Package 'projpred'

Description

The R package projpred performs the projection predictive variable (or "feature") selection for various regression models. We recommend to read the README file (available with enhanced formatting online) and the main vignette (topic = "projpred", but also available online) before continuing here.

Terminology

Throughout the whole package documentation, we use the term "submodel" for all kinds of candidate models onto which the reference model is projected. For custom reference models, the candidate models don't need to be actual submodels of the reference model, but in any case (even for custom reference models), the candidate models are always actual submodels of the full formula used by the search procedure. In this regard, it is correct to speak of submodels, even in case of a custom reference model.

The following model type abbreviations will be used at multiple places throughout the documentation: GLM (generalized linear model), GLMM (generalized linear multilevel—or "mixed"—model), GAM (generalized additive model), and GAMM (generalized additive multilevel—or "mixed"—model). Note that the term "generalized" includes the Gaussian family as well.

Draw-wise divergence minimizers

For the projection of the reference model onto a submodel, projpred currently relies on the following functions as draw-wise divergence minimizers (in other words, these are the workhorse functions employed by projpred's internal default div_minimizer functions, see init_refmodel()):

• Submodel without multilevel or additive terms:

  • For the traditional (or latent) projection (or the augmented-data projection in case of the binomial() or brms::bernoulli() family): An internal C++ function which basically serves the same purpose as lm() for the gaussian() family and glm() for all other families. The returned object inherits from class subfit. Possible tuning parameters for this internal C++ function are: regul (amount of ridge regularization; default: 1e-4), thresh_conv (convergence threshold; default: 1e-7), qa_updates_max (maximum number of quadratic approximation updates; default: 100, but fixed to 1 in case of the Gaussian family with identity link), ls_iter_max (maximum number of line search iterations; default: 30, but fixed to 1 in case of the Gaussian family with identity link), normalize (single logical value indicating whether to scale the predictors internally with the returned regression coefficient estimates being back-adjusted appropriately; default: TRUE), beta0_init (single numeric value giving the starting value for the intercept at centered predictors; default: 0), and beta_init (numeric vector giving the starting values for the regression coefficients; default: vector of 0s).

  • For the augmented-data projection: MASS::polr() (the returned object inherits from class polr) for the brms::cumulative() family or rstanarm::stan_polr() fits, nnet::multinom() (the returned object inherits from class multinom) for the brms::categorical() family.

• Submodel with multilevel but no additive terms:

  • For the traditional (or latent) projection (or the augmented-data projection in case of the binomial() or brms::bernoulli() family): lme4::lmer() (the returned object inherits from class lmerMod) for the gaussian() family, lme4::glmer() (the returned object inherits from class glmerMod) for all other families.

  • For the augmented-data projection: ordinal::clmm() (the returned object inherits from class clmm) for the brms::cumulative() family, mclogit::mblogit() (the returned object inherits from class mmblogit) for the brms::categorical() family.

• Submodel without multilevel but additive terms: mgcv::gam() (the returned object inherits from class gam).

• Submodel with multilevel and additive terms: gamm4::gamm4() (within projpred, the returned object inherits from class gamm4).

Verbosity, messages, warnings, errors

Setting global option projpred.extra_verbose to TRUE will print out which submodel projpred is currently projecting onto as well as (if method = "forward" and verbose = TRUE in varsel() or cv_varsel()) which submodel has been selected at those steps of the forward search for which a percentage (of the maximum submodel size that the search is run up to) is printed. In general, however, we cannot recommend setting this global option to TRUE for cv_varsel() with validate_search = TRUE (simply due to the amount of information that will be printed, but also due to the progress bar which will not work as intended anymore).

By default, projpred catches messages and warnings from the draw-wise divergence minimizers and throws their unique collection after performing all draw-wise divergence minimizations (i.e., draw-wise projections). This can be deactivated by setting global option projpred.warn_prj_drawwise to FALSE.

Furthermore, by default, projpred checks the convergence of the draw-wise divergence minimizers and throws a warning if any seem to have not converged. This warning is thrown after the warning message from global option projpred.warn_prj_drawwise (see above) and can be deactivated by setting global option projpred.check_conv to FALSE.

Parallelization

The projection of the reference model onto a submodel can be run in parallel (across the projected draws). This 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. Using the global option projpred.prll_prj_trigger, the number of projected draws below which no parallelization is applied (even if a parallel backend is registered) can be modified. Such a "trigger" threshold exists because of the computational overhead of a parallelization which makes the projection parallelization only useful for a sufficiently large number of projected draws. By default, the projection parallelization is turned off, which can also be achieved by supplying Inf (or NULL) to option projpred.prll_prj_trigger. Note that we cannot recommend the projection parallelization on Windows because in our experience, the parallelization overhead is larger there, causing a parallel run to take longer than a sequential run. Also note that the projection parallelization works well for submodels which are GLMs (and hence also for the latent projection if the submodel has no multilevel or additive predictor terms), but for all other types of submodels, the fitted submodel objects are quite big, which—when running in parallel—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). Thus, we currently cannot recommend parallelizing projections onto submodels which are GLMs (in this context, the latent projection onto a submodel without multilevel and without additive terms may be regarded as a projection onto a submodel which is a GLM). However, for cv_varsel(), there is also a CV parallelization (i.e., a parallelization of projpred's cross-validation) which can be activated via argument parallel.

Multilevel models: "Integrating out" group-level effects

In case of multilevel models, projpred offers two global options for "integrating out" group-level effects: projpred.mlvl_pred_new and projpred.mlvl_proj_ref_new. When setting projpred.mlvl_pred_new to TRUE (default is FALSE), then at prediction time, projpred will treat group levels existing in the training data as new group levels, implying that their group-level effects are drawn randomly from a (multivariate) Gaussian distribution. This concerns both, the reference model and the (i.e., any) submodel. Furthermore, setting projpred.mlvl_pred_new to TRUE causes as.matrix.projection() and as_draws_matrix.projection() to omit the projected group-level effects (for the group levels from the original dataset). When setting projpred.mlvl_proj_ref_new to TRUE (default is FALSE), then at projection time, the reference model's fitted values (that the submodels fit to) will be computed by treating the group levels from the original dataset as new group levels, implying that their group-level effects will be drawn randomly from a (multivariate) Gaussian distribution (as long as the reference model is a multilevel model, which—for custom reference models—does not need to be the case). This also affects the latent response values for a latent projection correspondingly. Setting projpred.mlvl_pred_new to TRUE makes sense, e.g., when the prediction task is such that any group level will be treated as a new one. Typically, setting projpred.mlvl_proj_ref_new to TRUE only makes sense when projpred.mlvl_pred_new is already set to TRUE. In that case, the default of FALSE for projpred.mlvl_proj_ref_new ensures that at projection time, the submodels fit to the best possible fitted values from the reference model, and setting projpred.mlvl_proj_ref_new to TRUE would make sense if the group-level effects should be integrated out completely.

Memory usage

By setting the global option projpred.run_gc to TRUE, projpred will call gc() at some places (e.g., after each size that the forward search passes through) to free up some memory. These gc() calls are not always necessary to reduce the peak memory usage, but they add runtime (hence the default of FALSE for that global option).

Other notes

Most examples are not executed when called via example(). To execute them, their code has to be copied and pasted manually to the console.

Functions

init_refmodel(), get_refmodel()

For setting up an object containing information about the reference model, the submodels, and how the projection should be carried out. Explicit calls to init_refmodel() and get_refmodel() are only rarely needed.

varsel(), cv_varsel()

For running the search part and the evaluation part for a projection predictive variable selection, possibly with cross-validation (CV).

summary.vsel(), print.vsel(), plot.vsel(), suggest_size.vsel(), ranking(), cv_proportions(), plot.cv_proportions(), performances()

For post-processing the results from varsel() and cv_varsel().

project()

For projecting the reference model onto submodel(s). Typically, this follows the variable selection, but it can also be applied directly (without a variable selection).

as.matrix.projection() and as_draws_matrix.projection()

For extracting projected parameter draws.

proj_linpred(), proj_predict()

For making predictions from a submodel (after projecting the reference model onto it).

