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Run search and performance evaluation with cross-validation

Source: R/cv_varsel.R
cv_varsel.Rd

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. In contrast to varsel(), cv_varsel() performs a cross-validation (CV) by running the search part with the training data of each CV fold separately (an exception is explained in section "Note" below) and by running the evaluation part on the corresponding test set of each CV fold. A special method is cv_varsel.vsel() because it re-uses the search results from an earlier cv_varsel() (or varsel()) run, as illustrated in the main vignette.

Usage

cv_varsel(object, ...)

# Default S3 method
cv_varsel(object, ...)

# S3 method for class 'vsel'
cv_varsel(
  object,
  cv_method = object$cv_method %||% "LOO",
  nloo = object$nloo,
  K = object$K %||% if (!inherits(object, "datafit")) 5 else 10,
  cvfits = object$cvfits,
  validate_search = object$validate_search %||% TRUE,
  ...
)

# S3 method for class 'refmodel'
cv_varsel(
  object,
  method = "forward",
  cv_method = if (!inherits(object, "datafit")) "LOO" else "kfold",
  ndraws = NULL,
  nclusters = 20,
  ndraws_pred = 400,
  nclusters_pred = NULL,
  refit_prj = !inherits(object, "datafit"),
  nterms_max = NULL,
  penalty = NULL,
  verbose = getOption("projpred.verbose", as.integer(interactive())),
  nloo = if (cv_method == "LOO") object$nobs else NULL,
  K = if (!inherits(object, "datafit")) 5 else 10,
  cvfits = object$cvfits,
  search_control = NULL,
  lambda_min_ratio = 1e-05,
  nlambda = 150,
  thresh = 1e-06,
  validate_search = TRUE,
  seed = NA,
  search_terms = NULL,
  search_out = NULL,
  parallel = getOption("projpred.parallel_cv", FALSE),
  ...
)

Arguments

object

An object of class refmodel (returned by get_refmodel() or init_refmodel()) or an object that can be passed to argument object of get_refmodel().

...

For cv_varsel.default(): Arguments passed to get_refmodel() as well as to cv_varsel.refmodel(). For cv_varsel.vsel(): Arguments passed to cv_varsel.refmodel(). For cv_varsel.refmodel(): Arguments passed to the divergence minimizer (see argument div_minimizer of init_refmodel() as well as section "Draw-wise divergence minimizers" of projpred-package) when refitting the submodels for the performance evaluation (if refit_prj is TRUE).

cv_method

The CV method, either "LOO" or "kfold". In the "LOO" case, a Pareto-smoothed importance sampling leave-one-out CV (PSIS-LOO CV) is performed, which avoids refitting the reference model nloo times (in contrast to a standard LOO-CV). In the "kfold" case, a \(K\)-fold CV is performed. See also section "Note" below.

nloo

Only relevant if cv_method = "LOO" and validate_search = TRUE. If nloo > 0 is smaller than the number of all observations, full LOO-CV (i.e., PSIS-LOO CV with validate_search = TRUE and with nloo = n where n denotes the number of all observations) is approximated by subsampled LOO-CV, i.e., by combining the fast (i.e., validate_search = FALSE) LOO result for the selected models and nloo leave-one-out searches using the difference estimator with simple random sampling (SRS) without replacement (WOR) (Magnusson et al., 2020). Smaller nloo values lead to faster computation, but higher uncertainty in the evaluation part. If NULL, all observations are used (as by default). Note that performance statistic "auc" (see argument stats of summary.vsel() and plot.vsel()) is not supported in case of subsampled LOO-CV. Furthermore, option "best" for argument baseline of summary.vsel() and plot.vsel() is not supported in case of subsampled LOO-CV.

K

Only relevant if cv_method = "kfold" and if cvfits is NULL (which is the case for reference model objects created by get_refmodel.stanreg() or brms::get_refmodel.brmsfit()). Number of folds in \(K\)-fold CV.

cvfits

Only relevant if cv_method = "kfold". The same as argument cvfits of init_refmodel(), but repeated here so that output from run_cvfun() can be inserted here straightforwardly.

A single logical value indicating whether to cross-validate also the search part, i.e., whether to run the search separately for each CV fold (TRUE) or not (FALSE). With FALSE, the computation is faster, but the predictive performance estimates of the selected submodels are optimistically biased. However, these fast biased estimates can be useful to obtain initial information on the usefulness of projection predictive variable selection.

method

The method for the search part. Possible options are "forward" for forward search and "L1" for L1 search. See also section "Details" below.

ndraws

Number of posterior draws used in the search part. Ignored if nclusters is not NULL or in case of L1 search (because L1 search always uses a single cluster). If both (nclusters and ndraws) are NULL, the number of posterior draws from the reference model is used for ndraws. See also section "Details" below.

