Added DOI for JSS 2023 paper and corrected some typos in documentation (nfold
-> nfolds
) and vignette.
Removed unneeded legacy fortran code, leaving only coxnet. Fixed up Matrix as() sequences
Relatively minor changes to bugs in survival functions and bigGlm, and some improved failure messages.
Most of the Fortran code has been replaced by C++ by James Yang, leading to speedups in all cases. The exception is the Cox routine for right censored data, which is still under development.
Some of the Fortran in glmnet has been replaced by C++, written by the newest member of our team, James Yang. * the wls
routines (dense and sparse), that are the engines under the glmnet.path
function when we use programmable families, are now written in C++, and lead to speedups of around 8x. * the family of elnet routines (sparse/dense, covariance/naive) for glmnet(...,family="gaussian")
are all in C++, and lead to speedups around 4x.
A new feature added, as well as some minor fixes to documentation. * The exclude argument has come to life. Users can now pass a function that can take arguments x, y and weights, or a subset of these, for filtering variables. Details in documentation and vignette. * Prediction with single newx
observation failed before. This is fixed. * Labeling of predictions from cv.glmnet
improved. * Fixed a bug in mortran/fortran that caused program to loop ad infinitum
Fixed some bugs in the coxpath function to do with sparse X. * when some penalty factors are zero, and X is sparse, we should not call GLM to get the start * apply does not work as intended with sparse X, so we now use matrix multiplies instead in computing lambda_max * added documentation for cv.glmnet
to explain implications of supplying lambda
Expanded scope for the Cox model. * We now allow (start, stop) data in addition to the original right-censored all start at zero option. * Allow for strata as in survival::coxph
* Allow for sparse X matrix with Cox models (was not available before) * Provide method for survival::survfit
Vignettes are revised and reorganized. Additional index information stored on cv.glmnet
objects, and included when printed.
concordance
function from package survival
Major revision with added functionality. Any GLM family can be used now with glmnet
, not just the built-in families. By passing a “family” object as the family argument (rather than a character string), one gets access to all families supported by glm
. This development was programmed by our newest member of the glmnet
team, Kenneth Tay.
Bug fixes
Intercept=FALSE
with “Gaussian” is fixed. The dev.ratio
comes out correctly now. The mortran code was changed directly in 4 places. look for “standard”. Thanks to Kenneth Tay.Bug fixes
confusion.glmnet
was sometimes not returning a list because of apply collapsing structurecv.mrelnet
and cv.multnet
dropping dimensions inappropriatelystorePB
to avoid segfault. Thanks Tomas Kalibera!assess.glmnet
and cousins to be more helpful!lambda.interp
to avoid edge cases (thanks David Keplinger)This is a major revision with much added functionality, listed roughly in order of importance. An additional vignette called relax
is supplied to describe the usage.
relax
argument added to glmnet
. This causes the models in the path to be refit without regularization. The resulting object inherits from class glmnet
, and has an additional component, itself a glmnet object, which is the relaxed fit.relax
argument to cv.glmnet
. This allows selection from a mixture of the relaxed fit and the regular fit. The mixture is governed by an argument gamma
with a default of 5 values between 0 and 1.predict
, coef
and plot
methods for relaxed
and cv.relaxed
objects.print
method for relaxed
object, and new print
methods for cv.glmnet
and cv.relaxed
objects.trace.it=TRUE
to glmnet
and cv.glmnet
. This can also be set for the session via glmnet.control
.assess.glmnet
, roc.glmnet
and confusion.glmnet
for displaying the performance of models.makeX
for building the x
matrix for input to glmnet
. Main functionality is one-hot-encoding of factor variables, treatment of NA
and creating sparse inputs.bigGlm
for fitting the GLMs of glmnet
unpenalized.In addition to these new features, some of the code in glmnet
has been tidied up, especially related to CV.
coxnet.deviance
to do with input pred
, as well as saturated loglike
(missing) and weightscoxgrad
function for computing the gradientinst/mortran
-Wall
warningsnewoffset
created problems all over - fixed theseexact=TRUE
calls to coef
and predict
. See help file for more detailsy
blows up elnet
; error trap includedlambda.interp
which was returning NaN
under degenerate circumstances.predict
and coef
with exact=TRUE
. The user is strongly encouraged to supply the original x
and y
values, as well as any other data such as weights that were used in the original fit.predict.glmnet
, predict.multnet
and predict.coxnet
, when s=
argument is used with a vector of values. It was not doing the matrix multiply correctlyglmnet.control
for setting systems parameterscoxnet
mgaussian
family for multivariate responsegrouped
option for multinomial familynewx
and make dgCmatrix
if sparselognet
added a classnames component to the objectpredict.lognet(type="class")
now returns a character vector/matrixpredict.glmnet
: fixed bug with type="nonzero"
glmnet
: Now x can inherit from sparseMatrix
rather than the very specific dgCMatrix
, and this will trigger sparse mode for glmnetglmnet.Rd
(lambda.min
) : changed value to 0.01 if nobs < nvars
, (lambda
) added warnings to avoid single value, (lambda.min
): renamed it lambda.min.ratio
glmnet
(lambda.min
) : changed value to 0.01 if nobs < nvars
(HessianExact
) : changed the sense (it was wrong), (lambda.min
): renamed it lambda.min.ratio
. This allows it to be called lambda.min
in a call thoughpredict.cv.glmnet
(new function) : makes predictions directly from the saved glmnet
object on the cv objectcoef.cv.glmnet
(new function) : as abovepredict.cv.glmnet.Rd
: help functions for the abovecv.glmnet
: insert drop(y)
to avoid 1 column matrices; now include a glmnet.fit
object for later predictionsnonzeroCoef
: added a special case for a single variable in x
; it was dying on thisdeviance.glmnet
: includeddeviance.glmnet.Rd
: included