Last updated on 2018-05-27 10:50:43 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.0-23 | 6.31 | 626.85 | 633.16 | WARN | |
| r-devel-linux-x86_64-debian-gcc | 2.0-23 | 4.75 | 491.27 | 496.02 | WARN | |
| r-devel-linux-x86_64-fedora-clang | 2.0-23 | 770.93 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0-23 | 725.67 | OK | |||
| r-devel-windows-ix86+x86_64 | 2.0-23 | 8.00 | 711.00 | 719.00 | OK | |
| r-patched-linux-x86_64 | 2.0-23 | 4.12 | 588.76 | 592.88 | OK | |
| r-patched-solaris-x86 | 2.0-23 | 1065.50 | ERROR | |||
| r-release-linux-x86_64 | 2.0-23 | 5.79 | 585.93 | 591.72 | OK | |
| r-release-windows-ix86+x86_64 | 2.0-23 | 12.00 | 846.00 | 858.00 | OK | |
| r-release-osx-x86_64 | 2.0-23 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 2.0-23 | 6.00 | 738.00 | 744.00 | OK | |
| r-oldrel-osx-x86_64 | 2.0-23 | NOTE |
Version: 2.0-23
Check: for unstated dependencies in ‘tests’
Result: WARN
'::' or ':::' import not declared from: ‘doMC’
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 2.0-23
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: ‘xgboost’
Flavor: r-patched-solaris-x86
Version: 2.0-23
Check: tests
Result: ERROR
Running ‘testthat.R’ [282s/300s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Super Learner
Version: 2.0-23
Package created on 2018-03-09
>
> test_check("SuperLearner")
── 1. Error: (unknown) (@test-XGBoost.R#2) ────────────────────────────────────
there is no package called 'xgboost'
1: library(xgboost) at testthat/test-XGBoost.R:2
2: stop(txt, domain = NA)
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0068):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 23.55
R-squared: 0.72
Signal-to-noise ratio: 2.58
Scale estimate (sigma): 4.853
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0024):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.65
R-squared: 0.51
Signal-to-noise ratio: 1.03
Prediction error: 0.128
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0238):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 24.01
R-squared: 0.72
Signal-to-noise ratio: 2.52
Scale estimate (sigma): 4.900
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.67
R-squared: 0.50
Signal-to-noise ratio: 0.98
Prediction error: 0.128
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.77675 0.00326959
SL.biglasso_All 23.67154 0.99673041
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2382946 0.02063139
SL.biglasso_All 0.1024175 0.97936861
Y
0 1
66 34
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
$params$ntree
[1] 100
[1] "SL.randomForest_1" "X" "Y"
[4] "create_rf" "data"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.050813 1
$grid
mtry
1 1
2 4
3 20
$names
[1] "SL.randomForest_1" "SL.randomForest_2" "SL.randomForest_3"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.05805154 0.2008891
SL.randomForest_2_All 0.04498379 0.6278342
SL.randomForest_3_All 0.04960304 0.1712766
$grid
alpha
1 0.00
2 0.25
3 0.50
4 0.75
5 1.00
$names
[1] "SL.glmnet_0" "SL.glmnet_0.25" "SL.glmnet_0.5" "SL.glmnet_0.75"
[5] "SL.glmnet_1"
$base_learner
[1] "SL.glmnet"
$params
list()
[1] "SL.glmnet_0" "SL.glmnet_0.25" "SL.glmnet_0.5" "SL.glmnet_0.75"
[5] "SL.glmnet_1"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = ls(learners),
cvControl = list(V = 2), env = learners)
Risk Coef
SL.glmnet_0_All 0.04764882 0.5990378
SL.glmnet_0.25_All 0.05046096 0.0000000
SL.glmnet_0.5_All 0.04818493 0.4009622
SL.glmnet_0.75_All 0.05707002 0.0000000
SL.glmnet_1_All 0.06232678 0.0000000
Call:
SuperLearner(Y = Y, X = X_clean, family = binomial(), SL.library = c("SL.mean",
svm$names), cvControl = list(V = 3))
Risk Coef
SL.mean_All 0.2355795 0.1272125
SL.svm_polynomial_All 0.1746844 0.0000000
SL.svm_radial_All 0.1662527 0.1505339
SL.svm_sigmoid_All 0.1588301 0.7222536
Length Class Mode