Author(s)

Maintainer: Frank Weber [email protected]

Authors:

• Juho Piironen [email protected]

• Markus Paasiniemi

• Alejandro Catalina [email protected]

• Aki Vehtari

Other contributors:

• Jonah Gabry [contributor]

• Marco Colombo [contributor]

• Paul-Christian Bürkner [contributor]

• Hamada S. Badr [contributor]

• Brian Sullivan [contributor]

• Sölvi Rögnvaldsson [contributor]

• The LME4 Authors (see file 'LICENSE' for details) [copyright holder]

• Yann McLatchie [contributor]

• Juho Timonen [contributor]

See Also

Useful links:

• https://mc-stan.org/projpred/

• https://discourse.mc-stan.org

• Report bugs at https://github.com/stan-dev/projpred/issues/

Description

These are the posterior::as_draws() and posterior::as_draws_matrix() methods for projection objects (returned by project(), possibly as elements of a list). They extract the projected parameter draws and return them as a draws_matrix. In case of different (i.e., nonconstant) weights for the projected draws, a draws_matrix allows for a safer handling of these weights (safer in contrast to the matrix returned by as.matrix.projection()), in particular by providing the natural input for posterior::resample_draws() (see section "Examples" below).

Usage

## S3 method for class 'projection'
as_draws_matrix(x, ...)

## S3 method for class 'projection'
as_draws(x, ...)

Arguments

Details

In case of the augmented-data projection for a multilevel submodel of a brms::categorical() reference model, the multilevel parameters (and therefore also their names) slightly differ from those in the brms reference model fit (see section "Augmented-data projection" in extend_family()'s documentation).

Value

An $S_{\mathrm{prj}} \times Q$ draws_matrix (see posterior::draws_matrix()) of projected draws, with $S_{\mathrm{prj}}$ denoting the number of projected draws and $Q$ the number of parameters. If the projected draws have nonconstant weights, posterior::weight_draws() is applied internally.

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 = 500, refresh = 0, seed = 9876
)

# Projection onto an arbitrary combination of predictor terms (with a small
# value for `nclusters`, but only for illustrative purposes; this is not
# recommended in general):
prj <- project(fit, predictor_terms = c("X1", "X3", "X5"), nclusters = 5,
               seed = 9182)

# Applying the posterior::as_draws_matrix() generic to the output of
# project() dispatches to the projpred::as_draws_matrix.projection()
# method:
prj_draws <- posterior::as_draws_matrix(prj)

# Resample the projected draws according to their weights:
set.seed(3456)
prj_draws_resampled <- posterior::resample_draws(prj_draws, ndraws = 1000)

# The values from the following two objects should be the same (in general,
# this only holds approximately):
print(proportions(table(rownames(prj_draws_resampled))))
print(weights(prj_draws))

# Treat the resampled draws like ordinary draws, e.g., summarize them:
print(posterior::summarize_draws(
  prj_draws_resampled,
  "median", "mad", function(x) quantile(x, probs = c(0.025, 0.975))
))
# Or visualize them using the `bayesplot` package:
if (requireNamespace("bayesplot", quietly = TRUE)) {
  print(bayesplot::mcmc_intervals(prj_draws_resampled))
}

Description

This is the as.matrix() method for projection objects (returned by project(), possibly as elements of a list). It extracts the projected parameter draws and returns them as a matrix. In case of different (i.e., nonconstant) weights for the projected draws, see as_draws_matrix.projection() for a better solution.

Usage

## S3 method for class 'projection'
as.matrix(x, nm_scheme = NULL, allow_nonconst_wdraws_prj = FALSE, ...)

Arguments

Details

In case of the augmented-data projection for a multilevel submodel of a brms::categorical() reference model, the multilevel parameters (and therefore also their names) slightly differ from those in the brms reference model fit (see section "Augmented-data projection" in extend_family()'s documentation).

Value

An $S_{\mathrm{prj}} \times Q$ matrix of projected draws, with $S_{\mathrm{prj}}$ denoting the number of projected draws and $Q$ the number of parameters. If allow_nonconst_wdraws_prj is set to TRUE, the weights of the projected draws are stored in an attribute wdraws_prj. (If allow_nonconst_wdraws_prj is FALSE, projected draws with nonconstant weights cause an error.)

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 = 500, refresh = 0, seed = 9876
)

# Projection onto an arbitrary combination of predictor terms (with a small
# value for `ndraws`, but only for the sake of speed in this example; this
# is not recommended in general):
prj <- project(fit, predictor_terms = c("X1", "X3", "X5"), ndraws = 21,
               seed = 9182)

# Applying the as.matrix() generic to the output of project() dispatches to
# the projpred::as.matrix.projection() method:
prj_mat <- as.matrix(prj)

# Since the draws have all the same weight here, we can treat them like
# ordinary MCMC draws, e.g., we can summarize them using the `posterior`
# package:
if (requireNamespace("posterior", quietly = TRUE)) {
  print(posterior::summarize_draws(
    posterior::as_draws_matrix(prj_mat),
    "median", "mad", function(x) quantile(x, probs = c(0.025, 0.975))
  ))
}
# Or visualize them using the `bayesplot` package:
if (requireNamespace("bayesplot", quietly = TRUE)) {
  print(bayesplot::mcmc_intervals(prj_mat))
}

Description

This is the function which has to be supplied to extend_family()'s argument augdat_ilink in case of the augmented-data projection for the binomial() family.

Usage

augdat_ilink_binom(eta_arr, link = "logit")

Arguments

Value

An array as described in section "Augmented-data projection" of extend_family()'s documentation.

Description

This is the function which has to be supplied to extend_family()'s argument augdat_link in case of the augmented-data projection for the binomial() family.

Usage

augdat_link_binom(prb_arr, link = "logit")

Arguments

Value

An array as described in section "Augmented-data projection" of extend_family()'s documentation.

Description

Sometimes there can be terms in a formula that refer to a matrix instead of a single predictor. This function breaks up the matrix term into individual predictors to handle separately, as that is probably the intention of the user.

Usage

break_up_matrix_term(formula, data)

Arguments

Value

A list containing the expanded formula and the expanded data.frame.

Description

This function aggregates $S$ parameter draws that have been clustered into $S_{\mathrm{cl}}$ clusters by averaging across the draws that belong to the same cluster. This averaging can be done in a weighted fashion.

Usage

cl_agg(
  draws,
  cl = seq_len(nrow(draws)),
  wdraws = rep(1, nrow(draws)),
  eps_wdraws = 0
)

Arguments

Value

An $S_{\mathrm{cl}} \times P$ matrix of aggregated parameter draws.

Examples

set.seed(323)
S <- 100L
P <- 3L
draws <- matrix(rnorm(S * P), nrow = S, ncol = P)
# Clustering example:
S_cl <- 10L
cl_draws <- sample.int(S_cl, size = S, replace = TRUE)
draws_cl <- cl_agg(draws, cl = cl_draws)
# Clustering example with nonconstant `wdraws`:
w_draws <- rgamma(S, shape = 4)
draws_cl <- cl_agg(draws, cl = cl_draws, wdraws = w_draws)
# Thinning example (implying constant `wdraws`):
S_th <- 50L
idxs_thin <- round(seq(1, S, length.out = S_th))
th_draws <- rep(NA, S)
th_draws[idxs_thin] <- seq_len(S_th)
draws_th <- cl_agg(draws, cl = th_draws)

Description

Calculates the ranking proportions from the fold-wise predictor rankings in a cross-validation (CV) with fold-wise searches. For a given predictor $x$ and a given submodel size $j$, the ranking proportion is the proportion of CV folds which have predictor $x$ at position $j$ of their predictor ranking. While these ranking proportions are helpful for investigating variability in the predictor ranking, they can also be cumulated across submodel sizes. The cumulated ranking proportions are more helpful when it comes to model selection.

Usage

cv_proportions(object, ...)

## S3 method for class 'ranking'
cv_proportions(object, cumulate = FALSE, ...)

## S3 method for class 'vsel'
cv_proportions(object, ...)

Arguments

Value

A numeric matrix containing the ranking proportions. This matrix has nterms_max rows and nterms_max columns, with nterms_max as specified in the (possibly implicit) ranking() call. The rows correspond to the submodel sizes and the columns to the predictor terms (sorted according to the full-data predictor ranking). If cumulate is FALSE, then the returned matrix is of class cv_proportions. If cumulate is TRUE, then the returned matrix is of classes cv_proportions_cumul and cv_proportions (in this order).

Note that if cumulate is FALSE, then the values in the returned matrix only need to sum to 1 (column-wise and row-wise) if nterms_max (see above) is equal to the full model size. Likewise, if cumulate is TRUE, then the value 1 only needs to occur in each column of the returned matrix if nterms_max is equal to the full model size.

The cv_proportions() function is only applicable if the ranking object includes fold-wise predictor rankings (i.e., if it is based on a vsel object created by cv_varsel() with validate_search = TRUE). If the ranking object contains only a full-data predictor ranking (i.e., if it is based on a vsel object created by varsel() or by cv_varsel(), but the latter with validate_search = FALSE), then an error is thrown because in that case, there are no fold-wise predictor rankings from which to calculate ranking proportions.

