667 0.1818182 0.5714286 0.3333333 0.3750000
## 145 0.3076923 0.3333333 0.4444444 0.5714286 0.2000000
## 146 0.2500000 0.5454545 0.2500000 0.2222222 0.4444444
## 147 0.6666667 0.7272727 0.0000000 0.2222222 0.5714286
## 148 0.4615385 0.3076923 0.5000000 0.1818182 0.4615385
## 149 0.4705882 0.4615385 0.1538462 0.5333333 0.4000000
## 150 0.3333333 0.2000000 0.2222222 0.4000000 0.2000000
## 151 0.2857143 0.4000000 0.5000000 0.4000000 0.4615385
## 152 0.4444444 0.2500000 0.4444444 0.4285714 0.2857143
## 153 0.6250000 0.7500000 0.5000000 0.3636364 0.5000000
## 154 0.2857143 0.4000000 0.4444444 0.2857143 0.6666667
## 155 0.2500000 0.2222222 0.4444444 0.4000000 0.3333333
## 156 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 157 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 158 0.3333333 0.6666667 0.5000000 0.6153846 0.7058824
## 159 0.1666667 0.2857143 0.2666667 0.3076923 0.6666667
## 160 0.6666667 0.2857143 0.6000000 0.0000000 0.2857143
## 161 0.1818182 0.4000000 0.6153846 0.5454545 0.0000000
## 162 0.5714286 0.4444444 0.5454545 0.3636364 0.6666667
## 163 0.2857143 0.4000000 0.0000000 0.5000000 0.4444444
## 164 0.2857143 0.4000000 0.2000000 0.2222222 0.4444444
## 165 0.5714286 0.2500000 0.0000000 0.0000000 0.3333333
## 166 0.2857143 0.0000000 0.4444444 0.1818182 0.0000000
## 167 0.7142857 0.4615385 0.3333333 0.5333333 0.4615385
## 168 0.3636364 0.3333333 0.6666667 0.0000000 0.0000000
## 169 0.0000000 0.0000000 0.2500000 0.0000000 0.0000000
## 170 0.2500000 0.5454545 0.6666667 0.6000000 0.0000000
## 171 0.5714286 0.4000000 0.5454545 0.0000000 0.6000000
## 172 0.3076923 0.3636364 0.4000000 0.5454545 0.4615385
## 173 0.5000000 0.5000000 0.5882353 0.5333333 0.4000000
## 174 0.5000000 0.0000000 0.0000000 0.0000000 0.0000000
## 175 0.3636364 0.3636364 0.3333333 0.3076923 0.3076923
## 176 0.5454545 0.6153846 0.3636364 0.3636364 0.4000000
## 177 0.8571429 0.2500000 0.7500000 0.5000000 0.2857143
## 178 0.4000000 0.2857143 0.4615385 0.5000000 0.3076923
## 179 0.5714286 0.4285714 0.0000000 0.6666667 0.2000000
## 180 0.2500000 0.2000000 0.2500000 0.5000000 0.2000000
## 181 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 182 0.2500000 0.2000000 0.5000000 0.0000000 0.2500000
## 183 0.3076923 0.2222222 0.7500000 0.2857143 0.7500000
## 184 0.3333333 0.4000000 0.5000000 0.6666667 0.6666667
## 185 0.4615385 0.5000000 0.2222222 0.2857143 0.4000000
## 186 0.0000000 0.3333333 0.0000000 0.3333333 0.5000000
## 187 0.3636364 0.4000000 0.4285714 0.4444444 0.2500000
## 188 0.3333333 0.2857143 0.3333333 0.5000000 0.0000000
## 189 0.2857143 0.3333333 0.0000000 0.2222222 0.4444444
## 190 0.2000000 0.1538462 0.6666667 0.3529412 0.2857143
## 191 0.2857143 0.0000000 0.7500000 0.2500000 0.1818182
## 192 0.4000000 0.3333333 0.0000000 0.0000000 0.0000000
## 193 0.3333333 0.0000000 0.2857143 0.0000000 0.2857143
## 194 0.4000000 0.3636364 0.6000000 0.2000000 0.6000000
## 195 0.4285714 0.7272727 0.6666667 0.4285714 0.6666667
## 196 0.2857143 0.4000000 0.0000000 0.0000000 0.0000000
## 197 0.6666667 0.7142857 0.5000000 0.6666667 0.4285714
## 198 0.0000000 0.0000000 0.2500000 0.5000000 0.4000000
## 199 0.2857143 0.0000000 0.0000000 0.0000000 0.4000000
## 200 0.4000000 0.0000000 0.2000000 0.4444444 0.6666667
## 201 0.5714286 0.5000000 0.5000000 0.3333333 0.0000000
## 202 0.4615385 0.5454545 0.5333333 0.3636364 0.7142857
## 203 0.2222222 0.0000000 0.5000000 0.2222222 0.0000000
## 204 0.2857143 0.6666667 0.8571429 0.6000000 0.3333333
## 205 0.5000000 0.5000000 0.4000000 0.0000000 0.3636364
## 206 0.0000000 0.4000000 0.3333333 0.0000000 0.0000000
## 207 0.2857143 0.5000000 0.6666667 0.6666667 0.2857143
## 208 0.5714286 0.3636364 0.2500000 0.0000000 0.2500000
## 209 0.1818182 0.4000000 0.4444444 0.6000000 0.4000000
## 210 0.3333333 0.3333333 0.5000000 0.5000000 0.0000000
## 211 0.0000000 0.4000000 0.6666667 0.6666667 0.0000000
## 212 0.7500000 0.6666667 0.5714286 0.2857143 0.7500000
## 213 0.2857143 0.3333333 0.2857143 0.2500000 0.0000000
## 214 0.6666667 0.5714286 0.5000000 0.4444444 0.6000000
## 215 0.4000000 0.3636364 0.2500000 0.0000000 0.2500000
## 216 0.5714286 0.2857143 0.5000000 0.0000000 0.2222222
## 217 0.0000000 0.3636364 0.2000000 0.2857143 0.0000000
## 218 0.6666667 0.5000000 0.4444444 0.6666667 0.2500000
## 219 0.2222222 0.4000000 0.3636364 0.6666667 0.2857143
## 220 0.7272727 0.2857143 0.8000000 0.8000000 0.3636364
## 221 0.6666667 0.7500000 0.5714286 0.5714286 0.2857143
## 222 0.0000000 0.2000000 0.5000000 0.3636364 0.8000000
## 223 0.0000000 0.4000000 0.0000000 0.0000000 0.0000000
## 224 0.3333333 0.2222222 0.3333333 0.4444444 