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 NULL, see argument ndraws. See also section "Details" below.

ndraws_pred

Only relevant if refit_prj is TRUE. Number of posterior draws used in the evaluation part. Ignored if nclusters_pred is not NULL. If both (nclusters_pred and ndraws_pred) are NULL, the number of posterior draws from the reference model is used for ndraws_pred. See also section "Details" below.

nclusters_pred

Only relevant if refit_prj is TRUE. Number of clusters of posterior draws used in the evaluation part. For the meaning of NULL, see argument ndraws_pred. See also section "Details" below.

refit_prj

For the evaluation part, should the projections onto the submodels along the predictor ranking be performed again using ndraws_pred draws or nclusters_pred clusters (TRUE) or should their projections from the search part, which used ndraws draws or nclusters clusters, be re-used (FALSE)?

nterms_max

Maximum submodel size (number of predictor terms) up to which the search is continued. If NULL, then min(19, D) is used where D is the number of terms in the reference model (or in search_terms, if supplied). Note that nterms_max does not count the intercept, so use nterms_max = 0 for the intercept-only model. (Correspondingly, D above does not count the intercept.)

penalty

Only relevant for L1 search. A numeric vector determining the relative penalties or costs for the predictors. A value of 0 means that those predictors have no cost and will therefore be selected first, whereas Inf means those predictors will never be selected. If NULL, then 1 is used for each predictor.

verbose

A single integer value from the set \(\{0, 1, 2, 3, 4\}\) (for varsel(), \(3\) and \(4\) have the same effect), indicating how much information (if any) to print out during the computations. Higher values indicate that more information should be printed, 0 deactivates the verbose mode. Internally, argument verbose is coerced to integer via as.integer(), so technically, a single logical value or a single numeric value work as well.

search_control

A list of "control" arguments (i.e., tuning parameters) for the search. In case of forward search, these arguments are passed to the divergence minimizer (see argument div_minimizer of init_refmodel() as well as section "Draw-wise divergence minimizers" of projpred-package). In case of forward search, NULL causes ... to be used not only for the performance evaluation, but also for the search. In case of L1 search, possible arguments are:

  • lambda_min_ratio: Ratio between the smallest and largest lambda in the L1-penalized search (default: 1e-5). 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: Number of values in the lambda grid for L1-penalized search (default: 150). No need to change this unless the program gives a warning about this.

  • thresh: Convergence threshold when computing the L1 path (default: 1e-6). Usually, there is no need to change this.

lambda_min_ratio

Deprecated (please use search_control instead). Only relevant for L1 search. Ratio between the smallest and largest lambda in the L1-penalized 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

Deprecated (please use search_control instead). Only relevant for L1 search. Number of values in the lambda grid for L1-penalized search. No need to change this unless the program gives a warning about this.

thresh

Deprecated (please use search_control instead). Only relevant for L1 search. Convergence threshold when computing the L1 path. Usually, there is no need to change this.

seed

Pseudorandom number generation (PRNG) seed by which the same results can be obtained again if needed. Passed to argument seed of set.seed(), but can also be NA to not call set.seed() at all. If not NA, then the PRNG state is reset (to the state before calling cv_varsel()) upon exiting cv_varsel(). Here, seed is used for clustering the reference model's posterior draws (if !is.null(nclusters) or !is.null(nclusters_pred)), for subsampling PSIS-LOO CV folds (if nloo is smaller than the number of observations), for sampling the folds in \(K\)-fold CV, and for drawing new group-level effects when predicting from a multilevel submodel (however, not yet in case of a GAMM).

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 ("1") is always included internally via union(), so there's no difference between including it explicitly or omitting it. The default search_terms considers all the terms in the reference model's formula.

search_out

Intended for internal use.

parallel

A single logical value indicating whether to run costly parts of the CV in parallel (TRUE) or not (FALSE). See also section "Note" below as well as section "Parallelization" in projpred-package.

Value

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 projpred-package).

Details

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 datafits). 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 augmented-data 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 overfit when more predictors are added. This overfit can be detected by running search validation (see cv_varsel()).

An L1 search may select an interaction term before all involved lower-order interaction terms (including main-effect terms) have been selected. In projpred versions > 2.6.0, the resulting predictor ranking is automatically modified so that the lower-order 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 intercept-only 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")).

Note

If validate_search is FALSE, the search is not included in the CV so that only a single full-data search is run. If the number of observations is large, the fast PSIS-LOO CV along the full-data search path is likely to be accurate. If the number of observations is small or moderate, the fast PSIS-LOO CV along the full-data search path is likely to have optimistic bias in the middle of the search path. This result can be used to guide further actions and the optimistic bias can be greatly reduced by using validate_search = TRUE.