See Also

plot.cv_proportions()

Examples

# For an example, see `?plot.cv_proportions`.

Description

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 = TRUE,
  nloo = object$nobs,
  K = if (!inherits(object, "datafit")) 5 else 10,
  cvfits = object$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.prll_cv", FALSE),
  ...
)

Arguments

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 start overfitting when more predictors are added.

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")).

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).

Note

If validate_search is FALSE, the search is not included in the CV so that only a single full-data search is run.

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 draws have different (i.e., nonconstant) weights or if the number of draws is too small. In such cases, projpred resorts to standard importance sampling (SIS) and throws a warning about this. Throughout the documentation, the term "PSIS" is used even though in fact, projpred resorts to SIS in these special cases.

With parallel = TRUE, costly parts of projpred's CV are 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 Andersen, Johan Jonasson, and Aki Vehtari. 2019. "Bayesian Leave-One-Out Cross-Validation for Large Data." In Proceedings of the 36th International Conference on Machine Learning, edited by Kamalika Chaudhuri and Ruslan Salakhutdinov, 97:4244–53. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v97/magnusson19a.html.

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. 2022. "Pareto Smoothed Importance Sampling." arXiv. doi:10.48550/arXiv.1507.02646.

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)
# Now see, for example, `?print.vsel`, `?plot.vsel`, `?suggest_size.vsel`,
# and `?ranking` for possible post-processing functions.

Description

These are helper functions to create cross-validation (CV) folds, i.e., to split up the indices from 1 to n into K subsets ("folds") for $K$-fold CV. These functions are potentially useful when creating the input for arguments cvfits and cvfun of init_refmodel() (or argument cvfits of cv_varsel.refmodel()). Function cvfolds() is deprecated; please use cv_folds() instead (apart from the name, they are the same). The return value of cv_folds() and cv_ids() is different, see below for details.

Usage

cv_folds(n, K, seed = NA)

cvfolds(n, K, seed = NA)

cv_ids(n, K, out = c("foldwise", "indices"), seed = NA)

Arguments

Value

cv_folds() returns a vector of length n such that each element is an integer between 1 and K denoting which fold the corresponding data point belongs to. The return value of cv_ids() depends on the out argument. If out = "foldwise", the return value is a list with K elements, each being a list with elements tr and ts giving the training and test indices, respectively, for the corresponding fold. If out = "indices", the return value is a list with elements tr and ts each being a list with K elements giving the training and test indices, respectively, for each fold.

Examples

n <- 100
set.seed(1234)
y <- rnorm(n)
cv <- cv_ids(n, K = 5)
# Mean within the test set of each fold:
cvmeans <- sapply(cv, function(fold) mean(y[fold$ts]))

Description

Binomial toy example

Usage

df_binom

Format

A simulated classification dataset containing 100 observations.

y

response, 0 or 1.

x

predictors, 30 in total.

Source

https://web.stanford.edu/~hastie/glmnet/glmnetData/BNExample.RData

Description

Gaussian toy example

Usage

df_gaussian

Format

A simulated regression dataset containing 100 observations.

y

response, real-valued.

x

predictors, 20 in total. Mean and SD are approximately 0 and 1, respectively.

Source

https://web.stanford.edu/~hastie/glmnet/glmnetData/QSExample.RData

Description

This function adds some internally required elements to an object of class family (see, e.g., family()). It is called internally by init_refmodel(), so you will rarely need to call it yourself.

Usage

extend_family(
  family,
  latent = FALSE,
  latent_y_unqs = NULL,
  latent_ilink = NULL,
  latent_ll_oscale = NULL,
  latent_ppd_oscale = NULL,
  augdat_y_unqs = NULL,
  augdat_link = NULL,
  augdat_ilink = NULL,
  augdat_args_link = list(),
  augdat_args_ilink = list(),
  ...
)

Arguments

Details

In the following, $N$, $C_{\mathrm{cat}}$, $C_{\mathrm{lat}}$, $S_{\mathrm{ref}}$, and $S_{\mathrm{prj}}$ from help topic refmodel-init-get are used. Note that $N$ does not necessarily denote the number of original observations; it can also refer to new observations. Furthermore, let $S$ denote either $S_{\mathrm{ref}}$ or $S_{\mathrm{prj}}$, whichever is appropriate in the context where it is used.

Value

The family object extended in the way needed by projpred.

Augmented-data projection

As their first input, the functions supplied to arguments augdat_link and augdat_ilink have to accept:

• For augdat_link: an $S \times N \times C_{\mathrm{cat}}$ array containing the probabilities for the response categories. The order of the response categories is the same as in family$cats (see argument augdat_y_unqs).

• For augdat_ilink: an $S \times N \times C_{\mathrm{lat}}$ array containing the linear predictors.

The return value of these functions needs to be:

• For augdat_link: an $S \times N \times C_{\mathrm{lat}}$ array containing the linear predictors.

• For augdat_ilink: an $S \times N \times C_{\mathrm{cat}}$ array containing the probabilities for the response categories. The order of the response categories has to be the same as in family$cats (see argument augdat_y_unqs).

For the augmented-data projection, the response vector resulting from extract_model_data (see init_refmodel()) is coerced to a factor (using as.factor()) at multiple places throughout this package. Inside of init_refmodel(), the levels of this factor have to be identical to family$cats (after applying extend_family() inside of init_refmodel()). Everywhere else, these levels have to be a subset of ⁠<refmodel>$family$cats⁠ (where ⁠<refmodel>⁠ is an object resulting from init_refmodel()). See argument augdat_y_unqs for how to control family$cats.

For ordinal brms families, be aware that the submodels (onto which the reference model is projected) currently have the following restrictions:

• The discrimination parameter disc is not supported (i.e., it is a constant with value 1).

• The thresholds are "flexible" (see brms::brmsfamily()).

• The thresholds do not vary across the levels of a factor-like variable (see argument gr of brms::resp_thres()).

• The "probit_approx" link is replaced by "probit".

For the brms::categorical() family, be aware that:

• For multilevel submodels, the group-level effects are allowed to be correlated between different response categories.

• For multilevel submodels, mclogit versions < 0.9.4 may throw the error 'a' (<number> x 1) must be square. Updating mclogit to a version >= 0.9.4 should fix this.

Latent projection

The function supplied to argument latent_ilink needs to have the prototype

latent_ilink(lpreds, cl_ref, wdraws_ref = rep(1, length(cl_ref)))

where:

• lpreds accepts an $S \times N$ matrix containing the linear predictors.

• cl_ref accepts a numeric vector of length $S_{\mathrm{ref}}$, containing projpred's internal cluster indices for these draws.

• wdraws_ref accepts a numeric vector of length $S_{\mathrm{ref}}$, containing weights for these draws. These weights should be treated as not being normalized (i.e., they don't necessarily sum to 1).

The return value of latent_ilink needs to contain the linear predictors transformed to the original response space, with the following structure:

• If is.null(family$cats) (after taking latent_y_unqs into account): an $S \times N$ matrix.

• If !is.null(family$cats) (after taking latent_y_unqs into account): an $S \times N \times C_{\mathrm{cat}}$ array. In that case, latent_ilink needs to return probabilities (for the response categories given in family$cats, after taking latent_y_unqs into account).

The function supplied to argument latent_ll_oscale needs to have the prototype

latent_ll_oscale(ilpreds, y_oscale, wobs = rep(1, length(y_oscale)), cl_ref,
                 wdraws_ref = rep(1, length(cl_ref)))

where:

• ilpreds accepts the return value from latent_ilink.

• y_oscale accepts a vector of length $N$ containing response values on the original response scale.

• wobs accepts a numeric vector of length $N$ containing observation weights.

• cl_ref accepts the same input as argument cl_ref of latent_ilink.

• wdraws_ref accepts the same input as argument wdraws_ref of latent_ilink.

The return value of latent_ll_oscale needs to be an $S \times N$ matrix containing the response-scale (not latent-scale) log-likelihood values for the $N$ observations from its inputs.

The function supplied to argument latent_ppd_oscale needs to have the prototype

latent_ppd_oscale(ilpreds_resamp, wobs, cl_ref,
                  wdraws_ref = rep(1, length(cl_ref)), idxs_prjdraws)

where:

• ilpreds_resamp accepts the return value from latent_ilink, but possibly with resampled (clustered) draws (see argument nresample_clusters of proj_predict()).

• wobs accepts a numeric vector of length $N$ containing observation weights.

• cl_ref accepts the same input as argument cl_ref of latent_ilink.

• wdraws_ref accepts the same input as argument wdraws_ref of latent_ilink.