0.5000000
## 225 0.3333333 1.0000000 0.2500000 0.4000000 0.5000000
## 226 0.6666667 0.8000000 0.8333333 0.6000000 0.4444444
## 227 0.4615385 0.2222222 0.6153846 0.6666667 0.2000000
## 228 0.0000000 0.0000000 0.0000000 0.3333333 0.4000000
## 229 0.0000000 0.5000000 0.0000000 0.3333333 0.5000000
## 230 0.5714286 0.3333333 0.3636364 0.6666667 0.6666667
## 231 0.5000000 0.6666667 0.3333333 0.2222222 0.0000000
## 232 0.2500000 0.3333333 0.7272727 0.3333333 0.2857143
## 233 0.5714286 0.2857143 0.5000000 0.5714286 0.2857143
## 234 0.0000000 0.0000000 0.2500000 0.4000000 0.6666667
## 235 0.0000000 0.2857143 0.0000000 0.0000000 0.0000000
## 236 0.4000000 0.2500000 0.2857143 0.2222222 0.4000000
## 237 0.0000000 0.5000000 0.3333333 0.0000000 0.0000000
## 238 0.0000000 0.2857143 0.2857143 0.4000000 0.4000000
## 239 0.2500000 0.3333333 0.3333333 0.4000000 0.2222222
## 240 0.3333333 0.3333333 0.5000000 0.0000000 0.5000000
## 241 0.0000000 0.0000000 0.2222222 0.0000000 0.0000000
## 242 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 243 0.8000000 0.5000000 0.0000000 0.0000000 0.3333333
## 244 0.5714286 0.2857143 0.0000000 0.5000000 0.4000000
## 245 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 246 0.0000000 0.6666667 0.0000000 0.5000000 0.6666667
## 247 0.5000000 0.0000000 0.6666667 0.5000000 0.0000000
## 248 0.3333333 0.5000000 0.5000000 0.5000000 0.3333333
## 249 0.7500000 0.3333333 0.6666667 0.3333333 0.2857143
## 250 0.7500000 0.6666667 1.0000000 0.5714286 0.4000000
## 251 0.0000000 0.3333333 0.2857143 0.0000000 0.3333333
## 252 0.0000000 0.0000000 0.0000000 0.0000000       NaN
## 253 0.0000000 0.0000000 0.0000000 0.0000000 0.5000000
## 254 0.3333333 0.4000000 0.5000000 0.0000000 0.3333333
## 255 0.0000000 0.4444444 0.4444444 0.4000000 0.6666667
## 256 0.6666667 0.5714286 0.4000000 0.6666667 0.2857143
## 257 0.0000000 0.6000000 0.0000000 0.2857143 0.2500000
## 258 0.3333333 0.4000000 0.4000000 0.3333333 0.2857143
## 259 0.5714286 0.5000000 0.8000000 0.4000000 0.0000000
## 260 0.8000000 0.0000000 0.8000000 0.0000000 0.4000000
## 261 0.0000000 0.2857143 0.0000000 0.0000000 0.4000000
## 262 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 263 0.0000000       NaN 0.0000000 0.0000000 0.0000000
## 264 0.4000000 0.5000000 0.4000000 0.4000000 0.5000000
## 265 0.5000000 0.4000000 0.8000000 0.5000000 0.8571429
## 266 0.6666667 0.3333333 0.5000000 0.4000000 0.5000000
## 267 0.8000000 0.6666667 0.4000000 0.8000000 0.6666667
## 268 0.6666667 0.8000000 0.2857143 0.6666667 0.6666667
## 269 0.5000000 0.3333333 0.6666667 0.4000000 0.2857143
## 270 0.4000000 0.5000000 0.3333333 0.5000000 0.3333333
## 271 0.3333333 0.3333333 0.4000000 0.5000000 0.3333333
## 272 0.0000000 0.0000000 0.3333333 0.6666667 0.0000000
## 273 0.3333333 0.4000000 0.0000000 0.4444444 0.0000000
## 274 0.5000000 0.3333333 0.3333333 0.3333333 0.5000000
## 275 0.3333333 0.4000000 0.8000000 0.5714286 0.4000000
## 276 0.2857143 0.5333333 0.4444444 0.4285714 0.5882353
## 277 0.5333333 0.4285714 0.4000000 0.3750000 0.6153846
## 278 0.7777778 0.6666667 0.7368421 0.8235294 0.6250000
## 279 0.3076923 0.3529412 0.4210526 0.1666667 0.5000000
## 280 0.1666667 0.1538462 0.4000000 0.4285714 0.1428571
## 281 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 282 0.2000000 0.3333333 0.0000000 0.2500000 0.0000000
## 283 0.4000000 0.5000000 0.3333333 0.0000000 0.4000000
## 284 0.5714286 0.5454545 0.4444444 0.1818182 0.6666667
## 285 0.2857143 0.2500000 0.0000000 0.3333333 0.8571429
## 286 0.4444444 0.2500000 0.0000000 0.0000000 0.5714286
## 287 0.5714286 0.2857143 0.5454545 0.2500000 0.5000000
## 288 0.0000000 0.3333333 0.3333333 0.5000000 0.2500000
## 289 0.0000000 0.3333333 0.2857143 0.5000000 0.5000000
## 290 0.3333333 0.4000000 0.2857143 0.0000000 0.5000000
## 291 0.0000000 0.0000000 0.2857143 0.0000000 0.5714286
## 292 0.4444444 0.0000000 0.4000000 0.5000000 0.5000000
## 293 0.4000000 0.4000000 0.5714286 0.4444444 0.6666667
## 294 0.2857143 0.4000000 0.0000000 0.5000000 0.3333333
## 295 0.4000000 0.0000000 0.4000000 0.3333333 0.0000000
## 296 0.6666667 0.3333333 0.0000000 0.5714286 0.5714286
## 297 0.0000000 0.4000000 0.5714286 0.5714286 0.3333333
## 298 0.7619048 0.7619048 0.6000000 0.7777778 0.8571429
## 299 0.8000000 0.5000000 0.8000000 0.3333333 0.5000000
## 300 0.6666667 0.5000000 0.0000000 0.0000000 0.5000000
## 
## $Jaccard
##              1          2          3          4          5
## 1   0.14285714 0.20000000 0.14285714 0.25000000 0.28571429
## 2   0.25000000 0.30000000 0.44444444 0.33333333 0.44444444
## 3   0.25000000 0.50000000 0.50000000 0.41666667 