PSIS uses the Pareto-\(\hat{k}\) diagnostic to assess the reliability of PSIS-LOO CV. Global option projpred.warn_psis (default TRUE) controls whether the Pareto-\(\hat{k}\) diagnostics may result in warnings. See loo::loo-glossary for how to interpret the Pareto-\(\hat{k}\) values and the warning thresholds. projpred does not support the usually recommended moment-matching (see loo::loo_moment_match() and brms::loo_moment_match()), mixture importance sampling (vignette("loo2-mixis", package="loo")), or reloo-ing (brms::reloo()). If the reference model PSIS-LOO CV Pareto-\(\hat{k}\) values are good, but there are high Pareto-\(\hat{k}\) values for the projected models, you can try increasing the number of draws used for the PSIS-LOO CV (ndraws in case of refit_prj = FALSE; ndraws_pred in case of refit_prj = TRUE). If increasing the number of draws does not help and if the reference model PSIS-LOO CV Pareto-\(\hat{k}\) values are high, and the reference model PSIS-LOO CV results change substantially when using moment-matching, mixture importance sampling, or reloo-ing, we recommend to use \(K\)-fold CV within projpred.

For PSIS-LOO CV, projpred calls loo::psis() (or, exceptionally, loo::sis(), see below) with r_eff = NA. This is only a problem if there was extreme autocorrelation between the MCMC iterations when the reference model was built. In those cases however, the reference model should not have been used anyway, so we don't expect projpred's r_eff = NA to be a problem.

PSIS cannot be used if the number of draws or clusters is too small. In such cases, projpred resorts to standard importance sampling (SIS) and shows a message about this. Throughout the documentation, the term "PSIS" is used even though in fact, projpred resorts to SIS in these special cases. If SIS is used, check that the reference model PSIS-LOO CV Pareto-\(\hat{k}\) values are good.

With parallel = TRUE, costly parts of projpred's CV can be run in parallel. Costly parts are the fold-wise searches and performance evaluations in case of validate_search = TRUE. (Note that in case of \(K\)-fold CV, the \(K\) reference model refits are not affected by argument parallel; only projpred's CV is affected.) The parallelization is powered by the foreach package. Thus, any parallel (or sequential) backend compatible with foreach can be used, e.g., the backends from packages doParallel, doMPI, or doFuture. For GLMs, this CV parallelization should work reliably, but for other models (such as GLMMs), it may lead to excessive memory usage which in turn may crash the R session (on Unix systems, setting an appropriate memory limit via unix::rlimit_as() may avoid crashing the whole machine). However, the problem of excessive memory usage is less pronounced for the CV parallelization than for the projection parallelization described in projpred-package. In that regard, the CV parallelization is recommended over the projection parallelization.

References

Magnusson, Måns, Michael Riis Andersen, Johan Jonasson, Aki Vehtari. 2020. "Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data." In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, edited by Silvia Chiappa and Roberto Calandra, 108:341–351. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v108/magnusson20a.html.

McLatchie, Yann, Sölvi Rögnvaldsson, Frank Weber, and Aki Vehtari. 2025. "Advances in Projection Predictive Inference." Statistical Science, 40 (1):128–147. doi:10.1214/24-STS949 .

Piironen, Juho, Markus Paasiniemi, and Aki Vehtari. 2020. "Projective Inference in High-Dimensional Problems: Prediction and Feature Selection." Electronic Journal of Statistics, 14 (1):2155–2197. doi:10.1214/20-EJS1711 .

Vehtari, Aki, Andrew Gelman, and Jonah Gabry. 2017. "Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC." Statistics and Computing, 27 (5):1413–32. doi:10.1007/s11222-016-9696-4 .

Vehtari, Aki, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry. 2024. "Pareto Smoothed Importance Sampling." Journal of Machine Learning Research, 25 (72):1–58. https://jmlr.org/papers/v25/19-556.html.

See also

varsel()

Examples

# 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 = 1000, refresh = 0, seed = 9876
)

# Run cv_varsel() (with L1 search and small values for `K`, `nterms_max`, and
# `nclusters_pred`, but only for the sake of speed in this example; this is
# not recommended in general):
cvvs <- cv_varsel(fit, method = "L1", cv_method = "kfold", K = 2,
                  nterms_max = 3, nclusters_pred = 10, seed = 5555)
#> Fitting model 1 out of 2
#> Fitting model 2 out of 2
# Now see, for example, `?print.vsel`, `?plot.vsel`, `?suggest_size.vsel`,
# and `?ranking` for possible post-processing functions.