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validate_search = FALSE case of
cv_varsel() (with cv_method = "LOO"), the PSIS
weights are now calculated based on the reference model (they used to be
calculated based on the submodels which is incorrect). (GitHub:
#325)"mse", "rmse",
"acc" (= "pctcorr"), and "auc"
(i.e., all performance statistics except for "elpd" and
"mlpd").plot.vsel() and suggest_size() gain a new
argument thres_elpd. By default, this argument doesn’t have
any impact, but a non-NA value can be used for a customized
model size selection rule (see ?suggest_size for details).
(GitHub: #335)suggest_size() heuristic).seed and .seed are now
allowed to be NA for not calling set.seed()
internally at all.d_test of varsel() is not
considered as an internal feature anymore. This was possible after
fixing a bug for d_test (see below). (GitHub: #341)<vsel_object>$summaries and
<vsel_object>$d_test now corresponds to the order of
the observations in the original dataset if
<vsel_object> was created by a call to
cv_varsel([...], cv_method = "kfold") (formerly, in that
case, the observations in those sub-elements were ordered by fold).
Thereby, the order of the observations in those sub-elements now always
corresponds to the order of the observations in the original dataset,
except if <vsel_object> was created by a call to
varsel([...], d_test = <non-NULL_d_test_object>), in
which case the order of the observations in those sub-elements
corresponds to the order of the observations in
<non-NULL_d_test_object>. (GitHub: #341)search_terms caused the R
session to crash).validate_search = FALSE bug described above in
“Major changes”: The PSIS weights are now calculated based on the
reference model (they used to be calculated based on the submodels which
is incorrect). (GitHub: #325)\mbox{} commands displayed incorrectly in the HTML
help from R version 4.2.0 on. (GitHub: #326)plot.vsel() now draws the dashed red horizontal line
for the reference model (and—if present—the dotted black horizontal line
for the baseline model) first (i.e., before the submodel-specific
graphical elements), to avoid overplotting.d_test of varsel(): Not only
the predictive performance of the reference model needs to be
evaluated on the test data, but also the predictive performance of the
submodels. (GitHub: #341)cv_varsel() with LOO CV and
validate_search = FALSE instead of K-fold CV. (GitHub:
#305)search_terms of
varsel() and cv_varsel(). (GitHub: #155,
#308)NULL)
search_terms, method = NULL is internally
changed to method = "forward" and
method = "L1" throws a warning. This is done because
search_terms only takes effect in case of a forward search.
(GitHub: #155, #308)search_terms. This is necessary to prevent a bug described
below. (GitHub: #308)PIRLS loop resulted in NaN value errors automatically.
(GitHub: #314)b of projpred:::bootstrap() to
B.search_terms vector which excluded
the intercept in conjunction with refit_prj = FALSE (the
latter in project(), varsel(), or
cv_varsel()) led to incorrect submodels being fetched from
the search or to an error while doing so. This has been fixed now by
internally forcing the inclusion of the intercept in
search_terms. (GitHub: #308)solution_terms of
project() to fix a test failure in R versions >=
4.2.cv_varsel() with nloo < n where
n denotes the number of observations. (GitHub: #94, #252,
commit feea39e)validate_search = FALSE in cv_varsel().nclusters (=
1) and nclusters_pred (= 5) of
varsel() and cv_varsel() were set internally
(the user-visible defaults were NULL). Now,
nclusters and ndraws_pred (note the
ndraws_pred, not nclusters_pred) have
non-NULL user-visible defaults of 20 and
400, respectively. In general, this increases the runtime
of these functions a lot. With respect to cv_varsel(), the
new vignette (see vignettes) mentions
two ways to quickly obtain some rough preliminary results which in
general should not be used as final results, though: (i)
varsel() and (ii) cv_varsel() with
validate_search = FALSE (which only takes effect for
cv_method = "LOO"). (GitHub: #291 and several commits
beforehand, in particular bbd0f0a, babe031, 4ef95d3, and ce7d1e0)proj_linpred() and proj_predict(),
arguments nterms, ndraws, and
seed have been removed to allow the user to pass them to
project(). New arguments filter_nterms,
nresample_clusters, and .seed have been
introduced (see the documentation for details). (GitHub: #92, #135)proj_linpred(), dimensions are not
dropped anymore (i.e., output elements pred and
lpd are always S x N matrices now). (GitHub: #143)integrated = TRUE,
proj_linpred() now averages the LPD (across the projected
posterior draws) instead of taking the LPD at the averaged linear
predictors. (GitHub: #143)newdata does not contain the response variable,
proj_linpred() now returns NULL for output
element lpd. (GitHub: #143)stanreg (from
package rstanarm) with offsets to have these offsets
specified via an offset() term in the model formula (and
not via argument offset).NULL to a
user-visible value (and NULL is not allowed anymore).data of get_refmodel.stanreg()
has been removed. (GitHub: #219)div_minimizer of
init_refmodel() now always needs to return a
list of submodels (see the documentation for details).
