NEWS | R Documentation |
optPenalty.LOOCV
is deprecated. Please use optPenalty.kCV
instead
optPenalty.LOOCVauto
is deprecated. Please use optPenalty.kCVauto
instead
Updated CITATION
file
Updated README
file
sparsify
now has an additional thresholding option: 'connected'
Updated CITATION
file
Updated README
file
Fixed bug in Ugraph
:
Incorrectly stated before that all igraph layouts were supported.
Now they indeed are supported.
conditionNumberPlot
is deprecated. Please use CNplot
instead
Features of the CNplot
function (above and beyond conditionNumberPlot
):
The digitLoss
and rlDist
arguments have been removed
These arguments have been replaced with the logical argument Iaids
Iaids = TRUE
amends the basic condition number plot with interpretational aids
These aids are the approximate loss in digits of accuracy and and approximation of the acceleration along the regularization path of the condition number
Argument main
is now a character argument
Argument value
now by default takes the value 1e-100 (convenient)
Now uses C++ functionalty for additional speed
edgeHeat
now has aligned x-axis labels
The visualizations of the optPenalty.LOOCV
and optPenalty.aLOOCV
functions
now will no longer produce horizontal and/or vertical lines that fall outside the boundaries
of the figure
optPenalty.LOOCV
now uses log-equidistant penalty grid for optimal penalty
parameter determination (this also enhances the visualization)
New features updated optPenalty.aLOOCV
function:
Function has been sped up by killing redundant inversion
now uses log-equidistant penalty grid for optimal penalty parameter determination (this also enhances the visualization)
New features updated Ugraph
function:
One can now also specify vertex placement by coordinate specification
Now outputs, for convenience, the vertex coordinates of the plotted graph
ridgePathS
has been sped up by killing redundant inversion
The covML
function has been amended with an argument that indicates if a correlation matrix
(instead of an ML estimate of a covariance matrix) is desired. This offers more flexibility. One can now get the
ML estimate of the covariance matrix, the ML estimate of the covariance matrix on standardized data, as well as
the correlation matrix
The optPenalty.LOOCVauto
function has been amended with an argument that indicates if the
evaluation of the LOOCV score should be performed on the correlation scale
The optPenalty.LOOCV
function has been amended with an argument that indicates if the
evaluation of the LOOCV score should be performed on the correlation scale
The optPenalty.aLOOCV
function has been amended with an argument that indicates if the
evaluation of the approximate LOOCV score should be performed on the correlation scale
Added this NEWS
file!
Updated (and corrected) CITATION
file
Added README
file
Added (selective) import statements for default packages as required for R-devel
rags2ridges now uses Rcpp and RcppArmadillo with
core functions written in C++
. The package should now be at least
two orders of magnitude faster in most cases.
Added, next to the core module, the fused ridge module.
The fused module provides functionality
for the estimation and graphical modeling of multiple precision matrices from
multiple high-dimensional data classes.
Functions from this module are generally suffixed with .fused
.
Functions tied to (or added with) this module are:
isSymmetricPD
isSymmetricPSD
is.Xlist
default.target.fused
createS
getKEGGPathway
kegg.target
pooledS
pooledP
KLdiv.fused
ridgeP.fused
optPenalty.fused.grid
print.optPenaltyFusedGrid
plot.optPenaltyFusedGrid
optPenalty.fused.auto
optPenalty.fused
default.penalty
fused.test
print.ptest
summary.ptest
hist.ptest
plot.ptest
sparsify.fused
GGMnetworkStats.fused
GGMpathStats.fused
The following functions were added to the core module:
covMLknown
GGMmutualInfo
Added miscellaneous (hidden) functions.
Fixed bugs in GGMpathstats
:
Code no longer breaks down if variable names are absent.
Now properly handles singleton pathsets.
Fixed bug in sparsify
: Now always returns symmetric objects
Argument verticle
as used in various functions has been renamed to
vertical
. Sorry for any inconvenience.
Internal usage of ridgeS
replaced by the faster C++-dependent counterpart
ridgeP
New features updated conditionNumberPlot
function:
Function has been sped up
Now uses log-equidistant grid for visualization
Now includes option to additionally plot the approximate loss in digits of accuracy
ridgeS
is deprecated. Please use ridgeP
instead
Future versions of rags2ridges will be subject to changes in naming conventions
Inclusion hidden function .pathContribution
for usage in
GGMpathStats
function
Inclusion hidden function .path2string
for usage in
GGMpathStats
function
Inclusion hidden function .pathAndStats
for usage in
GGMpathStats
function
Inclusion hidden function .cvl
for usage in
optPenalty.LOOCVauto
function
Inclusion hidden function .lambdaNullDist
for usage in
GGMblockNullPenalty
function
Inclusion hidden function .blockTestStat
for usage in
GGMblockTest
function
Inclusion function that expresses the covariance between a pair of
variables as a sum of path weights: GGMpathStats
Inclusion function that determines the optimal penalty parameter
value by application of the Brent algorithm to the LOOCV log-likelihood:
optPenalty.LOOCVauto
Inclusion function that generates the distribution of the penalty
parameter under the null hypothesis of block independence:
GGMblockNullPenalty
Inclusion function that performs a permutation test for block
structure in the precision matrix: GGMblockTest
Inclusion wrapper function: fullMontyS
Corrected small error in evaluateSfit
function.
