varsel  R Documentation 
Run the search part and the evaluation part for a projection predictive variable selection. The search part determines the predictor ranking (also known as solution path), i.e., the best submodel for each submodel size (number of predictor terms). The evaluation part determines the predictive performance of the submodels along the predictor ranking.
varsel(object, ...)
## Default S3 method:
varsel(object, ...)
## S3 method for class 'vsel'
varsel(object, ...)
## S3 method for class 'refmodel'
varsel(
object,
d_test = NULL,
method = "forward",
ndraws = NULL,
nclusters = 20,
ndraws_pred = 400,
nclusters_pred = NULL,
refit_prj = !inherits(object, "datafit"),
nterms_max = NULL,
verbose = TRUE,
lambda_min_ratio = 1e05,
nlambda = 150,
thresh = 1e06,
regul = 1e04,
penalty = NULL,
search_terms = NULL,
seed = NA,
...
)
object 
An object of class 
... 
Arguments passed to 
d_test 
A 
method 
The method for the search part. Possible options are

ndraws 
Number of posterior draws used in the search part. Ignored if

nclusters 
Number of clusters of posterior draws used in the search
part. Ignored in case of L1 search (because L1 search always uses a single
cluster). For the meaning of 
ndraws_pred 
Only relevant if 
nclusters_pred 
Only relevant if 
refit_prj 
For the evaluation part, should the submodels along the
predictor ranking be fitted again ( 
nterms_max 
Maximum submodel size (number of predictor terms) up to
which the search is continued. If 
verbose 
A single logical value indicating whether to print out additional information during the computations. 
lambda_min_ratio 
Only relevant for L1 search. Ratio between the smallest and largest lambda in the L1penalized search. This parameter essentially determines how long the search is carried out, i.e., how large submodels are explored. No need to change this unless the program gives a warning about this. 
nlambda 
Only relevant for L1 search. Number of values in the lambda grid for L1penalized search. No need to change this unless the program gives a warning about this. 
thresh 
Only relevant for L1 search. Convergence threshold when computing the L1 path. Usually, there is no need to change this. 
regul 
A number giving the amount of ridge regularization when projecting onto (i.e., fitting) submodels which are GLMs. Usually there is no need for regularization, but sometimes we need to add some regularization to avoid numerical problems. 
penalty 
Only relevant for L1 search. A numeric vector determining the
relative penalties or costs for the predictors. A value of 
search_terms 
Only relevant for forward search. A custom character
vector of predictor term blocks to consider for the search. Section
"Details" below describes more precisely what "predictor term block" means.
The intercept ( 
seed 
Pseudorandom number generation (PRNG) seed by which the same
results can be obtained again if needed. Passed to argument 
Arguments ndraws
, nclusters
, nclusters_pred
, and ndraws_pred
are automatically truncated at the number of posterior draws in the
reference model (which is 1
for datafit
s). Using less draws or clusters
in ndraws
, nclusters
, nclusters_pred
, or ndraws_pred
than posterior
draws in the reference model may result in slightly inaccurate projection
performance. Increasing these arguments affects the computation time
linearly.
For argument method
, there are some restrictions: For a reference model
with multilevel or additive formula terms or a reference model set up for
the augmenteddata projection, only the forward search is available.
Furthermore, argument search_terms
requires a forward search to take
effect.
L1 search is faster than forward search, but forward search may be more accurate. Furthermore, forward search may find a sparser model with comparable performance to that found by L1 search, but it may also start overfitting when more predictors are added.
An L1 search may select an interaction term before all involved lowerorder interaction terms (including maineffect terms) have been selected. In projpred versions > 2.6.0, the resulting predictor ranking is automatically modified so that the lowerorder interaction terms come before this interaction term, but if this is conceptually undesired, choose the forward search instead.
The elements of the search_terms
character vector don't need to be
individual predictor terms. Instead, they can be building blocks consisting
of several predictor terms connected by the +
symbol. To understand how
these building blocks work, it is important to know how projpred's
forward search works: It starts with an empty vector chosen
which will
later contain already selected predictor terms. Then, the search iterates
over model sizes j \in \{0, ..., J\}
(with J
denoting the maximum submodel size, not counting the intercept). The
candidate models at model size j
are constructed from those elements
from search_terms
which yield model size j
when combined with the
chosen
predictor terms. Note that sometimes, there may be no candidate
models for model size j
. Also note that internally, search_terms
is
expanded to include the intercept ("1"
), so the first step of the search
(model size 0) always consists of the interceptonly model as the only
candidate.
As a search_terms
example, consider a reference model with formula y ~ x1 + x2 + x3
. Then, to ensure that x1
is always included in the
candidate models, specify search_terms = c("x1", "x1 + x2", "x1 + x3", "x1 + x2 + x3")
(or, in a simpler way that leads to the same results,
search_terms = c("x1", "x1 + x2", "x1 + x3")
, for which helper function
force_search_terms()
exists). This search would start with y ~ 1
as the
only candidate at model size 0. At model size 1, y ~ x1
would be the only
candidate. At model size 2, y ~ x1 + x2
and y ~ x1 + x3
would be the
two candidates. At the last model size of 3, y ~ x1 + x2 + x3
would be
the only candidate. As another example, to exclude x1
from the search,
specify search_terms = c("x2", "x3", "x2 + x3")
(or, in a simpler way
that leads to the same results, search_terms = c("x2", "x3")
).
An object of class vsel
. The elements of this object are not meant
to be accessed directly but instead via helper functions (see the main
vignette and projpredpackage).
d_test
If not NULL
, then d_test
needs to be a list
with the following
elements:
data
: a data.frame
containing the predictor variables for the test set.
offset
: a numeric vector containing the offset values for the test set
(if there is no offset, use a vector of zeros).
weights
: a numeric vector containing the observation weights for the test
set (if there are no observation weights, use a vector of ones).
y
: a vector or a factor
containing the response values for the test
set. In case of the latent projection, this has to be a vector containing the
latent response values, but it can also be a vector full of NA
s if
latentscale postprocessing is not needed.
y_oscale
: Only needs to be provided in case of the latent projection
where this needs to be a vector or a factor
containing the original
(i.e., nonlatent) response values for the test set.
cv_varsel()
# Data:
dat_gauss < data.frame(y = df_gaussian$y, df_gaussian$x)
# The "stanreg" fit which will be used as the reference model (with small
# values for `chains` and `iter`, but only for technical reasons in this
# example; this is not recommended in general):
fit < rstanarm::stan_glm(
y ~ X1 + X2 + X3 + X4 + X5, family = gaussian(), data = dat_gauss,
QR = TRUE, chains = 2, iter = 500, refresh = 0, seed = 9876
)
# Run varsel() (here without crossvalidation, with L1 search, and with small
# values for `nterms_max` and `nclusters_pred`, but only for the sake of
# speed in this example; this is not recommended in general):
vs < varsel(fit, method = "L1", nterms_max = 3, nclusters_pred = 10,
seed = 5555)
# Now see, for example, `?print.vsel`, `?plot.vsel`, `?suggest_size.vsel`,
# and `?ranking` for possible postprocessing functions.
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