• idxs_prjdraws accepts a numeric vector of length dim(ilpreds_resamp)[1] containing the resampled indices of the projected draws (i.e., these indices are values from the set $\{1, ..., \texttt{dim(ilpreds)[1]}\}$ where ilpreds denotes the return value of latent_ilink).

The return value of latent_ppd_oscale needs to be a $\texttt{dim(ilpreds\_resamp)[1]} \times N$ matrix containing the response-scale (not latent-scale) draws from the posterior(-projection) predictive distributions for the $N$ observations from its inputs.

If the bodies of these three functions involve parameter draws from the reference model which have not been projected (e.g., for latent_ilink, the thresholds in an ordinal model), cl_agg() is provided as a helper function for aggregating these reference model draws in the same way as the draws have been aggregated for the first argument of these functions (e.g., lpreds in case of latent_ilink).

In fact, the weights passed to argument wdraws_ref are nonconstant only in case of cv_varsel() with cv_method = "LOO" and validate_search = TRUE. In that case, the weights passed to this argument are the PSIS-LOO CV weights for one observation. Note that although argument wdraws_ref has the suffix ⁠_ref⁠, wdraws_ref does not necessarily obtain weights for the initial reference model's posterior draws: In case of cv_varsel() with cv_method = "kfold", these weights may refer to one of the $K$ reference model refits (but in that case, they are constant anyway).

If family$cats is not NULL (after taking latent_y_unqs into account), then the response vector resulting from extract_model_data (see init_refmodel()) is coerced to a factor (using as.factor()) at multiple places throughout this package. Inside of init_refmodel(), the levels of this factor have to be identical to family$cats (after applying extend_family() inside of init_refmodel()). Everywhere else, these levels have to be a subset of ⁠<refmodel>$family$cats⁠ (where ⁠<refmodel>⁠ is an object resulting from init_refmodel()).

Description

Family objects not in the set of default family objects.

Usage

Student_t(link = "identity", nu = 3)

Arguments

Value

A family object analogous to those described in family.

Note

Support for the Student_t() family is still experimental.

Description

A helper function to construct the input for argument search_terms of varsel() or cv_varsel() if certain predictor terms should be forced to be selected first whereas other predictor terms are optional (i.e., they are subject to the variable selection, but only after the inclusion of the "forced" terms).

Usage

force_search_terms(forced_terms, optional_terms)

Arguments

Value

A character vector that may be used as input for argument search_terms of varsel() or cv_varsel().

See Also

varsel(), cv_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 = 500, refresh = 0, seed = 9876
)

# We will force X1 and X2 to be selected first:
search_terms_forced <- force_search_terms(
  forced_terms = paste0("X", 1:2),
  optional_terms = paste0("X", 3:5)
)

# Run varsel() (here without cross-validation and with small values for
# `nterms_max`, `nclusters`, and `nclusters_pred`, but only for the sake of
# speed in this example; this is not recommended in general):
vs <- varsel(fit, nclusters = 5, nclusters_pred = 10,
             search_terms = search_terms_forced, seed = 5555)
# Now see, for example, `?print.vsel`, `?plot.vsel`, `?suggest_size.vsel`,
# and `?ranking` for possible post-processing functions.

Description

The mesquite bushes yields dataset from Gelman and Hill (2006) (http://www.stat.columbia.edu/~gelman/arm/).

Usage

mesquite

Format

The response variable is the total weight (in grams) of photosynthetic material as derived from actual harvesting of the bush. The predictor variables are:

diam1

diameter of the canopy (the leafy area of the bush) in meters, measured along the longer axis of the bush.

diam2

canopy diameter measured along the shorter axis.

canopy height

height of the canopy.

total height

total height of the bush.

density

plant unit density (# of primary stems per plant unit).

group

group of measurements (0 for the first group, 1 for the second group).

Source

http://www.stat.columbia.edu/~gelman/arm/examples/mesquite/mesquite.dat

References

Gelman, Andrew, and Jennifer Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge, UK: Cambridge University Press. doi:10.1017/CBO9780511790942.

Description

Retrieves the predictive performance summaries after running varsel() or cv_varsel(). These summaries are computed by summary.vsel(), so the main method of performances() is performances.vselsummary() (objects of class vselsummary are returned by summary.vsel()). As a shortcut method, performances.vsel() is provided as well (objects of class vsel are returned by varsel() and cv_varsel()). For a graphical representation, see plot.vsel().

Usage

performances(object, ...)

## S3 method for class 'vselsummary'
performances(object, ...)

## S3 method for class 'vsel'
performances(object, ...)

Arguments

Value

An object of class performances which is a list with the following elements:

• submodels: The predictive performance results for the submodels, as a data.frame.

• reference_model: The predictive performance results for the reference model, as a named vector.

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 = 500, refresh = 0, seed = 9876
)

# Run varsel() (here without cross-validation, 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)
print(performances(vs))

Description

Plots the ranking proportions (see cv_proportions()) from the fold-wise predictor rankings in a cross-validation with fold-wise searches. This is a visualization of the transposed matrix returned by cv_proportions(). The proportions printed as text inside of the colored tiles are rounded to whole percentage points (the plotted proportions themselves are not rounded).

Usage

## S3 method for class 'cv_proportions'
plot(x, text_angle = NULL, ...)

## S3 method for class 'ranking'
plot(x, ...)

Arguments

Value

A ggplot2 plotting object (of class gg and ggplot).

Author(s)

Idea and original code by Aki Vehtari. Slight modifications of the original code by Frank Weber, Yann McLatchie, and Sölvi Rögnvaldsson. Final implementation in projpred by Frank Weber.

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)

# Extract predictor rankings:
rk <- ranking(cvvs)

# Compute ranking proportions:
pr_rk <- cv_proportions(rk)

# Visualize the ranking proportions:
gg_pr_rk <- plot(pr_rk)
print(gg_pr_rk)

# Since the object returned by plot.cv_proportions() is a standard ggplot2
# plotting object, you can modify the plot easily, e.g., to remove the
# legend:
print(gg_pr_rk + ggplot2::theme(legend.position = "none"))

Description

This is the plot() method for vsel objects (returned by varsel() or cv_varsel()). It visualizes the predictive performance of the reference model (possibly also that of some other "baseline" model) and that of the submodels along the full-data predictor ranking. Basic information about the (CV) variability in the ranking of the predictors is included as well (if available; inferred from cv_proportions()). For a tabular representation, see summary.vsel() and performances().

Usage

## S3 method for class 'vsel'
plot(
  x,
  nterms_max = NULL,
  stats = "elpd",
  deltas = FALSE,
  alpha = 2 * pnorm(-1),
  baseline = if (!inherits(x$refmodel, "datafit")) "ref" else "best",
  thres_elpd = NA,
  resp_oscale = TRUE,
  point_size = 3,
  bar_thickness = 1,
  ranking_nterms_max = NULL,
  ranking_abbreviate = FALSE,
  ranking_abbreviate_args = list(),
  ranking_repel = NULL,
  ranking_repel_args = list(),
  ranking_colored = FALSE,
  show_cv_proportions = TRUE,
  cumulate = FALSE,
  text_angle = NULL,
  size_position = "primary_x_bottom",
  ...
)

Arguments

Details

The stats options "mse" and "rmse" are only available for:

• the traditional projection,

• the latent projection with resp_oscale = FALSE,

• the latent projection with resp_oscale = TRUE in combination with ⁠<refmodel>$family$cats⁠ being NULL.

The stats option "acc" (= "pctcorr") is only available for:

• the binomial() family in case of the traditional projection,

• all families in case of the augmented-data projection,

• the binomial() family (on the original response scale) in case of the latent projection with resp_oscale = TRUE in combination with ⁠<refmodel>$family$cats⁠ being NULL,

• all families (on the original response scale) in case of the latent projection with resp_oscale = TRUE in combination with ⁠<refmodel>$family$cats⁠ being not NULL.

The stats option "auc" is only available for:

• the binomial() family in case of the traditional projection,

• the binomial() family (on the original response scale) in case of the latent projection with resp_oscale = TRUE in combination with ⁠<refmodel>$family$cats⁠ being NULL.

Value

A ggplot2 plotting object (of class gg and ggplot). If ranking_abbreviate is TRUE, the output of abbreviate() is stored in an attribute called projpred_ranking_abbreviated (to allow the abbreviations to be easily mapped back to the original predictor names).