The dir
argument was not properly used previously.
New features updated optPenalty.aLOOCV
function:
For scalar matrix targets the complete solution path depends on only
1 eigendecomposition and 1 matrix inversion.
Meaning: the function is sped up somewhat by lifting redundant
inversions out of for
loops.
Optional graph now plots the approximated LOOCV negative log-likelihood instead of ln(approximated LOOCV negative log-likelihood).
Legend in optional graph has been adapated accordingly.
New features updated optPenalty.LOOCV
function:
Optional graph now plots the LOOCV negative log-likelihood instead of ln(LOOCV negative log-likelihood).
Legend in optional graph has been adapated accordingly.
New features updated default.target
function:
Inclusion new default target option: type = DIAES
. Gives diagonal
matrix with inverse of average of eigenvalues of S as entries.
New features updated GGMnetworkStats
function:
Now also assesses (and returns a logical) if graph/network is chordal.
Now also includes assesment of the eigenvalue centrality.
Now includes option to have list or table output.
New features updated ridgePathS
function:
Sped up considerably for rotation equivariant alternative estimator. By avoidance of redundant eigendecompositions and inversions.
Now catches breakdown due to rounding preculiarities when
plotType = "pcor"
.
New features updated sparsify
function:
Inclusion new thresholding function top
: retainment of top elements
based on absolute partial correlation.
Inclusion output option: When output = "light"
, only the (matrix)
positions of the zero and non-zero elements are returned.
Function no longer dependent on GeneNet; now makes direct use of fdrtool.
Function now also prints some general information on the number of edges retained.
Inclusion hidden function .ridgeSi
for usage in
conditionNumberPlot
function.
Inclusion hidden function .eigShrink
for usage in (a.o.)
ridgeS
function.
Inclusion function calculating various network statistics from a
sparse matrix: GGMnetworkStats
Inclusion function for visual inspection fit of regularized precision
matrix to sample covariance matrix: evaluateSfit
Inclusion function for visualization of regularization paths:
ridgePathS
Inclusion function for default target matrix generation:
default.target
New features updated evaluateS
function:
The printed output of the evaluateS
function is now aligned
Calculation spectral condition number has been improved
conditionNumber
function now called conditionNumberPlot
.
The updated function has new features:
Main plot can now be obtained with either the spectral (l2) or the (approximation to) l1 condition number
Main plot can now be amended with plot of the relative distance to the set of singular matrices
The title of the main plot can now be suppressed
One can now obtain numeric output from the function: lambdas and condition numbers
New features updated sparsify
function:
Changed type = c("threshold", "localFDR")
to
threshold = c("absValue", "localFDR")
(clarifying nomenclature)
Changed threshold
to absValueCut
(clarifying nomenclature)
Will now output both sparsified partial correlation/standardized precision matrix and the sparsified precison matrix, when input consists of the unstandardized precision matrix
New features updated ridgeS
function:
Contains an improved evaluation of the target matrix possibly being a null matrix
Now evaluates if a rotation equivariant alternative estimator ensues for a given target matrix
When rotation equivariant alternative estimator ensues, computation is sped up considerably by circumventing the matrix square root
optPenaltyCV
function now called optPenalty.LOOCV
, for sake
of (naming) consistency. The updated function has new features:
targetScale
option has been removed
Replaced log
in optional graph by ln
Visual layout of optional graph now more in line with recommendations by Tufte (regarding data-ink ratio)
New features updated optPenalty.aLOOCV
function:
Replaced log
in optional graph by ln
Visual layout of optional graph now more in line with recommendations by Tufte (regarding data-ink ratio)
Computation optimal penalty in conditionNumberPlot
,
optPenalty.aLOOCV
and optPenalty.LOOCV
functions sped up
considerably for rotation equivariant alternative estimator.
By usage new ridgeS and avoidance of redundant eigendecompositions
Default target in ridgeS
, conditionNumberPlot
,
optPenalty.aLOOCV
and optPenalty.LOOCV
now "DAIE"
option from default.target
Inclusion function for ML estimation of the sample covariance matrix:
covML
Inclusion function for approximate leave-one-out cross-validation:
optPenalty.aLOOCV
Inclusion function conditionNumber
to visualize the
spectral condition number over the regularization path
Inclusion function evaluateS
to evaluate basic
properties of a covariance matrix
Inclusion function KLdiv
that calculates the
Kullback-Leibler divergence between two normal distributions
Inclusion option to suppress on-screen output in sparsify
function
Corrected small error in optPenaltyCV
function
Both optPenaltyCV
and optPenalty.aLOOCV
now utilize
covML
instead of cov
Default output option in optPenaltyCV
(as in
optPenalty.aLOOCV
) is now light