0.36363636
## 4   0.38461538 0.36363636 0.40000000 0.55555556 0.33333333
## 5   0.18181818 0.10000000 0.33333333 0.16666667 0.15384615
## 6   0.70000000 0.57142857 0.58333333 0.64285714 0.61538462
## 7   0.45454545 0.10000000 0.30000000 0.36363636 0.33333333
## 8   0.45454545 0.45454545 0.38461538 0.50000000 0.40000000
## 9   0.45454545 0.58333333 0.33333333 0.33333333 0.50000000
## 10  0.33333333 0.33333333 0.27272727 0.36363636 0.57142857
## 11  0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 12  0.69230769 0.71428571 0.64285714 0.78571429 0.71428571
## 13  0.66666667 0.75000000 0.75000000 0.80000000 0.66666667
## 14  0.63636364 0.63636364 0.50000000 0.54545455 0.36363636
## 15  0.66666667 0.64285714 0.56250000 0.78571429 0.42857143
## 16  0.53333333 0.58333333 0.57142857 0.71428571 0.53333333
## 17  0.36363636 0.50000000 0.23076923 0.71428571 0.55555556
## 18  0.54545455 0.54545455 0.50000000 0.50000000 0.54545455
## 19  0.66666667 0.73333333 0.80000000 0.73333333 0.86666667
## 20  0.35714286 0.42857143 0.53846154 0.42857143 0.46153846
## 21  0.46153846 0.50000000 0.35714286 0.53846154 0.70000000
## 22  0.30769231 0.35714286 0.35714286 0.42857143 0.53846154
## 23  0.27272727 0.30000000 0.27272727 0.30000000 0.33333333
## 24  0.35714286 0.38461538 0.23076923 0.30769231 0.61538462
## 25  0.30000000 0.57142857 0.44444444 0.30769231 0.25000000
## 26  0.33333333 0.42857143 0.18181818 0.50000000 0.37500000
## 27  0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 28  0.46153846 0.45454545 0.33333333 0.30000000 0.33333333
## 29  0.36363636 0.30000000 0.27272727 0.55555556 0.36363636
## 30  0.41666667 0.54545455 0.30769231 0.54545455 0.28571429
## 31  0.57142857 0.53333333 0.57142857 0.71428571 0.50000000
## 32  0.64285714 0.76923077 0.58333333 0.35714286 0.76923077
## 33  0.50000000 0.60000000 0.66666667 0.36363636 0.50000000
## 34  0.53846154 0.53846154 0.46153846 0.36363636 0.50000000
## 35  0.83333333 0.45454545 0.45454545 0.63636364 0.63636364
## 36  0.40000000 0.66666667 0.41666667 0.38461538 0.53846154
## 37  0.57142857 0.57142857 0.61538462 0.69230769 0.60000000
## 38  0.41666667 0.42857143 0.58333333 0.38461538 0.38461538
## 39  0.33333333 0.61538462 0.57142857 0.46153846 0.35714286
## 40  0.58333333 0.53846154 0.63636364 0.75000000 0.38461538
## 41  0.53846154 0.75000000 0.58333333 0.58333333 0.61538462
## 42  0.21428571 0.41666667 0.33333333 0.35714286 0.61538462
## 43  0.58333333 0.45454545 0.33333333 0.50000000 0.60000000
## 44  0.50000000 0.50000000 0.63636364 0.27272727 0.38461538
## 45  0.54545455 0.41666667 0.72727273 0.58333333 0.64285714
## 46  0.50000000 0.50000000 0.54545455 0.54545455 0.66666667
## 47  0.37500000 0.40000000 0.45454545 0.30000000 0.27272727
## 48  0.58333333 0.33333333 0.50000000 0.25000000 0.38461538
## 49  0.25000000 0.38461538 0.60000000 0.70000000 0.45454545
## 50  0.53846154 0.76923077 0.33333333 0.57142857 0.83333333
## 51  0.25000000 0.41666667 0.45454545 0.45454545 0.41666667
## 52  0.50000000 0.20000000 0.22222222 0.30000000 0.33333333
## 53  0.61538462 0.55555556 0.50000000 0.53846154 0.46153846
## 54  0.61538462 0.61538462 0.58333333 0.73333333 0.46153846
## 55  0.44444444 0.33333333 0.36363636 0.36363636 0.33333333
## 56  0.25000000 0.33333333 0.30000000 0.33333333 0.33333333
## 57  0.53846154 0.45454545 0.42857143 0.50000000 0.28571429
## 58  0.30769231 0.55555556 0.53846154 0.50000000 0.54545455
## 59  0.57142857 0.66666667 0.71428571 0.75000000 0.64285714
## 60  0.08333333 0.50000000 0.37500000 0.28571429 0.11111111
## 61  0.60000000 0.50000000 0.46153846 0.58333333 0.75000000
## 62  0.60000000 0.60000000 0.45454545 0.70000000 0.50000000
## 63  0.25000000 0.50000000 0.09090909 0.27272727 0.50000000
## 64  0.50000000 0.42857143 0.57142857 0.36363636 0.40000000
## 65  0.18181818 0.54545455 0.21428571 0.40000000 0.33333333
## 66  0.30769231 0.50000000 0.45454545 0.41666667 0.16666667
## 67  0.38461538 0.50000000 0.41666667 0.26666667 0.46153846
## 68  0.75000000 0.57142857 0.71428571 0.66666667 0.71428571
## 69  0.36363636 0.36363636 0.30000000 0.36363636 0.23076923
## 70  0.53846154 0.46153846 0.50000000 0.30769231 0.50000000
## 71  0.30769231 0.44444444 0.36363636 0.41666667 0.33333333
## 72  0.35714286 0.46153846 0.46153846 0.53846154 0.46153846
## 73  0.83333333 0.58333333 0.33333333 0.61538462 0.66666667
## 74  0.38461538 0.46153846 0.70000000 0.40000000 0.15384615
## 75  0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 76  0.53333333 0.66666667 0.50000000 0.42857143 0.60000000
## 77  0.64285714 0.28571429 0.50000000 0.57142857 0.53846154
## 