Horizontal lines

As long as the reference model's performance is computable, it is always shown in the plot as a dashed red horizontal line. If baseline = "best", the baseline model's performance is shown as a dotted black horizontal line. If !is.na(thres_elpd) and any(stats %in% c("elpd", "mlpd", "gmpd")), the value supplied to thres_elpd (which is automatically adapted internally in case of the MLPD or the GMPD or deltas = FALSE) is shown as a dot-dashed gray horizontal line for the reference model and, if baseline = "best", as a long-dashed green horizontal line for the baseline model.

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 = 500, refresh = 0, seed = 9876
)

# Run varsel() (here without cross-validation, 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)
print(plot(vs))

Description

After the projection of the reference model onto a submodel, the linear predictors (for the original or a new dataset) based on that submodel can be calculated by proj_linpred(). These linear predictors can also be transformed to response scale and averaged across the projected parameter draws. Furthermore, proj_linpred() returns the corresponding log predictive density values if the (original or new) dataset contains response values. The proj_predict() function draws from the predictive distributions (there is one such distribution for each observation from the original or new dataset) of the submodel that the reference model has been projected onto. If the projection has not been performed yet, both functions call project() internally to perform the projection. Both functions can also handle multiple submodels at once (for objects of class vsel or objects returned by a project() call to an object of class vsel; see project()).

Usage

proj_linpred(
  object,
  newdata = NULL,
  offsetnew = NULL,
  weightsnew = NULL,
  filter_nterms = NULL,
  transform = FALSE,
  integrated = FALSE,
  allow_nonconst_wdraws_prj = return_draws_matrix,
  return_draws_matrix = FALSE,
  .seed = NA,
  ...
)

proj_predict(
  object,
  newdata = NULL,
  offsetnew = NULL,
  weightsnew = NULL,
  filter_nterms = NULL,
  nresample_clusters = 1000,
  return_draws_matrix = FALSE,
  .seed = NA,
  resp_oscale = TRUE,
  ...
)

Arguments

Details

Currently, proj_predict() ignores observation weights that are not equal to 1. A corresponding warning is thrown if this is the case.

In case of the latent projection and transform = FALSE:

• Output element pred contains the linear predictors without any modifications that may be due to the original response distribution (e.g., for a brms::cumulative() model, the ordered thresholds are not taken into account).

• Output element lpd contains the latent log predictive density values, i.e., those corresponding to the latent Gaussian distribution. If newdata is not NULL, this requires the latent response values to be supplied in a column called ⁠.<response_name>⁠ of newdata where ⁠<response_name>⁠ needs to be replaced by the name of the original response variable (if ⁠<response_name>⁠ contained parentheses, these have been stripped off by init_refmodel(); see the left-hand side of ⁠formula(<refmodel>)⁠). For technical reasons, the existence of column ⁠<response_name>⁠ in newdata is another requirement (even though ⁠.<response_name>⁠ is actually used).

Value

In the following, $S_{\mathrm{prj}}$, $N$, $C_{\mathrm{cat}}$, and $C_{\mathrm{lat}}$ from help topic refmodel-init-get are used. (For proj_linpred() with integrated = TRUE, we have $S_{\mathrm{prj}} = 1$.) Furthermore, let $C$ denote either $C_{\mathrm{cat}}$ (if transform = TRUE) or $C_{\mathrm{lat}}$ (if transform = FALSE). Then, if the prediction is done for one submodel only (i.e., length(nterms) == 1 || !is.null(predictor_terms) in the explicit or implicit call to project(), see argument object):

• proj_linpred() returns a list with the following elements:

  • Element pred contains the actual predictions, i.e., the linear predictors, possibly transformed to response scale (depending on argument transform).

  • Element lpd is non-NULL only if newdata is NULL or if newdata contains response values in the corresponding column. In that case, it contains the log predictive density values (conditional on each of the projected parameter draws if integrated = FALSE and averaged across the projected parameter draws if integrated = TRUE).

  In case of (i) the traditional projection, (ii) the latent projection with transform = FALSE, or (iii) the latent projection with transform = TRUE and ⁠<refmodel>$family$cats⁠ (where ⁠<refmodel>⁠ is an object resulting from init_refmodel(); see also extend_family()'s argument latent_y_unqs) being NULL, both elements are $S_{\mathrm{prj}} \times N$ matrices (converted to a—possibly weighted—draws_matrix if argument return_draws_matrix is TRUE, see the description of this argument). In case of (i) the augmented-data projection or (ii) the latent projection with transform = TRUE and ⁠<refmodel>$family$cats⁠ being not NULL, pred is an $S_{\mathrm{prj}} \times N \times C$ array (if argument return_draws_matrix is TRUE, this array is "compressed" to an $S_{\mathrm{prj}} \times (N \cdot C)$ matrix—with the columns consisting of $C$ blocks of $N$ rows—and then converted to a—possibly weighted—draws_matrix) and lpd is an $S_{\mathrm{prj}} \times N$ matrix (converted to a—possibly weighted—draws_matrix if argument return_draws_matrix is TRUE). If return_draws_matrix is FALSE and allow_nonconst_wdraws_prj is TRUE and integrated is FALSE and the projected draws have nonconstant weights, then both list elements have the weights of these draws stored in an attribute wdraws_prj. (If return_draws_matrix, allow_nonconst_wdraws_prj, and integrated are all FALSE, then projected draws with nonconstant weights cause an error.)

• proj_predict() returns an $S_{\mathrm{prj}} \times N$ matrix of predictions where $S_{\mathrm{prj}}$ denotes nresample_clusters in case of clustered projection (or, more generally, in case of projected draws with nonconstant weights). If argument return_draws_matrix is TRUE, the returned matrix is converted to a draws_matrix (see posterior::draws_matrix()). In case of (i) the augmented-data projection or (ii) the latent projection with resp_oscale = TRUE and ⁠<refmodel>$family$cats⁠ being not NULL, the returned matrix (or draws_matrix) has an attribute called cats (the character vector of response categories) and the values of the matrix (or draws_matrix) are the predicted indices of the response categories (these indices refer to the order of the response categories from attribute cats).

If the prediction is done for more than one submodel, the output from above is returned for each submodel, giving a named list with one element for each submodel (the names of this list being the numbers of predictor terms of the submodels when counting the intercept, too).

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 = 500, refresh = 0, seed = 9876
)

# Projection onto an arbitrary combination of predictor terms (with a small
# value for `ndraws`, but only for the sake of speed in this example; this
# is not recommended in general):
prj <- project(fit, predictor_terms = c("X1", "X3", "X5"), ndraws = 21,
               seed = 9182)

# Predictions (at the training points) from the submodel onto which the
# reference model was projected:
prjl <- proj_linpred(prj)
prjp <- proj_predict(prj, .seed = 7364)

Description

This is the predict() method for refmodel objects (returned by get_refmodel() or init_refmodel()). It offers three types of output which are all based on the reference model and new (or old) observations: Either the linear predictor on link scale, the linear predictor transformed to response scale, or the log posterior predictive density.

Usage

## S3 method for class 'refmodel'
predict(
  object,
  newdata = NULL,
  ynew = NULL,
  offsetnew = NULL,
  weightsnew = NULL,
  type = "response",
  ...
)

Arguments

Details

Argument weightsnew is only relevant if !is.null(ynew).

In case of a multilevel reference model, group-level effects for new group levels are drawn randomly from a (multivariate) Gaussian distribution. When setting projpred.mlvl_pred_new to TRUE, all group levels from newdata (even those that already exist in the original dataset) are treated as new group levels (if is.null(newdata), all group levels from the original dataset are considered as new group levels in that case).

Value

In the following, $N$, $C_{\mathrm{cat}}$, and $C_{\mathrm{lat}}$ from help topic refmodel-init-get are used. Furthermore, let $C$ denote either $C_{\mathrm{cat}}$ (if type = "response") or $C_{\mathrm{lat}}$ (if type = "link"). Then, if is.null(ynew), the returned object contains the reference model's predictions (with the scale depending on argument type) as:

• a length-$N$ vector in case of (i) the traditional projection, (ii) the latent projection with type = "link", or (iii) the latent projection with type = "response" and object$family$cats being NULL;

• an $N \times C$ matrix in case of (i) the augmented-data projection or (ii) the latent projection with type = "response" and object$family$cats being not NULL.

If !is.null(ynew), the returned object is a length-$N$ vector of log posterior predictive densities evaluated at ynew.

Predictor terms used in a project() run

Description

For a projection object (returned by project(), possibly as elements of a list), this function extracts the combination of predictor terms onto which the projection was performed.

Usage

predictor_terms(object, ...)

## S3 method for class 'projection'
predictor_terms(object, ...)

Arguments

Value

A character vector of predictor terms.

