Date: 2025-12-05

# projpred: Projection predictive feature selection

#### 2025-12-05

## Introduction

This vignette illustrates the main functionalities of the projpred package, which implements the projection predictive variable selection for various regression models (see section “Supported types of models” below for more details on supported model types). What is special about the projection predictive variable selection is that it not only performs a variable selection, but also allows for (approximately) valid post-selection inference.

The projection predictive variable selection is based on the ideas of Goutis and Robert (1998) and Dupuis and Robert (2003). The methods implemented in projpred are described in detail in Piironen, Paasiniemi, and Vehtari (2020), Catalina, Bürkner, and Vehtari (2022), Weber, Glass, and Vehtari (2025), and Catalina, Bürkner, and Vehtari (2021). A comparison to many other methods may also be found in Piironen and Vehtari (2017a). An introduction to the theory behind projpred, a workflow for practitioners, and further insights into the theory (and practice) of projection predictive inference are presented by McLatchie et al. (2025). For details on how to cite projpred, see the projpred citation info on CRAN1.

## Data

For this vignette, we use projpred’s `df_gaussian` data. It contains 100 observations of 20 continuous predictor variables `X1`, …, `X20` (originally stored in a sub-matrix; we turn them into separate columns below) and one continuous response variable `y`.

    data("df_gaussian", package = "projpred")
    dat_gauss <- data.frame(y = df_gaussian$y, df_gaussian$x)

## Reference model

First, we have to construct a reference model for the projection predictive variable selection. This model is considered as the best (“reference”) solution to the prediction task. The aim of the projection predictive variable selection is to find a subset of a set of candidate predictors which is as small as possible but achieves a predictive performance as close as possible to that of the reference model.

Usually (and this is also the case in this vignette), the reference model will be an rstanarm or brms fit. To our knowledge, rstanarm and brms are currently the only packages for which a `get_refmodel()` method (which establishes the compatibility with projpred) exists. Creating a reference model object via one of these methods `get_refmodel.stanreg()` or `brms::get_refmodel.brmsfit()` (either implicitly by a call to a top-level function such as `project()`, `varsel()`, and `cv_varsel()`, as done below, or explicitly by a call to `get_refmodel()`) leads to a “typical” reference model object. In that case, all candidate models are actual submodels of the reference model. In general, however, this assumption is not necessary for a projection predictive variable selection (see, e.g., Piironen, Paasiniemi, and Vehtari 2020). This is why “custom” (i.e., non-“typical”) reference model objects allow to avoid this assumption (although the candidate models of a “custom” reference model object will still be actual submodels of the full `formula` used by the search procedure—which does not have to be the same as the reference model’s `formula`, if the reference model possesses a `formula` at all). Such “custom” reference model objects can be constructed via `init_refmodel()` (or `get_refmodel.default()`), as shown in section “Examples” of the `?init_refmodel` help.

Here, we use the rstanarm package to fit the reference model. If you want to use the brms package, simply replace the rstanarm fit (of class `stanreg`) in all the code below by your brms fit (of class `brmsfit`).

    library(rstanarm)

For our rstanarm reference model, we use the Gaussian distribution as the `family` for our response. With respect to the predictors, we only include the linear main effects of all 20 predictor variables. Compared to the more complex types of reference models supported by projpred (see section “Supported types of models” below), this is a quite simple reference model which is sufficient, however, to demonstrate the interplay of projpred’s functions.

We use rstanarm’s default priors in our reference model, except for the regression coefficients for which we use a regularized horseshoe prior (Piironen and Vehtari 2017c) with the hyperprior for its global shrinkage parameter following Piironen and Vehtari (2017b) and Piironen and Vehtari (2017c). In R code, these are the preparation steps for the regularized horseshoe prior:

    # Number of regression coefficients:
    ( D <- sum(grepl("^X", names(dat_gauss))) )
    # Prior guess for the number of relevant (i.e., non-zero) regression
    # coefficients:
    p0 <- 5
    # Number of observations:
    N <- nrow(dat_gauss)
    # Hyperprior scale for tau, the global shrinkage parameter (note that for the
    # Gaussian family, 'rstanarm' will automatically scale this by the residual
    # standard deviation):
    tau0 <- p0 / (D - p0) * 1 / sqrt(N)