78  0.61538462 0.66666667 0.46153846 0.53846154 0.66666667
## 79  0.12500000 0.00000000 0.10000000 0.22222222 0.00000000
## 80  0.22222222 0.27272727 0.25000000 0.50000000 0.11111111
## 81  0.69230769 0.41666667 0.53846154 0.42857143 0.23076923
## 82  0.54545455 0.16666667 0.37500000 0.41666667 0.50000000
## 83  0.41666667 0.46666667 0.46153846 0.41666667 0.33333333
## 84  0.16666667 0.00000000 0.00000000 0.33333333 0.00000000
## 85  0.28571429 0.14285714 0.25000000 0.22222222 0.12500000
## 86  0.20000000 0.09090909 0.36363636 0.27272727 0.40000000
## 87  0.46153846 0.30769231 0.21428571 0.21428571 0.30769231
## 88  0.54545455 0.45454545 0.23076923 0.25000000 0.54545455
## 89  0.41666667 0.23076923 0.27272727 0.30769231 0.27272727
## 90  0.50000000 0.41666667 0.07692308 0.22222222 0.09090909
## 91  0.41666667 0.16666667 0.38461538 0.38461538 0.41666667
## 92  0.26666667 0.66666667 0.33333333 0.61538462 0.53333333
## 93  0.50000000 0.66666667 0.66666667 0.33333333 0.61538462
## 94  0.50000000 0.41666667 0.30000000 0.40000000 0.45454545
## 95  0.37500000 0.37500000 0.50000000 0.50000000 0.50000000
## 96  0.45454545 0.36363636 0.45454545 0.40000000 0.18181818
## 97  0.33333333 0.38461538 0.55555556 0.15384615 0.41666667
## 98  0.36363636 0.36363636 0.45454545 0.36363636 0.50000000
## 99  0.25000000 0.13333333 0.45454545 0.25000000 0.33333333
## 100 0.25000000 0.60000000 0.36363636 0.10000000 0.16666667
## 101 0.22222222 0.23076923 0.18181818 0.36363636 0.00000000
## 102 0.33333333 0.50000000 0.44444444 0.20000000 0.30000000
## 103 0.45454545 0.33333333 0.55555556 0.35714286 0.20000000
## 104 0.35714286 0.21428571 0.66666667 0.42857143 0.35714286
## 105 0.12500000 0.40000000 0.25000000 0.36363636 0.28571429
## 106 0.12500000 0.20000000 0.22222222 0.28571429 0.37500000
## 107 0.25000000 0.28571429 0.25000000 0.30000000 0.20000000
## 108 0.25000000 0.28571429 0.45454545 0.27272727 0.66666667
## 109 0.21428571 0.23076923 0.28571429 0.41666667 0.33333333
## 110 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 111 0.22222222 0.18181818 0.40000000 0.18181818 0.50000000
## 112 0.20000000 0.23076923 0.50000000 0.36363636 0.27272727
## 113 0.10000000 0.10000000 0.08333333 0.25000000 0.40000000
## 114 0.15384615 0.22222222 0.18181818 0.11111111 0.22222222
## 115 0.30769231 0.63636364 0.10000000 0.28571429 0.27272727
## 116 0.16666667 0.11111111 0.14285714 0.14285714 0.16666667
## 117 0.35714286 0.38461538 0.50000000 0.54545455 0.16666667
## 118 0.50000000 0.53846154 0.50000000 0.46153846 0.50000000
## 119 0.28571429 0.00000000 0.33333333 0.14285714 0.50000000
## 120 0.00000000 0.00000000 0.11111111 0.00000000 0.09090909
## 121 0.45454545 0.08333333 0.25000000 0.16666667 0.10000000
## 122 0.50000000 0.50000000 0.42857143 0.07142857 0.42857143
## 123 0.25000000 0.27272727 0.25000000 0.27272727 0.20000000
## 124 0.00000000 0.25000000 0.25000000 0.10000000 0.12500000
## 125 0.00000000 0.14285714 0.00000000 0.16666667 0.00000000
## 126 0.15384615 0.54545455 0.15384615 0.20000000 0.09090909
## 127 0.08333333 0.41666667 0.23076923 0.16666667 0.09090909
## 128 0.46153846 0.41666667 0.46153846 0.33333333 0.75000000
## 129 0.45454545 0.70000000 0.40000000 0.10000000 0.00000000
## 130 0.46666667 0.28571429 0.41666667 0.46153846 0.30769231
## 131 0.35714286 0.18181818 0.63636364 0.54545455 0.25000000
## 132 0.27272727 0.33333333 0.30769231 0.18181818 0.27272727
## 133 0.12500000 0.42857143 0.22222222 0.09090909 0.28571429
## 134 0.18181818 0.23076923 0.23076923 0.27272727 0.33333333
## 135 0.41666667 0.07142857 0.41666667 0.23076923 0.15384615
## 136 0.33333333 0.30000000 0.30000000 0.25000000 0.36363636
## 137 0.40000000 0.45454545 0.23076923 0.45454545 0.20000000
## 138 0.23076923 0.33333333 0.28571429 0.45454545 0.12500000
## 139 0.00000000 0.00000000 0.00000000 0.22222222 0.22222222
## 140 0.28571429 0.33333333 0.00000000 0.50000000 0.28571429
## 141 0.22222222 0.10000000 0.40000000 0.37500000 0.30000000
## 142 0.09090909 0.11111111 0.30000000 0.30000000 0.30000000
## 143 0.22222222 0.30000000 0.20000000 0.20000000 0.28571429
## 144 0.09090909 0.10000000 0.40000000 0.20000000 0.23076923
## 145 0.18181818 0.20000000 0.28571429 0.40000000 0.11111111
## 146 0.14285714 0.37500000 0.14285714 0.12500000 0.28571429
## 147 0.50000000 0.57142857 0.00000000 0.12500000 0.40000000
## 148 0.30000000 0.18181818 0.33333333 0.10000000 0.30000000
## 149 0.30769231 0.30000000 0.08333333 0.36363636 0.25000000
## 150 0.20000000 0.11111111 0.12500000 0.25000000 0.11111111
## 151 0.16666667 0.25000000 0.33333333 0.25000000 0.30000000
## 152 0.28571429 