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 = 500, refresh = 0, seed = 9876
)

# Projection onto an arbitrary combination of predictor terms (with a small
# value for `nclusters`, but only for the sake of speed in this example;
# this is not recommended in general):
prj <- project(fit, predictor_terms = c("X1", "X3", "X5"), nclusters = 10,
               seed = 9182)
print(predictor_terms(prj)) # gives `c("X1", "X3", "X5")`

Print information about project() output

Description

This is the print() method for objects of class projection. This method mainly exists to avoid cluttering the console when printing such objects accidentally.

Usage

## S3 method for class 'projection'
print(x, ...)

Arguments

Value

The input object x (invisible).

Description

This is the print() method for reference model objects (objects of class refmodel). This method mainly exists to avoid cluttering the console when printing such objects accidentally.

Usage

## S3 method for class 'refmodel'
print(x, ...)

Arguments

Value

The input object x (invisible).

Print results (summary) of a varsel() or cv_varsel() run

Description

This is the print() method for vsel objects (returned by varsel() or cv_varsel()). It displays a summary of a varsel() or cv_varsel() run by first calling summary.vsel() and then print.vselsummary().

Usage

## S3 method for class 'vsel'
print(x, digits = getOption("projpred.digits", 2), ...)

Arguments

Value

The output of summary.vsel() (invisible).

Print summary of a varsel() or cv_varsel() run

Description

This is the print() method for summary objects created by summary.vsel(). It displays a summary of the results from a varsel() or cv_varsel() run.

Usage

## S3 method for class 'vselsummary'
print(x, digits = getOption("projpred.digits", 2), ...)

Arguments

Details

In the submodel predictive performance table printed at (or towards) the bottom, column ranking_fulldata contains the full-data predictor ranking and column cv_proportions_diag contains the main diagonal of the matrix returned by cv_proportions() (with cumulate as set in the summary.vsel() call that created x). To retrieve the fold-wise predictor rankings, use the ranking() function, possibly followed by cv_proportions() for computing the ranking proportions (which can be visualized by plot.cv_proportions()).

Value

The output of summary.vsel() (invisible).

Description

Project the posterior of the reference model onto the parameter space of a single submodel consisting of a specific combination of predictor terms or (after variable selection) onto the parameter space of a single or multiple submodels of specific sizes.

Usage

project(
  object,
  nterms = NULL,
  solution_terms = predictor_terms,
  predictor_terms = NULL,
  refit_prj = TRUE,
  ndraws = 400,
  nclusters = NULL,
  seed = NA,
  verbose = getOption("projpred.verbose_project", TRUE),
  ...
)

Arguments

Details

Arguments ndraws and nclusters 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 or nclusters than posterior draws in the reference model may result in slightly inaccurate projection performance. Increasing these arguments affects the computation time linearly.

If refit_prj = FALSE (which is only possible if object is of class vsel), project() retrieves the submodel fits from the full-data search that was run when creating object. Usually, the search relies on a rather coarse clustering or thinning of the reference model's posterior draws (by default, varsel() and cv_varsel() use nclusters = 20). Consequently, project() with refit_prj = FALSE then inherits this coarse clustering or thinning.

Value

If the projection is performed onto a single submodel (i.e., length(nterms) == 1 || !is.null(predictor_terms)), an object of class projection which is a list containing the following elements:

dis

Projected draws for the dispersion parameter.

ce

The cross-entropy part of the Kullback-Leibler (KL) divergence from the reference model to the submodel. For some families, this is not the actual cross-entropy, but a reduced one where terms which would cancel out when calculating the KL divergence have been dropped. In case of the Gaussian family, that reduced cross-entropy is further modified, yielding merely a proxy.

wdraws_prj

Weights for the projected draws.

predictor_terms

A character vector of the submodel's predictor terms.

outdmin

A list containing the submodel fits (one fit per projected draw). This is the same as the return value of the div_minimizer function (see init_refmodel()), except if project() was used with an object of class vsel based on an L1 search as well as with refit_prj = FALSE, in which case this is the output from an internal L1-penalized divergence minimizer.

cl_ref

A numeric vector of length equal to the number of posterior draws in the reference model, containing the cluster indices of these draws.

wdraws_ref

A numeric vector of length equal to the number of posterior draws in the reference model, giving the weights of these draws. These weights should be treated as not being normalized (i.e., they don't necessarily sum to 1).

const_wdraws_prj

A single logical value indicating whether the projected draws have constant weights (TRUE) or not (FALSE).

refmodel

The reference model object.

If the projection is performed onto more than one submodel, the output from above is returned for each submodel, giving a list with one element for each submodel.

The elements of an object of class projection are not meant to be accessed directly but instead via helper functions (see the main vignette and projpred-package; see also as_draws_matrix.projection(), argument return_draws_matrix of proj_linpred(), and argument nresample_clusters of proj_predict() for the intended use of the weights stored in element wdraws_prj).

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 = 500, refresh = 0, seed = 9876
)

# Run varsel() (here without cross-validation, 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)

# Projection onto the best submodel with 2 predictor terms (with a small
# value for `nclusters`, but only for the sake of speed in this example;
# this is not recommended in general):
prj_from_vs <- project(vs, nterms = 2, nclusters = 10, seed = 9182)

# Projection onto an arbitrary combination of predictor terms (with a small
# value for `nclusters`, but only for the sake of speed in this example;
# this is not recommended in general):
prj <- project(fit, predictor_terms = c("X1", "X3", "X5"), nclusters = 10,
               seed = 9182)

Description

Extracts the predictor ranking(s) from an object of class vsel (returned by varsel() or cv_varsel()). A predictor ranking is simply a character vector of predictor terms ranked by predictive relevance (with the most relevant term first). In any case, objects of class vsel contain the predictor ranking based on the full-data search. If an object of class vsel is based on a cross-validation (CV) with fold-wise searches (i.e., if it was created by cv_varsel() with validate_search = TRUE), then it also contains fold-wise predictor rankings.

Usage

ranking(object, ...)

## S3 method for class 'vsel'
ranking(object, nterms_max = NULL, ...)

Arguments

Value

An object of class ranking which is a list with the following elements:

• fulldata: The predictor ranking from the full-data search.

• foldwise: The predictor rankings from the fold-wise searches in the form of a character matrix (only available if object is based on a CV with fold-wise searches, otherwise element foldwise is NULL). The rows of this matrix correspond to the CV folds and the columns to the submodel sizes. Each row contains the predictor ranking from the search of that CV fold.

See Also

cv_proportions()

Examples

# For an example, see `?plot.cv_proportions`.

Description

Function get_refmodel() is a generic function whose methods usually call init_refmodel() which is the underlying workhorse (and may also be used directly without a call to get_refmodel()).

Both, get_refmodel() and init_refmodel(), create an object containing information needed for the projection predictive variable selection, namely about the reference model, the submodels, and how the projection should be carried out. For the sake of simplicity, the documentation may refer to the resulting object also as "reference model" or "reference model object", even though it also contains information about the submodels and the projection.

A "typical" reference model object is created by get_refmodel.stanreg() and brms::get_refmodel.brmsfit(), either implicitly by a call to a top-level function such as project(), varsel(), and cv_varsel() or explicitly by a call to get_refmodel(). All non-"typical" reference model objects will be called "custom" reference model objects.

Some arguments are for $K$-fold cross-validation ($K$-fold CV) only; see cv_varsel() for the use of $K$-fold CV in projpred.

Usage

get_refmodel(object, ...)

## S3 method for class 'refmodel'
get_refmodel(object, ...)

## S3 method for class 'vsel'
get_refmodel(object, ...)

## S3 method for class 'projection'
get_refmodel(object, ...)

## Default S3 method:
get_refmodel(object, family = NULL, ...)

## S3 method for class 'stanreg'
get_refmodel(object, latent = FALSE, dis = NULL, ...)

init_refmodel(
  object,
  data,
  formula,
  family,
  ref_predfun = NULL,
  div_minimizer = NULL,
  proj_predfun = NULL,
  extract_model_data = NULL,
  cvfun = NULL,
  cvfits = NULL,
  dis = NULL,
  cvrefbuilder = NULL,
  called_from_cvrefbuilder = FALSE,
  ...
)

Arguments

Value

An object that can be passed to all the functions that take the reference model fit as the first argument, such as varsel(), cv_varsel(), project(), proj_linpred(), and proj_predict(). Usually, the returned object is of class refmodel. However, if object is NULL, the returned object is of class datafit as well as of class refmodel (with datafit being first). Objects of class datafit are handled differently at several places throughout this package.

The elements of the returned object are not meant to be accessed directly but instead via downstream functions (see the functions mentioned above as well as predict.refmodel()).