We now fit the reference model to the data. To make this vignette build faster, we use only 2 MCMC chains and 1000 iterations per chain (with half of them being discarded as warmup draws). In practice, 4 chains and 2000 iterations per chain are reasonable defaults. Furthermore, we make use of rstan’s parallelization, which means to run each chain on a separate CPU core2. If you run the following code yourself, you can either rely on an automatic mechanism to detect the number of CPU cores (like the `parallel::detectCores()` function shown below) or adapt `ncores` manually to your system.

    # Set this manually if desired:
    ncores <- parallel::detectCores(logical = FALSE)
    ### Only for technical reasons in this vignette (you can omit this when running
    ### the code yourself):
    ncores <- min(ncores, 2L)
    ###
    options(mc.cores = ncores)
    set.seed(50780)
    refm_fit <- stan_glm(
      y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10 + X11 + X12 + X13 + X14 +
        X15 + X16 + X17 + X18 + X19 + X20,
      family = gaussian(),
      data = dat_gauss,
      prior = hs(global_scale = tau0),
      ### Only for the sake of speed (not recommended in general):
      chains = 2, iter = 1000,
      ###
      refresh = 0
    )

Usually, we would now have to check the convergence diagnostics (see, e.g., `?posterior::diagnostics` and `?posterior::default_convergence_measures`). However, due to the technical reasons for which we reduced `chains` and `iter`, we skip this step here.

## Variable selection

Now, projpred comes into play.

    library(projpred)

From the reference model fit (called `refm_fit` here), we create a reference model object (i.e., an object of class `refmodel`) since this avoids redundant calculations in the remainder of this vignette3:

    refm_obj <- get_refmodel(refm_fit)

In projpred, the projection predictive variable selection relies on a so-called search part and a so-called evaluation part. The search part determines the predictor ranking (also known as solution path), i.e., the best submodel for each submodel size (the size is given by the number of predictor terms). The evaluation part determines the predictive performance of the increasingly complex submodels along the predictor ranking.

There are two functions for running the combination of search and evaluation: `varsel()` and `cv_varsel()`. In contrast to `varsel()`, `cv_varsel()` performs a cross-validation (CV). With `cv_method = "LOO"` (the default), `cv_varsel()` runs a Pareto-smoothed importance sampling leave-one-out CV (PSIS-LOO CV, see Vehtari, Gelman, and Gabry 2017; Vehtari et al. 2022). With `cv_method = "kfold"`, `cv_varsel()` runs a \(K\)-fold CV. The extent of the CV mainly depends on `cv_varsel()`’s argument `validate_search`: If `validate_search = TRUE` (the default), the search part is run with the training data of each CV fold separately and the evaluation part is run with the corresponding test data of each CV fold. If `validate_search = FALSE`, the search is excluded from the CV so that only a single full-data search is run. Because of its more thorough protection against overfitting4, `cv_varsel()` with `validate_search = TRUE` is recommended over `varsel()` and `cv_varsel()` with `validate_search = FALSE`. Nonetheless, a preliminary and comparatively fast run of `varsel()` or `cv_varsel()` with `validate_search = FALSE` can give a rough idea of the performance of the submodels and can be used for finding a suitable value for argument `nterms_max` in subsequent runs (argument `nterms_max` imposes a limit on the submodel size up to which the search is continued and is thus able to reduce the runtime considerably).

### Preliminary `cv_varsel()` run

To illustrate a preliminary `cv_varsel()` run with `validate_search = FALSE`, we set `nterms_max` to the number of predictor terms in the full model, i.e., `nterms_max = 20`. To speed up the building of the vignette (this is not recommended in general), we choose the `"L1"` search `method` and set `refit_prj` to `FALSE`.