call 24 -none- call
first.sigma 100 -none- numeric
sigma 1000 -none- numeric
sigest 1 -none- numeric
yhat.train 30000 -none- numeric
yhat.train.mean 30 -none- numeric
yhat.test 30000 -none- numeric
yhat.test.mean 30 -none- numeric
varcount 13000 -none- numeric
y 30 -none- numeric
Length Class Mode
call 24 -none- call
yhat.train 30000 -none- numeric
yhat.test 30000 -none- numeric
varcount 13000 -none- numeric
binaryOffset 30 -none- numeric
Length Class Mode
call 24 -none- call
first.sigma 100 -none- numeric
sigma 1000 -none- numeric
sigest 1 -none- numeric
yhat.train 30000 -none- numeric
yhat.train.mean 30 -none- numeric
yhat.test 30000 -none- numeric
yhat.test.mean 30 -none- numeric
varcount 13000 -none- numeric
y 30 -none- numeric
Length Class Mode
call 24 -none- call
yhat.train 30000 -none- numeric
yhat.test 30000 -none- numeric
varcount 13000 -none- numeric
binaryOffset 30 -none- numeric
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.dbarts", "SL.bartMachine"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 39.02547 0.0078632
SL.dbarts_All 25.97348 0.9921368
SL.bartMachine_All 36.05066 0.0000000
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.dbarts", "SL.bartMachine"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.3288889 0
SL.dbarts_All 0.1980883 1
SL.bartMachine_All 0.2282789 0
ExtraTrees:
- # of trees: 500
- node size: 5
- # of dim: 13
- # of tries: 4
- type: numeric (regression)
- multi-task: no
ExtraTrees:
- # of trees: 500
- node size: 1
- # of dim: 13
- # of tries: 3
- type: factor (classification)
- multi-task: no
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = sl_lib,
cvControl = list(V = 2))
Risk Coef
SL.extraTrees_All 0.1038481 0.5531783
SL.ranger_All 0.1047875 0.4468217
SL.mean_All 0.2436000 0.0000000
$grid
NULL
$names
[1] "SL.extraTrees_1"
$base_learner
[1] "SL.extraTrees"
$params
list()
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
lib$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2436000 0
SL.extraTrees_1_All 0.1124902 1
SL.extraTrees_1 <- function(...) SL.extraTrees(...)
$grid
NULL
$names
[1] "SL.extraTrees_1"
$base_learner
[1] "SL.extraTrees"
$params
list()
[1] "SL.extraTrees_1"
[1] 1
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
lib$names), cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.mean_All 0.2436000 0
SL.extraTrees_1_All 0.1130368 1
$grid
mtry
1 1
2 2
$names
[1] "SL.extraTrees_1" "SL.extraTrees_2"
$base_learner
[1] "SL.extraTrees"
$params
list()
[1] "SL.extraTrees_1" "SL.extraTrees_2"
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
lib$names), cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.mean_All 0.2436000 0
SL.extraTrees_1_All 0.1133418 0
SL.extraTrees_2_All 0.1059143 1
$grid
mtry
1 1
2 2
$names
[1] "SL.extraTrees_1" "SL.extraTrees_2"
$base_learner
[1] "SL.extraTrees"
$params
list()
[1] "SL.extraTrees_1" "SL.extraTrees_2"
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
lib$names), cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.mean_All 0.2544000 0
SL.extraTrees_1_All 0.1450968 0
SL.extraTrees_2_All 0.1313301 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Deviance Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9747 -0.4250 -0.0911 0.2720 3.5929
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Deviance Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9747 -0.4250 -0.0911 0.2720 3.5929
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.83418 0.01409163
SL.glm_All 23.62860 0.98590837
V1
Min. :-3.903
1st Qu.:17.517
Median :22.124
Mean :22.533
3rd Qu.:27.341
Max. :44.361
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23508982 0.009634095
SL.glm_All 0.09334622 0.990365905
V1
Min. :0.003619
1st Qu.:0.034209
Median :0.195582
Mean :0.375494
3rd Qu.:0.783137
Max. :0.993512
Got an error, as expected.