Formula terms

Although bad practice (in general), a reference model lacking an intercept can be used within projpred. However, it will always be projected onto submodels which include an intercept. The reason is that even if the true intercept in the reference model is zero, this does not need to hold for the submodels.

In multilevel (group-level) terms, function calls on the right-hand side of the | character (e.g., (1 | gr(group_variable)), which is possible in brms) are currently not allowed in projpred.

For additive models (still an experimental feature), only mgcv::s() and mgcv::t2() are currently supported as smooth terms. Furthermore, these need to be called without any arguments apart from the predictor names (symbols). For example, for smoothing the effect of a predictor x, only s(x) or t2(x) are allowed. As another example, for smoothing the joint effect of two predictors x and z, only s(x, z) or t2(x, z) are allowed (and analogously for higher-order joint effects, e.g., of three predictors). Note that all smooth terms need to be included in formula (there is no random argument as in rstanarm::stan_gamm4(), for example).

Arguments ref_predfun, proj_predfun, and div_minimizer

Arguments ref_predfun, proj_predfun, and div_minimizer may be NULL for using an internal default (see projpred-package for the functions used by the default divergence minimizers). Otherwise, let $N$ denote the number of observations (in case of CV, these may be reduced to each fold), $S_{\mathrm{ref}}$ the number of posterior draws for the reference model's parameters, and $S_{\mathrm{prj}}$ the number of draws for the parameters of a submodel that the reference model has been projected onto (short: the number of projected draws). For the augmented-data projection, let $C_{\mathrm{cat}}$ denote the number of response categories, $C_{\mathrm{lat}}$ the number of latent response categories (which typically equals $C_{\mathrm{cat}} - 1$), and define $N_{\mathrm{augcat}} := N \cdot C_{\mathrm{cat}}$ as well as $N_{\mathrm{auglat}} := N \cdot C_{\mathrm{lat}}$. Then the functions supplied to these arguments need to have the following prototypes:

• ref_predfun: ref_predfun(fit, newdata = NULL) where:

  • fit accepts the reference model fit as given in argument object (but possibly refitted to a subset of the observations, as done in $K$-fold CV).

  • newdata accepts either NULL (for using the original dataset, typically stored in fit) or data for new observations (at least in the form of a data.frame).

• proj_predfun: proj_predfun(fits, newdata) where:

  • fits accepts a list of length $S_{\mathrm{prj}}$ containing this number of submodel fits. This list is the same as that returned by project() in its output element outdmin (which in turn is the same as the return value of div_minimizer, except if project() was used with an object of class vsel based on an L1 search as well as with refit_prj = FALSE).

  • newdata accepts data for new observations (at least in the form of a data.frame).

• div_minimizer does not need to have a specific prototype, but it needs to be able to be called with the following arguments:

  • formula accepts either a standard formula with a single response (if $S_{\mathrm{prj}} = 1$ or in case of the augmented-data projection) or a formula with $S_{\mathrm{prj}} > 1$ response variables cbind()-ed on the left-hand side in which case the projection has to be performed for each of the response variables separately.

  • data accepts a data.frame to be used for the projection. In case of the traditional or the latent projection, this dataset has $N$ rows. In case of the augmented-data projection, this dataset has $N_{\mathrm{augcat}}$ rows.

  • family accepts an object of class family.

  • weights accepts either observation weights (at least in the form of a numeric vector) or NULL (for using a vector of ones as weights).

  • projpred_var accepts an $N \times S_{\mathrm{prj}}$ matrix of predictive variances (necessary for projpred's internal GLM fitter) in case of the traditional or the latent projection and an $N_{\mathrm{augcat}} \times S_{\mathrm{prj}}$ matrix (containing only NAs) in case of the augmented-data projection.

  • projpred_ws_aug accepts an $N \times S_{\mathrm{prj}}$ matrix of expected values for the response in case of the traditional or the latent projection and an $N_{\mathrm{augcat}} \times S_{\mathrm{prj}}$ matrix of probabilities for the response categories in case of the augmented-data projection.

  • ... accepts further arguments specified by the user (or by projpred).

The return value of these functions needs to be:

• ref_predfun: for the traditional or the latent projection, an $N \times S_{\mathrm{ref}}$ matrix; for the augmented-data projection, an $S_{\mathrm{ref}} \times N \times C_{\mathrm{lat}}$ array (the only exception is the augmented-data projection for the binomial() family in which case ref_predfun needs to return an $N \times S_{\mathrm{ref}}$ matrix just like for the traditional projection because the array is constructed by an internal wrapper function).

• proj_predfun: for the traditional or the latent projection, an $N \times S_{\mathrm{prj}}$ matrix; for the augmented-data projection, an $N \times C_{\mathrm{lat}} \times S_{\mathrm{prj}}$ array.

• div_minimizer: a list of length $S_{\mathrm{prj}}$ containing this number of submodel fits.

Argument extract_model_data

The function supplied to argument extract_model_data needs to have the prototype

extract_model_data(object, newdata, wrhs = NULL, orhs = NULL,
                   extract_y = TRUE)

where:

• object accepts the reference model fit as given in argument object (but possibly refitted to a subset of the observations, as done in $K$-fold CV).

• newdata accepts data for new observations (at least in the form of a data.frame).

• wrhs accepts at least (i) a right-hand side formula consisting only of the variable in newdata containing the observation weights or (ii) NULL for using the observation weights corresponding to newdata (typically, the observation weights are stored in a column of newdata; if the model was fitted without observation weights, a vector of ones should be used).

• orhs accepts at least (i) a right-hand side formula consisting only of the variable in newdata containing the offsets or (ii) NULL for using the offsets corresponding to newdata (typically, the offsets are stored in a column of newdata; if the model was fitted without offsets, a vector of zeros should be used).

• extract_y accepts a single logical value indicating whether output element y (see below) shall be NULL (TRUE) or not (FALSE).

The return value of extract_model_data needs to be a list with elements y, weights, and offset, each being a numeric vector containing the data for the response, the observation weights, and the offsets, respectively. An exception is that y may also be NULL (depending on argument extract_y), a non-numeric vector, or a factor.

The weights and offsets returned by extract_model_data will be assumed to hold for the reference model as well as for the submodels.

Above, arguments wrhs and orhs were assumed to have defaults of NULL. It should be possible to use defaults other than NULL, but we strongly recommend to use NULL. If defaults other than NULL are used, they need to imply the behaviors described at items "(ii)" (see the descriptions of wrhs and orhs).

Augmented-data projection

If a custom reference model for an augmented-data projection is needed, see also extend_family().

For the augmented-data projection, the response vector resulting from extract_model_data is internally coerced to a factor (using as.factor()). The levels of this factor have to be identical to family$cats (after applying extend_family() internally; see extend_family()'s argument augdat_y_unqs).

Note that response-specific offsets (i.e., one length-$N$ offset vector per response category) are not supported by projpred yet. So far, only offsets which are the same across all response categories are supported. This is why in case of the brms::categorical() family, offsets are currently not supported at all.

Currently, object = NULL (i.e., a datafit; see section "Value") is not supported in case of the augmented-data projection.

Latent projection

If a custom reference model for a latent projection is needed, see also extend_family().

For the latent projection, family$cats (after applying extend_family() internally; see extend_family()'s argument latent_y_unqs) currently must not be NULL if the original (i.e., non-latent) response is a factor. Conversely, if family$cats (after applying extend_family()) is non-NULL, the response vector resulting from extract_model_data is internally coerced to a factor (using as.factor()). The levels of this factor have to be identical to that non-NULL element family$cats.

Currently, object = NULL (i.e., a datafit; see section "Value") is not supported in case of the latent projection.

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 = 500, refresh = 0, seed = 9876
)

# Define the reference model object explicitly:
ref <- get_refmodel(fit)
print(class(ref)) # gives `"refmodel"`
# Now see, for example, `?varsel`, `?cv_varsel`, and `?project` for
# possible post-processing functions. Most of the post-processing functions
# call get_refmodel() internally at the beginning, so you will rarely need
# to call get_refmodel() yourself.

# A custom reference model object which may be used in a variable selection
# where the candidate predictors are not a subset of those used for the
# reference model's predictions:
ref_cust <- init_refmodel(
  fit,
  data = dat_gauss,
  formula = y ~ X6 + X7,
  family = gaussian(),
  cvfun = function(folds) {
    kfold(
      fit, K = max(folds), save_fits = TRUE, folds = folds, cores = 1
    )$fits[, "fit"]
  },
  dis = as.matrix(fit)[, "sigma"],
  cvrefbuilder = function(cvfit) {
    init_refmodel(cvfit,
                  data = dat_gauss[-cvfit$omitted, , drop = FALSE],
                  formula = y ~ X6 + X7,
                  family = gaussian(),
                  dis = as.matrix(cvfit)[, "sigma"],
                  called_from_cvrefbuilder = TRUE)
  }
)
# Now, the post-processing functions mentioned above (for example,
# varsel(), cv_varsel(), and project()) may be applied to `ref_cust`.

