Description Usage Arguments Value Examples
Perform the projection predictive variable selection for generalized linear models, generalized linear and additive multilevel models using generic reference models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  varsel(object, ...)
## Default S3 method:
varsel(object, ...)
## S3 method for class 'refmodel'
varsel(
object,
d_test = NULL,
method = NULL,
ndraws = NULL,
nclusters = NULL,
ndraws_pred = NULL,
nclusters_pred = NULL,
cv_search = TRUE,
nterms_max = NULL,
intercept = TRUE,
verbose = TRUE,
lambda_min_ratio = 1e05,
nlambda = 150,
thresh = 1e06,
regul = 1e04,
penalty = NULL,
search_terms = NULL,
...
)

object 
Either a 
... 
Additional arguments to be passed to the

d_test 
A test dataset, which is used to evaluate model performance. If not provided, training data is used. Currently this argument is for internal use only. 
method 
The method used in the variable selection. Possible options are

ndraws 
Number of posterior draws used in the variable selection. Cannot be larger than the number of draws in the reference model. Ignored if nclusters is set. 
nclusters 
Number of clusters to use in the clustered projection.
Overrides the 
ndraws_pred 
Number of projected draws used for prediction (after selection). Ignored if nclusters_pred is given. Note that setting less draws or clusters than posterior draws in the reference model may result in slightly inaccurate projection performance, although increasing this argument linearly affects the computation time. 
nclusters_pred 
Number of clusters used for prediction (after selection). Default is 5. 
cv_search 
If TRUE, then the projected coefficients after L1selection
are computed without any penalization (or using only the regularization
determined by 
nterms_max 
Maximum number of varibles until which the selection is continued. Defaults to min(20, D, floor(0.4*n)) where n is the number of observations and D the number of variables. 
intercept 
Whether to use intercept in the submodels. Defaults to TRUE. 
verbose 
If TRUE, may print out some information during the selection. Defaults to FALSE. 
lambda_min_ratio 
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 the default value unless the program gives a warning about this. 
nlambda 
Number of values in the lambda grid for L1penalized search. No need to change unless the program gives a warning about this. 
thresh 
Convergence threshold when computing L1path. Usually no need to change this. 
regul 
Amount of regularization in the projection. Usually there is no need for regularization, but sometimes for some models the projection can be illbehaved and we need to add some regularization to avoid numerical problems. 
penalty 
Vector determining the relative penalties or costs for the variables. Zero means that those variables have no cost and will therefore be selected first, whereas Inf means those variables will never be selected. Currently works only if method == 'L1'. By default 1 for each variable. 
search_terms 
A custom list of terms to evaluate for variable selection. By default considers all the terms in the reference model's formula. 
An object of type vsel
that contains information about the
feature selection. The fields are not meant to be accessed directly by
the user but instead via the helper functions (see the vignettes or type
?projpred to see the main functions in the package.)
1 2 3 4 5 6 7 8 9 10 11 12  if (requireNamespace('rstanarm', quietly=TRUE)) {
### Usage with stanreg objects
n < 30
d < 5
x < matrix(rnorm(n*d), nrow=n)
y < x[,1] + 0.5*rnorm(n)
data < data.frame(x,y)
fit < rstanarm::stan_glm(y ~ X1 + X2 + X3 + X4 + X5, gaussian(), data=data,
chains=2, iter=500)
vs < varsel(fit)
plot(vs)
}

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