Correspondingly, the function passed to argument
proj_predfun of init_refmodel() can now always
expect a list as input for argument fits (see
the documentation for details). (GitHub: #230)proj_predfun of
init_refmodel() now always needs to return a matrix (see
the documentation for details). (GitHub: #230)?`projpred-package`. (GitHub: #235)Student_t() family is regarded as
experimental. Therefore, a corresponding warning is thrown when creating
the reference model. (GitHub: #233, #252)Gamma() family is regarded as
experimental. Therefore, a corresponding warning is thrown when creating
the reference model. (GitHub: paul-buerkner/brms#1255, #240, #252)init_refmodel() in case of
argument dis being NULL (the default) was
dangerous for custom reference models with a family having
a dispersion parameter (in that case, dis values of
all-zeros were used silently). The new behavior now requires a
non-NULL argument dis in that case. (GitHub:
#254)cv_search has been renamed to
refit_prj. (GitHub: #154, #265)as.matrix.projection() has gained a new argument
nm_scheme which allows to choose the naming scheme for the
column names of the returned matrix. The default ("auto")
follows the naming scheme of the reference model fit (and uses the
"rstanarm" naming scheme if the reference model fit is of
an unknown class). (GitHub: #82, #279)seed (and .seed) arguments now have a
default of sample.int(.Machine$integer.max, 1) instead of
NULL. Furthermore, the value supplied to these arguments is
now used to generate new seeds internally on-the-fly. In many cases,
this will change results compared to older projpred
versions. Also note that now, the internal seeds are never fixed to a
specific value if seed (and .seed) arguments
are set to NULL. (GitHub: #84, #286)as.matrix.projection()
method now also returns the estimated group-level effects themselves.
(GitHub: #75)as.matrix.projection()
method now returns the variance components (population SD(s) and
population correlation(s)) instead of the empirical SD(s) of the
group-level effects. (GitHub: #74)README file. (GitHub: #245)nclusters_pred was removed. (GitHub: commit 5062f2f)project(): Warn if elements of
solution_terms are not found in the reference model (and
therefore ignored). (GitHub: #140)get_refmodel.default() now passes arguments via the
ellipsis (...) to init_refmodel(). (GitHub:
#153, commit dd3716e)init_refmodel(): The default (NULL) for
argument extract_model_data has been removed as it wasn’t
meaningful anyway. (GitHub: #219)folds of init_refmodel() has been
removed as it was effectively unused. (GitHub: #220)solution_terms(). This allowed
the introduction of a solution_terms.projection() method.
(GitHub: #223)predict.refmodel() now uses a default of
newdata = NULL. (GitHub: #223)weights of init_refmodel()’s
argument proj_predfun has been removed. (GitHub: #163,
#224)div_minimizer
functions have been unified into a single div_minimizer
which chooses an appropriate submodel fitter based on the formula of the
submodel, not based on that of the reference model. Furthermore, the
automatic handling of errors in the submodel fitters has been improved.
(GitHub: #230)plot.vsel(). (GitHub: #234,
#270)cvfun for
stanreg fits will now always use inner
parallelization in rstanarm::kfold.stanreg() (i.e., across
chains, not across CV folds), with getOption("mc.cores", 1)
cores. We do so on all systems (not only Windows). (GitHub: #249)fit of init_refmodel()’s argument
proj_predfun was renamed to fits. This is a
non-breaking change since all calls to proj_predfun in
projpred have that argument unnamed. However, this
cannot be guaranteed in the future, so we strongly encourage users with
a custom proj_predfun to rename argument fit
to fits. (GitHub: #263)init_refmodel() has gained argument
cvrefbuilder which may be a custom function for
constructing the K reference models in a K-fold CV. (GitHub: #271)project(),
varsel(), and cv_varsel() to the divergence
minimizer. (GitHub: #278)init_refmodel(), any contrasts
attributes of the dataset’s columns are silently removed. (GitHub:
#284)NAs in data supplied to newdata arguments
now trigger an error. (GitHub: #285)as.matrix.projection() (causing
incorrect column names for the returned matrix). (GitHub: #72, #73)vsel object. (GitHub: #79, #80)varsel(). (GitHub #90)nloo of
cv_varsel(). (GitHub: #93)cv_varsel(), causing an error in case of
!validate_search && cv_method != "LOO". (GitHub:
#95)proj_linpred() to raise an error if
argument newdata was NULL. (GitHub: #97)lpd in
proj_linpred() (for integrated = TRUE as well
as for integrated = FALSE). (GitHub: #105)proj_linpred()’s calculation of output
element lpd (for integrated = TRUE). (GitHub:
#106, #112)proj_linpred()’s output elements pred and
lpd (for integrated = FALSE): Now, they are
both S x N matrices, with S denoting the number of (possibly clustered)
posterior draws and N denoting the number of observations. (GitHub:
#107, #112)proj_predict()’s output matrix to
be transposed in case of nrow(newdata) == 1. (GitHub:
#112)proj_linpred(). (GitHub: #114)varsel()/make_formula
to fail with multidimensional interaction terms. (GitHub: #102,
#103)cv_varsel() for models with a
single predictor. (GitHub: #115)nterms of
proj_linpred() and proj_predict(). (GitHub:
#110)as.matrix.projection() in case of 1
(clustered) draw after projection. (GitHub: #130)subfit, make the column names of
as.matrix.projection()’s output matrix consistent with
other classes of submodels. (GitHub: #132)nterms_max of
plot.vsel() if there is just the intercept-only submodel.