Description

A helper function that can be used to create input for cv_varsel.refmodel()'s argument cvfits by running first cv_folds() and then the reference model object's cvfun (see init_refmodel()). This is helpful if $K$-fold CV is run multiple times based on the same $K$ reference model refits.

Usage

run_cvfun(object, ...)

## Default S3 method:
run_cvfun(object, ...)

## S3 method for class 'refmodel'
run_cvfun(
  object,
  K = if (!inherits(object, "datafit")) 5 else 10,
  folds = NULL,
  seed = NA,
  ...
)

Arguments

Value

An object that can be used as input for cv_varsel.refmodel()'s argument cvfits.

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 = 500, refresh = 0, seed = 9876
)

# Define the reference model object explicitly (not really necessary here
# because the get_refmodel() call is quite fast in this example, but in
# general, this approach is faster than defining the reference model object
# multiple times implicitly):
ref <- get_refmodel(fit)

# Run the reference model object's `cvfun` (with a small value for `K`, but
# only for the sake of speed in this example; this is not recommended in
# general):
cv_fits <- run_cvfun(ref, K = 2, seed = 184)

# Run cv_varsel() (with L1 search and small values for `nterms_max` and
# `nclusters_pred`, but only for the sake of speed in this example; this is
# not recommended in general) and use `cv_fits` there:
cvvs_L1 <- cv_varsel(ref, method = "L1", cv_method = "kfold",
                     cvfits = cv_fits, nterms_max = 3, nclusters_pred = 10,
                     seed = 5555)
# Now see, for example, `?print.vsel`, `?plot.vsel`, `?suggest_size.vsel`,
# and `?ranking` for possible post-processing functions.

# The purpose of run_cvfun() is to create an object that can be used in
# multiple cv_varsel() calls, e.g., to check the sensitivity to the search
# method (L1 or forward):
cvvs_fw <- cv_varsel(ref, method = "forward", cv_method = "kfold",
                     cvfits = cv_fits, nterms_max = 3, nclusters = 5,
                     nclusters_pred = 10, seed = 5555)

# Stratified K-fold CV is straightforward:
n_strat <- 3L
set.seed(692)
# Some example strata:
strat_fac <- sample(paste0("lvl", seq_len(n_strat)), size = nrow(dat_gauss),
                    replace = TRUE,
                    prob = diff(c(0, pnorm(seq_len(n_strat - 1L) - 0.5), 1)))
table(strat_fac)
# Use loo::kfold_split_stratified() to create the folds vector:
folds_strat <- loo::kfold_split_stratified(K = 2, x = strat_fac)
table(folds_strat, strat_fac)
# Call run_cvfun(), but this time with argument `folds` instead of `K` (here,
# specifying argument `seed` would not be necessary because of the set.seed()
# call above, but we specify it nonetheless for the sake of generality):
cv_fits_strat <- run_cvfun(ref, folds = folds_strat, seed = 391)
# Now use `cv_fits_strat` analogously to `cv_fits` from above.

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

Description

The solution_terms.vsel() method retrieves the solution path from a full-data search (vsel objects are returned by varsel() or cv_varsel()). The solution_terms.projection() method retrieves the predictor combination onto which a projection was performed (projection objects are returned by project(), possibly as elements of a list). Both methods (and hence also the solution_terms() generic) are deprecated and will be removed in a future release. Please use ranking() instead of solution_terms.vsel() (ranking()'s output element fulldata contains the full-data predictor ranking that is extracted by solution_terms.vsel(); ranking()'s output element foldwise contains the fold-wise predictor rankings—if available—which were previously not accessible via a built-in function) and predictor_terms() instead of solution_terms.projection().

Usage

solution_terms(object, ...)

## S3 method for class 'vsel'
solution_terms(object, ...)

## S3 method for class 'projection'
solution_terms(object, ...)

Arguments

Value

A character vector of predictor terms.

Description

This function can suggest an appropriate submodel size based on a decision rule described in section "Details" below. Note that this decision is quite heuristic and should be interpreted with caution. It is recommended to examine the results via plot.vsel(), cv_proportions(), plot.cv_proportions(), and/or summary.vsel() and to make the final decision based on what is most appropriate for the problem at hand.

Usage

suggest_size(object, ...)

## S3 method for class 'vsel'
suggest_size(
  object,
  stat = "elpd",
  pct = 0,
  type = "upper",
  thres_elpd = NA,
  warnings = TRUE,
  ...
)

Arguments

Details

In general (beware of special cases below), the suggested model size is the smallest model size $j \in \{0, 1, ..., \texttt{nterms\_max}\}$ for which either the lower or upper bound (depending on argument type) of the normal-approximation (or bootstrap or exponentiated normal-approximation; see argument stat) confidence interval (with nominal coverage 1 - alpha; see argument alpha of summary.vsel()) for $U_j - U_{\mathrm{base}}$ (with $U_j$ denoting the $j$-th submodel's true utility and $U_{\mathrm{base}}$ denoting the baseline model's true utility) falls above (or is equal to)

$\texttt{pct} \cdot (u_0 - u_{\mathrm{base}})$

where $u_0$ denotes the null model's estimated utility and $u_{\mathrm{base}}$ the baseline model's estimated utility. The baseline model is either the reference model or the best submodel found (see argument baseline of summary.vsel()).

In doing so, loss statistics like the root mean squared error (RMSE) and the mean squared error (MSE) are converted to utilities by multiplying them by -1, so a call such as suggest_size(object, stat = "rmse", type = "upper") finds the smallest model size whose upper confidence interval bound for the negative RMSE or MSE exceeds (or is equal to) the cutoff (or, equivalently, has the lower confidence interval bound for the RMSE or MSE below—or equal to—the cutoff). This is done to make the interpretation of argument type the same regardless of argument stat.

For the geometric mean predictive density (GMPD), the decision rule above is applied on log() scale. In other words, if the true GMPD is denoted by $U^\ast_j$ for the $j$-th submodel and $U^\ast_{\mathrm{base}}$ for the baseline model (so that $U_j$ and $U_{\mathrm{base}}$ from above are given by $U_j = \log(U^\ast_j)$ and $U_{\mathrm{base}} = \log(U^\ast_{\mathrm{base}})$), then suggest_size() yields the smallest model size whose lower or upper (depending on argument type) confidence interval bound for $\frac{U^\ast_j}{U^\ast_{\mathrm{base}}}$ exceeds (or is equal to)

$(\frac{u^\ast_0}{u^\ast_{\mathrm{base}}})^{\texttt{pct}}$

where $u^\ast_0$ denotes the null model's estimated GMPD and $u^\ast_{\mathrm{base}}$ the baseline model's estimated GMPD.

If !is.na(thres_elpd) and stat = "elpd", the decision rule above is extended: The suggested model size is then the smallest model size $j$ fulfilling the rule above or $u_j - u_{\mathrm{base}} > \texttt{thres\_elpd}$. Correspondingly, in case of stat = "mlpd" (and !is.na(thres_elpd)), the suggested model size is the smallest model size $j$ fulfilling the rule above or $u_j - u_{\mathrm{base}} > \frac{\texttt{thres\_elpd}}{N}$ with $N$ denoting the number of observations. Correspondingly, in case of stat = "gmpd" (and !is.na(thres_elpd)), the suggested model size is the smallest model size $j$ fulfilling the rule above or $\frac{u^\ast_j}{u^\ast_{\mathrm{base}}} > \exp(\frac{\texttt{thres\_elpd}}{N})$.

For example (disregarding the special extensions in case of !is.na(thres_elpd) with stat %in% c("elpd", "mlpd", "gmpd")), alpha = 2 * pnorm(-1), pct = 0, and type = "upper" means that we select the smallest model size for which the upper bound of the 1 - 2 * pnorm(-1) (approximately 68.3%) confidence interval for $U_j - U_{\mathrm{base}}$ ($\frac{U^\ast_j}{U^\ast_{\mathrm{base}}}$ in case of the GMPD) exceeds (or is equal to) zero (one in case of the GMPD), that is (if stat is a performance statistic for which the normal approximation is used, not the bootstrap and not the exponentiated normal approximation), for which the submodel's utility estimate is at most one standard error smaller than the baseline model's utility estimate (with that standard error referring to the utility difference).