0.14285714 0.28571429 0.27272727 0.16666667
## 153 0.45454545 0.60000000 0.33333333 0.22222222 0.33333333
## 154 0.16666667 0.25000000 0.28571429 0.16666667 0.50000000
## 155 0.14285714 0.12500000 0.28571429 0.25000000 0.20000000
## 156 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 157 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 158 0.20000000 0.50000000 0.33333333 0.44444444 0.54545455
## 159 0.09090909 0.16666667 0.15384615 0.18181818 0.50000000
## 160 0.50000000 0.16666667 0.42857143 0.00000000 0.16666667
## 161 0.10000000 0.25000000 0.44444444 0.37500000 0.00000000
## 162 0.40000000 0.28571429 0.37500000 0.22222222 0.50000000
## 163 0.16666667 0.25000000 0.00000000 0.33333333 0.28571429
## 164 0.16666667 0.25000000 0.11111111 0.12500000 0.28571429
## 165 0.40000000 0.14285714 0.00000000 0.00000000 0.20000000
## 166 0.16666667 0.00000000 0.28571429 0.10000000 0.00000000
## 167 0.55555556 0.30000000 0.20000000 0.36363636 0.30000000
## 168 0.22222222 0.20000000 0.50000000 0.00000000 0.00000000
## 169 0.00000000 0.00000000 0.14285714 0.00000000 0.00000000
## 170 0.14285714 0.37500000 0.50000000 0.42857143 0.00000000
## 171 0.40000000 0.25000000 0.37500000 0.00000000 0.42857143
## 172 0.18181818 0.22222222 0.25000000 0.37500000 0.30000000
## 173 0.33333333 0.33333333 0.41666667 0.36363636 0.25000000
## 174 0.33333333 0.00000000 0.00000000 0.00000000 0.00000000
## 175 0.22222222 0.22222222 0.20000000 0.18181818 0.18181818
## 176 0.37500000 0.44444444 0.22222222 0.22222222 0.25000000
## 177 0.75000000 0.14285714 0.60000000 0.33333333 0.16666667
## 178 0.25000000 0.16666667 0.30000000 0.33333333 0.18181818
## 179 0.40000000 0.27272727 0.00000000 0.50000000 0.11111111
## 180 0.14285714 0.11111111 0.14285714 0.33333333 0.11111111
## 181 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 182 0.14285714 0.11111111 0.33333333 0.00000000 0.14285714
## 183 0.18181818 0.12500000 0.60000000 0.16666667 0.60000000
## 184 0.20000000 0.25000000 0.33333333 0.50000000 0.50000000
## 185 0.30000000 0.33333333 0.12500000 0.16666667 0.25000000
## 186 0.00000000 0.20000000 0.00000000 0.20000000 0.33333333
## 187 0.22222222 0.25000000 0.27272727 0.28571429 0.14285714
## 188 0.20000000 0.16666667 0.20000000 0.33333333 0.00000000
## 189 0.16666667 0.20000000 0.00000000 0.12500000 0.28571429
## 190 0.11111111 0.08333333 0.50000000 0.21428571 0.16666667
## 191 0.16666667 0.00000000 0.60000000 0.14285714 0.10000000
## 192 0.25000000 0.20000000 0.00000000 0.00000000 0.00000000
## 193 0.20000000 0.00000000 0.16666667 0.00000000 0.16666667
## 194 0.25000000 0.22222222 0.42857143 0.11111111 0.42857143
## 195 0.27272727 0.57142857 0.50000000 0.27272727 0.50000000
## 196 0.16666667 0.25000000 0.00000000 0.00000000 0.00000000
## 197 0.50000000 0.55555556 0.33333333 0.50000000 0.27272727
## 198 0.00000000 0.00000000 0.14285714 0.33333333 0.25000000
## 199 0.16666667 0.00000000 0.00000000 0.00000000 0.25000000
## 200 0.25000000 0.00000000 0.11111111 0.28571429 0.50000000
## 201 0.40000000 0.33333333 0.33333333 0.20000000 0.00000000
## 202 0.30000000 0.37500000 0.36363636 0.22222222 0.55555556
## 203 0.12500000 0.00000000 0.33333333 0.12500000 0.00000000
## 204 0.16666667 0.50000000 0.75000000 0.42857143 0.20000000
## 205 0.33333333 0.33333333 0.25000000 0.00000000 0.22222222
## 206 0.00000000 0.25000000 0.20000000 0.00000000 0.00000000
## 207 0.16666667 0.33333333 0.50000000 0.50000000 0.16666667
## 208 0.40000000 0.22222222 0.14285714 0.00000000 0.14285714
## 209 0.10000000 0.25000000 0.28571429 0.42857143 0.25000000
## 210 0.20000000 0.20000000 0.33333333 0.33333333 0.00000000
## 211 0.00000000 0.25000000 0.50000000 0.50000000 0.00000000
## 212 0.60000000 0.50000000 0.40000000 0.16666667 0.60000000
## 213 0.16666667 0.20000000 0.16666667 0.14285714 0.00000000
## 214 0.50000000 0.40000000 0.33333333 0.28571429 0.42857143
## 215 0.25000000 0.22222222 0.14285714 0.00000000 0.14285714
## 216 0.40000000 0.16666667 0.33333333 0.00000000 0.12500000
## 217 0.00000000 0.22222222 0.11111111 0.16666667 0.00000000
## 218 0.50000000 0.33333333 0.28571429 0.50000000 0.14285714
## 219 0.12500000 0.25000000 0.22222222 0.50000000 0.16666667
## 220 0.57142857 0.16666667 0.66666667 0.66666667 0.22222222
## 221 0.50000000 0.60000000 0.40000000 0.40000000 0.16666667
## 222 0.00000000 0.11111111 0.33333333 0.22222222 0.66666667
## 223 0.00000000 0.25000000 0.00000000 0.00000000 0.00000000
## 224 0.20000000 0.12500000 0.20000000 0.28571429 0.33333333
## 225 0.20000000 1.00000000 0.14285714 0.25000000 0.33333333
## 226 0.50000000 0.66666667 0.71428571 