<simpleError in cbind2(1, newx) %*% nbeta: Cholmod error 'X and/or Y have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90>
Got an error, as expected.
<simpleError in cbind2(1, newx) %*% nbeta: Cholmod error 'X and/or Y have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90>
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.83418 0.01409163
SL.lm_All 23.62860 0.98590837
V1
Min. :-3.903
1st Qu.:17.517
Median :22.124
Mean :22.533
3rd Qu.:27.341
Max. :44.361
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2350898 0
SL.lm_All 0.1115445 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2417014 0.02808554
SL.glmnet_All 0.1759297 0.97191446
SL.mean_All 0.2666500 0.00000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.1730096 0.35627490
SL.glmnet_All 0.1647030 0.61741243
SL.mean_All 0.2504500 0.02631267
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.1959772 0.4596517
SL.glmnet_All 0.1943974 0.4326680
SL.mean_All 0.2558500 0.1076803
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0.06137199
SL.glmnet_All 0.4907020 0.93862801
SL.mean_All 0.6964542 0.00000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4877618 1
SL.mean_All 0.6928473 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.1851302 0.42598535
SL.glmnet_All 0.1768747 0.54102157
SL.mean_All 0.2558500 0.03299307
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 244.2850 0.2339504
SL.glmnet_All 205.9486 0.7660496
SL.mean_All 284.3800 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 259.5446 0.1345917
SL.glmnet_All 198.5286 0.7396111
SL.mean_All 277.1389 0.1257971
SL.bad_algorithm_All NA 0.0000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2332599 0.4562568
SL.glmnet_All 0.1828646 0.2982123
SL.mean_All 0.5250225 0.2455309
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2827044 0.3333333
SL.glmnet_All 0.2284056 0.3333333
SL.mean_All 0.5150135 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Y
0 1
68 32
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = sl_lib, cvControl = list(V = 2))
Risk Coef
SL.randomForest_All 0.08543132 1
SL.mean_All 0.24760000 0
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_All 0.1237247 1
SL.randomForest_1 <- function(...) SL.randomForest(...)
$grid
NULL
$names
[1] "SL.randomForest_1"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1"
[1] 1
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.1286959 1
$grid
mtry
1 1
2 2
$names
[1] "SL.randomForest_1" "SL.randomForest_2"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1" "SL.randomForest_2"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.07415690 0.3566581
SL.randomForest_2_All 0.06937001 0.6433419
$grid
mtry
1 1
2 2
$names
[1] "SL.randomForest_1" "SL.randomForest_2"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1" "SL.randomForest_2"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_All 0.05962605 0.1247237
SL.randomForest_2_All 0.05350255 0.8752763
$grid
mtry nodesize maxnodes
1 1 NULL NULL
2 2 NULL NULL
$names
[1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
$base_learner
[1] "SL.randomForest"
$params
list()
[1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_NULL_NULL_All 0.06451327 0.6179109
SL.randomForest_2_NULL_NULL_All 0.05916622 0.3820891
$grid
mtry maxnodes
1 1 5
2 2 5
3 1 10
4 2 10
5 1 NULL
6 2 NULL
$names
[1] "SL.randomForest_1_5" "SL.randomForest_2_5" "SL.randomForest_1_10"
[4] "SL.randomForest_2_10" "SL.randomForest_1_NULL" "SL.randomForest_2_NULL"
$base_learner
[1] "SL.randomForest"
$params
list()
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2), env = sl_env)
Risk Coef
SL.randomForest_1_5_All 0.08783373 0
SL.randomForest_2_5_All 0.08487843 0
SL.randomForest_1_10_All 0.08546784 0
SL.randomForest_2_10_All 0.07830362 1
SL.randomForest_1_NULL_All 0.08360061 0
SL.randomForest_2_NULL_All 0.08112253 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_5_All 0.06490989 0