(GitHub: #138)search_path in, e.g.,
varsel()’s output. (GitHub: #140)unused argument) when initializing the
K reference models in a K-fold CV with CV fits not of class
brmsfit or stanreg. (GitHub: #140)get_refmodel.default(), remove old defunct arguments
fetch_data, wobs, and offset.
(GitHub: #140)get_refmodel.stanreg(). (GitHub: #142,
#184)extract_model_data()’s
argument extract_y in get_refmodel.default().
(GitHub: #153, commit 39fece8)extract_model_data() in
K-fold CV. (GitHub: #153, commit 4f32195)proj_predfun() for GLMMs.
(GitHub: #174)proj_predfun() for
datafits. (GitHub: #177)summary.vsel()$selection for objects
of class vsel created by varsel(). (GitHub:
#179)search_terms are not
consecutive in size. (GitHub: commit 34e24de)cv_varsel()$pct_solution_terms_cv.
(GitHub: #188, commit e529ec1)glm_elnet() (the workhorse for L1 search),
causing the grid for lambda to be constructed without taking observation
weights into account. (GitHub: #198; note that the second part of #198
did not have any consequences for users)print.vsel() causing argument
digits to be ignored. (GitHub: #222)cv_search in
varsel() and cv_varsel() to be
TRUE for datafits, although it should be
FALSE in that case. (GitHub: #223)Error: Levels '<...>' of grouping factor '<...>' cannot be found in the fitted model. Consider setting argument 'allow_new_levels' to TRUE.)
when predicting from submodels which are GLMMs for newdata
containing new levels for grouping factors. (GitHub: #223)predict.refmodel(): Fix a bug for integer
ynew. (GitHub: #223)predict.refmodel(): Fix input checks for
offsetnew and weightsnew. (GitHub: #223)extract_model_data(), the weights
and offsets are now checked if they are of length 0 (and if yes, then
they are set to vectors of ones and zeros, respectively). This is
important for extract_model_data() functions which return
weights and offsets of length 0 (see, e.g., brms version
<= 2.16.1). (GitHub: #223)var (the predictive variances) and
regul (amount of ridge regularization) to the internal
submodel fitter for GLMs. (GitHub: #230)NAs,
an appropriate error is now thrown. Previously, the reference model was
created successfully, but this caused opaque errors in downstream code
such as project(). (GitHub: #274)We have fully rewritten the internals in several ways. Most importantly, we now leverage maximum likelihood estimation to third parties depending on the reference model’s family. This allows a lot of flexibility and extensibility for various models. Functionality wise, the major updates since the last release are:
search_terms that allows
the user to specify custom unit building blocks of the projections. New
vignette coming up.Better validation of function arguments.
Added print methods for vsel and cvsel objects. Added AUC statistics for binomial family. A few additional minor patches.
Removed the dependency on the rngtools package.
This version contains only a few patches, no new features to the user.
stan_glm(log(y) ~ log(x), ...), that is, it did not allow
transformation for y.refmodel-objects using the generic
get_refmodel-function, and all the functions use only this
object. This makes it much easier to use projpred with other reference
models by writing them a new get_refmodel-function. The
syntax is now changed so that varsel and
cv_varsel both return an object that has similar structure
always, and the reference model is stored into this object.plot/summary. Now it is possible to compare also to the
best submodel found, not only to the reference model.nloo = n by default in
cv_varsel. regul=1e-4 now by default in all
functions.cv_search argument for the main functions
(varsel,cv_varsel,project and the
prediction functions). Now it is possible to make predictions also with
those parameter estimates that were computed during the L1-penalized
search. This change also allows the user to compute the Lasso-solution
by providing the observed data as the ‘reference fit’ for init_refmodel.
An example will be added to the vignette.Until this version, we did not keep record of the changes between different versions. Started to do this from version 0.9.0 onwards.