0.42857143 0.28571429
## 227 0.30000000 0.12500000 0.44444444 0.50000000 0.11111111
## 228 0.00000000 0.00000000 0.00000000 0.20000000 0.25000000
## 229 0.00000000 0.33333333 0.00000000 0.20000000 0.33333333
## 230 0.40000000 0.20000000 0.22222222 0.50000000 0.50000000
## 231 0.33333333 0.50000000 0.20000000 0.12500000 0.00000000
## 232 0.14285714 0.20000000 0.57142857 0.20000000 0.16666667
## 233 0.40000000 0.16666667 0.33333333 0.40000000 0.16666667
## 234 0.00000000 0.00000000 0.14285714 0.25000000 0.50000000
## 235 0.00000000 0.16666667 0.00000000 0.00000000 0.00000000
## 236 0.25000000 0.14285714 0.16666667 0.12500000 0.25000000
## 237 0.00000000 0.33333333 0.20000000 0.00000000 0.00000000
## 238 0.00000000 0.16666667 0.16666667 0.25000000 0.25000000
## 239 0.14285714 0.20000000 0.20000000 0.25000000 0.12500000
## 240 0.20000000 0.20000000 0.33333333 0.00000000 0.33333333
## 241 0.00000000 0.00000000 0.12500000 0.00000000 0.00000000
## 242 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 243 0.66666667 0.33333333 0.00000000 0.00000000 0.20000000
## 244 0.40000000 0.16666667 0.00000000 0.33333333 0.25000000
## 245 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 246 0.00000000 0.50000000 0.00000000 0.33333333 0.50000000
## 247 0.33333333 0.00000000 0.50000000 0.33333333 0.00000000
## 248 0.20000000 0.33333333 0.33333333 0.33333333 0.20000000
## 249 0.60000000 0.20000000 0.50000000 0.20000000 0.16666667
## 250 0.60000000 0.50000000 1.00000000 0.40000000 0.25000000
## 251 0.00000000 0.20000000 0.16666667 0.00000000 0.20000000
## 252 0.00000000 0.00000000 0.00000000 0.00000000        NaN
## 253 0.00000000 0.00000000 0.00000000 0.00000000 0.33333333
## 254 0.20000000 0.25000000 0.33333333 0.00000000 0.20000000
## 255 0.00000000 0.28571429 0.28571429 0.25000000 0.50000000
## 256 0.50000000 0.40000000 0.25000000 0.50000000 0.16666667
## 257 0.00000000 0.42857143 0.00000000 0.16666667 0.14285714
## 258 0.20000000 0.25000000 0.25000000 0.20000000 0.16666667
## 259 0.40000000 0.33333333 0.66666667 0.25000000 0.00000000
## 260 0.66666667 0.00000000 0.66666667 0.00000000 0.25000000
## 261 0.00000000 0.16666667 0.00000000 0.00000000 0.25000000
## 262 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 263 0.00000000        NaN 0.00000000 0.00000000 0.00000000
## 264 0.25000000 0.33333333 0.25000000 0.25000000 0.33333333
## 265 0.33333333 0.25000000 0.66666667 0.33333333 0.75000000
## 266 0.50000000 0.20000000 0.33333333 0.25000000 0.33333333
## 267 0.66666667 0.50000000 0.25000000 0.66666667 0.50000000
## 268 0.50000000 0.66666667 0.16666667 0.50000000 0.50000000
## 269 0.33333333 0.20000000 0.50000000 0.25000000 0.16666667
## 270 0.25000000 0.33333333 0.20000000 0.33333333 0.20000000
## 271 0.20000000 0.20000000 0.25000000 0.33333333 0.20000000
## 272 0.00000000 0.00000000 0.20000000 0.50000000 0.00000000
## 273 0.20000000 0.25000000 0.00000000 0.28571429 0.00000000
## 274 0.33333333 0.20000000 0.20000000 0.20000000 0.33333333
## 275 0.20000000 0.25000000 0.66666667 0.40000000 0.25000000
## 276 0.16666667 0.36363636 0.28571429 0.27272727 0.41666667
## 277 0.36363636 0.27272727 0.25000000 0.23076923 0.44444444
## 278 0.63636364 0.50000000 0.58333333 0.70000000 0.45454545
## 279 0.18181818 0.21428571 0.26666667 0.09090909 0.33333333
## 280 0.09090909 0.08333333 0.25000000 0.27272727 0.07692308
## 281 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 282 0.11111111 0.20000000 0.00000000 0.14285714 0.00000000
## 283 0.25000000 0.33333333 0.20000000 0.00000000 0.25000000
## 284 0.40000000 0.37500000 0.28571429 0.10000000 0.50000000
## 285 0.16666667 0.14285714 0.00000000 0.20000000 0.75000000
## 286 0.28571429 0.14285714 0.00000000 0.00000000 0.40000000
## 287 0.40000000 0.16666667 0.37500000 0.14285714 0.33333333
## 288 0.00000000 0.20000000 0.20000000 0.33333333 0.14285714
## 289 0.00000000 0.20000000 0.16666667 0.33333333 0.33333333
## 290 0.20000000 0.25000000 0.16666667 0.00000000 0.33333333
## 291 0.00000000 0.00000000 0.16666667 0.00000000 0.40000000
## 292 0.28571429 0.00000000 0.25000000 0.33333333 0.33333333
## 293 0.25000000 0.25000000 0.40000000 0.28571429 0.50000000
## 294 0.16666667 0.25000000 0.00000000 0.33333333 0.20000000
## 295 0.25000000 0.00000000 0.25000000 0.20000000 0.00000000
## 296 0.50000000 0.20000000 0.00000000 0.40000000 0.40000000
## 297 0.00000000 0.25000000 0.40000000 0.40000000 0.20000000
## 298 0.61538462 0.61538462 0.42857143 0.63636364 0.75000000
## 299 0.66666667 0.33333333 0.66666667 0.20000000 0.33333333
## 300 0.50000000 0.33333333 0.00000000 0.00000000 0.33333333