SL.randomForest_2_5_All 0.05400114 0
SL.randomForest_1_10_All 0.06018865 0
SL.randomForest_2_10_All 0.05188455 0
SL.randomForest_1_NULL_All 0.05936604 0
SL.randomForest_2_NULL_All 0.05173980 1
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,
cvControl = list(V = 2))
Risk Coef
SL.randomForest_1_5_All 0.06896458 0
SL.randomForest_2_5_All 0.06124560 1
SL.randomForest_1_10_All 0.06885161 0
SL.randomForest_2_10_All 0.06549691 0
SL.randomForest_1_NULL_All 0.06775305 0
SL.randomForest_2_NULL_All 0.06500317 0
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
OOB prediction error (MSE): 10.54595
R squared (OOB): 0.8753238
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
OOB prediction error: 0.08262419
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
OOB prediction error (MSE): 10.45612
R squared (OOB): 0.8763858
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
OOB prediction error: 0.08395011
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.28e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.09e-03 **
zn 4.642e-02 0.013728 3.3816 7.78e-04 ***
indus 2.056e-02 0.061496 0.3343 7.38e-01
chas 2.687e+00 0.861580 3.1184 1.93e-03 **
nox -1.777e+01 3.819744 -4.6513 4.25e-06 ***
rm 3.810e+00 0.417925 9.1161 1.98e-18 ***
age 6.922e-04 0.013210 0.0524 9.58e-01
dis -1.476e+00 0.199455 -7.3980 6.01e-13 ***
rad 3.060e-01 0.066346 4.6129 5.07e-06 ***
tax -1.233e-02 0.003760 -3.2800 1.11e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.31e-12 ***
black 9.312e-03 0.002686 3.4668 5.73e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.78e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.45e-03 **
crim -0.040649 0.049796 -0.8163 4.14e-01
zn 0.012134 0.010678 1.1364 2.56e-01
indus -0.040715 0.045615 -0.8926 3.72e-01
chas 0.248209 0.653283 0.3799 7.04e-01
nox -3.601085 2.924365 -1.2314 2.18e-01
rm 1.155156 0.374843 3.0817 2.06e-03 **
age -0.018661 0.009319 -2.0023 4.53e-02 *
dis -0.518934 0.146286 -3.5474 3.89e-04 ***
rad 0.255522 0.061391 4.1622 3.15e-05 ***
tax -0.009500 0.003107 -3.0574 2.23e-03 **
ptratio -0.409317 0.103191 -3.9666 7.29e-05 ***
black -0.001452 0.002558 -0.5674 5.70e-01
lstat -0.318436 0.054735 -5.8178 5.96e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.28e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.09e-03 **
zn 4.642e-02 0.013728 3.3816 7.78e-04 ***
indus 2.056e-02 0.061496 0.3343 7.38e-01
chas 2.687e+00 0.861580 3.1184 1.93e-03 **
nox -1.777e+01 3.819744 -4.6513 4.25e-06 ***
rm 3.810e+00 0.417925 9.1161 1.98e-18 ***
age 6.922e-04 0.013210 0.0524 9.58e-01
dis -1.476e+00 0.199455 -7.3980 6.01e-13 ***
rad 3.060e-01 0.066346 4.6129 5.07e-06 ***
tax -1.233e-02 0.003760 -3.2800 1.11e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.31e-12 ***
black 9.312e-03 0.002686 3.4668 5.73e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.78e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.45e-03 **
crim -0.040649 0.049796 -0.8163 4.14e-01
zn 0.012134 0.010678 1.1364 2.56e-01
indus -0.040715 0.045615 -0.8926 3.72e-01
chas 0.248209 0.653283 0.3799 7.04e-01
nox -3.601085 2.924365 -1.2314 2.18e-01
rm 1.155156 0.374843 3.0817 2.06e-03 **
age -0.018661 0.009319 -2.0023 4.53e-02 *
dis -0.518934 0.146286 -3.5474 3.89e-04 ***
rad 0.255522 0.061391 4.1622 3.15e-05 ***
tax -0.009500 0.003107 -3.0574 2.23e-03 **
ptratio -0.409317 0.103191 -3.9666 7.29e-05 ***
black -0.001452 0.002558 -0.5674 5.70e-01
lstat -0.318436 0.054735 -5.8178 5.96e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
══ testthat results ═══════════════════════════════════════════════════════════
OK: 89 SKIPPED: 0 FAILED: 1
1. Error: (unknown) (@test-XGBoost.R#2)
Error: testthat unit tests failed
Execution halted
Flavor: r-patched-solaris-x86
Version: 2.0-23
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking: ‘genefilter’ ‘sva’
Flavor: r-oldrel-osx-x86_64