Package index • projpred

All functions

as.matrix(<projection>): Extract projected parameter draws and coerce to matrix

as_draws_matrix(<projection>) as_draws(<projection>): Extract projected parameter draws and coerce to draws_matrix (see package posterior)

augdat_ilink_binom(): Inverse-link function for augmented-data projection with binomial family

augdat_link_binom(): Link function for augmented-data projection with binomial family

break_up_matrix_term(): Break up matrix terms

cl_agg(): Weighted averaging within clusters of parameter draws

cv_folds() cvfolds() cv_ids(): Create cross-validation folds

cv_proportions(): Ranking proportions from fold-wise predictor rankings

cv_varsel(): Run search and performance evaluation with cross-validation

df_binom: Binomial toy example

df_gaussian: Gaussian toy example

extend_family(): Extend a family

Student_t(): Extra family objects

force_search_terms(): Force search terms

mesquite: Mesquite data set

performances(): Predictive performance results

plot(<cv_proportions>) plot(<ranking>): Plot ranking proportions from fold-wise predictor rankings

plot(<vsel>): Plot predictive performance

proj_linpred() proj_predict(): Predictions from a submodel (after projection)

predict(<refmodel>): Predictions or log posterior predictive densities from a reference model

predictor_terms(): Predictor terms used in a project() run

print(<projection>): Print information about project() output

print(<refmodel>): Print information about a reference model object

print(<vsel>): Print results (summary) of a varsel() or cv_varsel() run

print(<vselsummary>): Print summary of a varsel() or cv_varsel() run

project(): Projection onto submodel(s)

projpred projpred-package: Projection predictive feature selection

ranking(): Predictor ranking(s)

get_refmodel() init_refmodel(): Reference model and more general information

run_cvfun(): Create cvfits from cvfun

solution_terms(): Retrieve the full-data solution path from a varsel() or cv_varsel() run or the predictor combination from a project() run

suggest_size(): Suggest submodel size

summary(<vsel>): Summary of a varsel() or cv_varsel() run

varsel(): Run search and performance evaluation without cross-validation

y_wobs_offs(): Extract response values, observation weights, and offsets