Spatial Predictions and Projections

ESM Ensemble of Small Models

library(biomod2)
## Loading required package: raster
## 
## Attaching package: 'raster'
## The following objects are masked from 'package:ape':
## 
##     rotate, zoom
## Loading required package: reshape
## Loading required package: ggplot2
## biomod2 3.3-7 loaded.
## 
## Type browseVignettes(package='biomod2') to access directly biomod2 vignettes.
path.wd<-getwd()
# species
# occurrences
xy <- inv[,1:2]
head(xy)
##        x      y
## 1 142.25 -10.25
## 2 142.25 -10.75
## 3 131.25 -11.25
## 4 132.25 -11.25
## 5 142.25 -11.25
## 6 142.75 -11.25
sp_occ <- inv[11]

# env
current <- inv[3:7]
head(current)
##      aetpet    gdd      p      pet     stdp
## 1 0.3180346 7965.1 1595.7 1950.320 137.8134
## 2 0.2807616 7888.9 1693.7 1991.475 156.3950
## 3 0.2638533 8165.3 1595.0 2179.968 127.0621
## 4 0.2790938 8195.6 1346.0 1919.897 114.7686
## 5 0.3030646 7858.1 1711.1 1795.255 158.3286
## 6 0.3217786 7888.5 1711.1 1788.220 151.8030
## BIOMOD
setwd(path.wd)
t1 <- Sys.time()
sp<-1
### Formating the data with the BIOMOD_FormatingData() function form the package biomod2

myBiomodData <- BIOMOD_FormatingData( resp.var = as.numeric(sp_occ[,sp]),
                                      expl.var = current,
                                      resp.xy = xy,
                                      resp.name = colnames(sp_occ)[sp])
## 
## -=-=-=-=-=-=-=-=-=-=-=-= species_occ Data Formating -=-=-=-=-=-=-=-=-=-=-=-=
## 
##  Response variable name was converted into species.occ
## > No pseudo absences selection !
##       ! No data has been set aside for modeling evaluation
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
myBiomodOption <- Print_Default_ModelingOptions()
## 
##  Defaut modeling options. copy, change what you want paste it as arg to BIOMOD_ModelingOptions
## 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=  'BIOMOD.Model.Options'  -=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## GLM = list( type = 'quadratic',
##             interaction.level = 0,
##             myFormula = NULL,
##             test = 'AIC',
##             family = binomial(link = 'logit'),
##             mustart = 0.5,
##             control = glm.control(epsilon = 1e-08, maxit = 50
## , trace = FALSE) ),
## 
## 
## GBM = list( distribution = 'bernoulli',
##             n.trees = 2500,
##             interaction.depth = 7,
##             n.minobsinnode = 5,
##             shrinkage = 0.001,
##             bag.fraction = 0.5,
##             train.fraction = 1,
##             cv.folds = 3,
##             keep.data = FALSE,
##             verbose = FALSE,
##             perf.method = 'cv'),
## 
## GAM = list( algo = 'GAM_mgcv',
##             type = 's_smoother',
##             k = -1,
##             interaction.level = 0,
##             myFormula = NULL,
##             family = binomial(link = 'logit'),
##             method = 'GCV.Cp',
##             optimizer = c('outer','newton'),
##             select = FALSE,
##             knots = NULL,
##             paraPen = NULL,
##             control = list(nthreads = 1, irls.reg = 0, epsilon = 1e-07
## , maxit = 200, trace = FALSE, mgcv.tol = 1e-07, mgcv.half = 15
## , rank.tol = 1.49011611938477e-08
## , nlm = list(ndigit=7, gradtol=1e-06, stepmax=2, steptol=1e-04, iterlim=200, check.analyticals=0)
## , optim = list(factr=1e+07)
## , newton = list(conv.tol=1e-06, maxNstep=5, maxSstep=2, maxHalf=30, use.svd=0)
## , outerPIsteps = 0, idLinksBases = TRUE, scalePenalty = TRUE
## , keepData = FALSE, scale.est = fletcher, edge.correct = FALSE) ),
## 
## 
## CTA = list( method = 'class',
##             parms = 'default',
##             cost = NULL,
##             control = list(xval = 5, minbucket = 5, minsplit = 5
## , cp = 0.001, maxdepth = 25) ),
## 
## 
## ANN = list( NbCV = 5,
##             size = NULL,
##             decay = NULL,
##             rang = 0.1,
##             maxit = 200),
## 
## SRE = list( quant = 0.025),
## 
## FDA = list( method = 'mars',
##             add_args = NULL),
## 
## MARS = list( type = 'simple',
##              interaction.level = 0,
##              myFormula = NULL,
##              nk = NULL,
##              penalty = 2,
##              thresh = 0.001,
##              nprune = NULL,
##              pmethod = 'backward'),
## 
## RF = list( do.classif = TRUE,
##            ntree = 500,
##            mtry = 'default',
##            nodesize = 5,
##            maxnodes = NULL),
## 
## MAXENT.Phillips = list( path_to_maxent.jar = '/private/var/folders/y_/356n2gv11490bhhswc7m8m6w0000gn/T/RtmpDWdwN5/Rbuild6c666bd1c584/ecospat/vignettes',
##                memory_allocated = 512,
##                background_data_dir = 'default',
##                maximumbackground = 'default',
##                maximumiterations = 200,
##                visible = FALSE,
##                linear = TRUE,
##                quadratic = TRUE,
##                product = TRUE,
##                threshold = TRUE,
##                hinge = TRUE,
##                lq2lqptthreshold = 80,
##                l2lqthreshold = 10,
##                hingethreshold = 15,
##                beta_threshold = -1,
##                beta_categorical = -1,
##                beta_lqp = -1,
##                beta_hinge = -1,
##                betamultiplier = 1,
##                defaultprevalence = 0.5),
## 
## MAXENT.Tsuruoka = list( l1_regularizer = 0,
##                         l2_regularizer = 0,
##                         use_sgd = FALSE,
##                         set_heldout = 0,
##                         verbose = FALSE)
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
myBiomodOption@GLM$test = 'none'
myBiomodOption@GBM$interaction.depth = 2
### Calibration of simple bivariate models
my.ESM <- ecospat.ESM.Modeling( data=myBiomodData,
                                models=c('GLM','RF'),
                                models.options=myBiomodOption,
                                NbRunEval=1,
                                DataSplit=70,
                                weighting.score=c("AUC"),
                                parallel=F)  
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.1 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( aetpet gdd )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.1_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.1_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.1 ~ 1 + aetpet + I(aetpet^2) + gdd + I(gdd^2)
## <environment: 0x7fecf18410b0>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.1_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.1 ~ 1 + aetpet + I(aetpet^2) + gdd + I(gdd^2)
## <environment: 0x7fed0d4f16d8>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.2 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( aetpet p )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.2_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.2_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.2 ~ 1 + aetpet + I(aetpet^2) + p + I(p^2)
## <environment: 0x7fed103ec0e8>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.2_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.2 ~ 1 + aetpet + I(aetpet^2) + p + I(p^2)
## <environment: 0x7fecf3fdfc70>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: algorithm did not converge

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.3 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( aetpet pet )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.3_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.3_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.3 ~ 1 + aetpet + I(aetpet^2) + pet + I(pet^2)
## <environment: 0x7fecf7010f08>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.3_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.3 ~ 1 + aetpet + I(aetpet^2) + pet + I(pet^2)
## <environment: 0x7fecf2faba98>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: algorithm did not converge

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.4 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( aetpet stdp )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.4_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.4_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.4 ~ 1 + aetpet + I(aetpet^2) + stdp + I(stdp^2)
## <environment: 0x7fecf5119240>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.4_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.4 ~ 1 + aetpet + I(aetpet^2) + stdp + I(stdp^2)
## <environment: 0x7fecf5077e98>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.5 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( gdd p )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.5_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.5_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.5 ~ 1 + gdd + I(gdd^2) + p + I(p^2)
## <environment: 0x7fecf1eb8710>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.5_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.5 ~ 1 + gdd + I(gdd^2) + p + I(p^2)
## <environment: 0x7fecf7ff5e08>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.6 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( gdd pet )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.6_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.6_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.6 ~ 1 + gdd + I(gdd^2) + pet + I(pet^2)
## <environment: 0x7fecf55d4800>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.6_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.6 ~ 1 + gdd + I(gdd^2) + pet + I(pet^2)
## <environment: 0x7fecf54fa038>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: algorithm did not converge

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.7 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( gdd stdp )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.7_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.7_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.7 ~ 1 + gdd + I(gdd^2) + stdp + I(stdp^2)
## <environment: 0x7fecf52e4918>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.7_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.7 ~ 1 + gdd + I(gdd^2) + stdp + I(stdp^2)
## <environment: 0x7fecf4d21e28>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: algorithm did not converge

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.8 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( p pet )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.8_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.8_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.8 ~ 1 + p + I(p^2) + pet + I(pet^2)
## <environment: 0x7fecf52a7918>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.8_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.8 ~ 1 + p + I(p^2) + pet + I(pet^2)
## <environment: 0x7fecf757a000>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.9 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( p stdp )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.9_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.9_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.9 ~ 1 + p + I(p^2) + stdp + I(stdp^2)
## <environment: 0x7fecf2a69828>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.9_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.9 ~ 1 + p + I(p^2) + stdp + I(stdp^2)
## <environment: 0x7fecf7268428>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## Loading required library...
## 
## Checking Models arguments...
## 
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
## 
##  > Automatic weights creation to rise a 0.5 prevalence
## 
## 
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.10 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
## 
##  2  environmental variables ( pet stdp )
## Number of evaluation repetitions : 2
## Models selected : GLM RF 
## 
## Total number of model runs : 4 
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## 
## -=-=-=- Run :  ESM.BIOMOD.10_AllData 
## 
## 
## -=-=-=--=-=-=- ESM.BIOMOD.10_AllData_RUN1 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.10 ~ 1 + pet + I(pet^2) + stdp + I(stdp^2)
## <environment: 0x7fecf53a9c28>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
##  Evaluating Model stuff...
## 
## -=-=-=--=-=-=- ESM.BIOMOD.10_AllData_RUN2 
## 
## Model=GLM ( quadratic with no interaction )
##  No stepwise procedure
##  ! You might be confronted to models convergence issues !
##  selected formula : ESM.BIOMOD.10 ~ 1 + pet + I(pet^2) + stdp + I(stdp^2)
## <environment: 0x7fecf4622ee8>
## 
##  Model scaling...
##  Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
##  Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
##  Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
### Evaluation and average of simple bivariate models to ESMs
my.ESM_EF <- ecospat.ESM.EnsembleModeling(my.ESM,weighting.score=c("SomersD"),threshold=0)
### Projection of simple bivariate models into new space 
my.ESM_proj_current <- ecospat.ESM.Projection(ESM.modeling.output=my.ESM,
                                              new.env=current)
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.1_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.1_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.2_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.2_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.3_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.3_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.4_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.4_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.5_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.5_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.6_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.6_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.7_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.7_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.8_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.8_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.9_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.9_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
## 
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
## 
##      ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
##  > Projecting ESM.BIOMOD.10_AllData_RUN2_GLM ...
##  > Projecting ESM.BIOMOD.10_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
### Projection of calibrated ESMs into new space 
my.ESM_EFproj_current <- ecospat.ESM.EnsembleProjection(ESM.prediction.output=my.ESM_proj_current,
                                                        ESM.EnsembleModeling.output=my.ESM_EF)

Spatial prediction of communities

Input data for the first argument (proba) as data frame of rough probabilities from SDMs for all species in columns in the considered sites in rows.

proba <- ecospat.testData[,73:92]

Input data for the second argument (sr) as data frame with richness value in the first column and sites.

sr <- as.data.frame(rowSums(proba))

SESAM framework with ecospat.SESAM.prr()

ecospat.SESAM.prr(proba, sr)
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##     glm_Agrostis_capillaris glm_Leontodon_hispidus_sl
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##     glm_Dactylis_glomerata glm_Trifolium_repens_sstr
## 1                        1                         0
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##     glm_Geranium_sylvaticum glm_Ranunculus_acris_sl glm_Prunella_vulgaris
## 1                         0                       0                     0
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##     glm_Veronica_chamaedrys glm_Taraxacum_officinale_aggr
## 1                         0                             0
## 2                         1                             1
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## 4                         1                             1
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##     glm_Plantago_lanceolata glm_Potentilla_erecta glm_Carex_sempervirens
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##     glm_Soldanella_alpina glm_Cynosurus_cristatus
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##     glm_Campanula_scheuchzeri glm_Festuca_pratensis_sl
## 1                           0                        0
## 2                           0                        0
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##     glm_Pritzelago_alpina_sstr
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## 300                          0

Post-Modelling

Spatial Predictions of species assamblages

Co-occurrence analysis & Environmentally Constrained Null Models

Input data as a matrix of plots (rows) x species (columns). Input matrices should have column names (species names) and row names (sampling plots).

presence<-ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
pred<-ecospat.testData[c(73:92)]

Define the number of permutations. It is recomended to use at least 10000 permutatiobns for the test. As an example we used nperm = 100, to reduce the computational time.

nbpermut <- 100

Define the outpath

outpath <- getwd()

Run the function ecospat.cons_Cscore

The function tests for non-random patterns of species co-occurrence in a presence-absence matrix. It calculates the C-score index for the whole community and for each species pair. An environmental constraint is applied during the generation of the null communities.

ecospat.cons_Cscore(presence, pred, nbpermut, outpath)
## Computing observed co-occurence matrix 
## ............. 
## ............. 
## ............. 
## Computing permutations 
## ............. 
## ............. 
## .............