    # Preliminary cv_varsel() run:
    cvvs_fast <- cv_varsel(
      refm_obj,
      validate_search = FALSE,
      ### Only for the sake of speed (not recommended in general):
      method = "L1",
      refit_prj = FALSE,
      ###
      nterms_max = 20,
      ### In interactive use, we recommend not to deactivate the verbose mode:
      verbose = 0
      ###
    )

To find a suitable value for `nterms_max` in subsequent `cv_varsel()` runs, we take a look at a plot of at least one predictive performance statistic in dependence of the submodel size. Here, we choose the mean log predictive density (MLPD; see the documentation for argument `stats` of `summary.vsel()` for details) as the only performance statistic. Since we will be using the following plot only to determine `nterms_max` for subsequent `cv_varsel()` runs, we can omit the predictor ranking from the plot by setting `ranking_nterms_max` to `NA` (which requires `size_position = "primary_x_bottom"`, which we set here via its global option, just like argument `text_angle`):

    options(projpred.plot_vsel_size_position = "primary_x_bottom")
    options(projpred.plot_vsel_text_angle = 0)
    plot(cvvs_fast, stats = "mlpd", ranking_nterms_max = NA)

This plot suggests that the submodel MLPD levels off from submodel size 8 on. However, we used L1 search with `refit_prj = FALSE`, which means that the projections employed for the predictive performance evaluation are L1-penalized, which is usually undesired (Piironen, Paasiniemi, and Vehtari 2020, sec. 4). Thus, to investigate the impact of `refit_prj = FALSE`, we re-run `cv_varsel()`, but this time with the default of `refit_prj = TRUE` and re-using the search results (as well as the CV-related arguments such as `validate_search`; see section “Usage” in `?cv_varsel.vsel`) from the former `cv_varsel()` call (this is done by applying `cv_varsel()` to `cvvs_fast` instead of `refm_obj` so that the `cv_varsel()` generic dispatches to the `cv_varsel.vsel()` method that was introduced in projpred 2.8.05). To save time, we also set `nclusters_pred` to a comparatively low value of `20`:

    # Preliminary cv_varsel() run with `refit_prj = TRUE`:
    cvvs_fast_refit <- cv_varsel(
      cvvs_fast,
      ### Only for the sake of speed (not recommended in general):
      nclusters_pred = 20,
      ###
      ### In interactive use, we recommend not to deactivate the verbose mode:
      verbose = 0
      ###
    )

Here, we ignore the warning that SIS is used (instead of PSIS) because this is due to `nclusters_pred = 20` which we used only to speed up the building of the vignette.

With the `refit_prj = TRUE` results, the predictive performance plot from above now looks as follows:

    plot(cvvs_fast_refit, stats = "mlpd", ranking_nterms_max = NA)

This refined plot confirms that the submodel MLPD does not change much after submodel size 8, so in our final `cv_varsel()` run, we set `nterms_max` to a value slightly higher than 8 (here: 9) to ensure that we see the MLPD leveling off. The search results from the initial `cvvs_fast` object could now be re-used again (via `cv_varsel.vsel()`) for investigating the sensitivity of the results to changes in `nclusters_pred` (or `ndraws_pred`). Here, we skip this for the sake of brevity and instead head over to the final `cv_varsel()` run.

### Final `cv_varsel()` run

For this final `cv_varsel()` run (with `validate_search = TRUE`, as recommended), we use a \(K\)-fold CV with a small number of folds (`K = 2`) to make this vignette build faster. In practice, we recommend using either the default of `cv_method = "LOO"` (possibly subsampled, see argument `nloo` of `cv_varsel()`) or a larger value for `K` if this is possible in terms of computation time. Here, we also perform the \(K\) reference model refits outside of `cv_varsel()`. Although not strictly necessary here, this is helpful in practice because often, `cv_varsel()` needs to be re-run multiple times in order to try out different argument settings. We also show how projpred’s CV (i.e., the CV comprising search and performance evaluation, after refitting the reference model \(K\) times) can be parallelized (though we disable it here for vignette building). In practice, parallelization is of little use with only `K = 2` folds and fast fold-wise operations, as the parallelization overhead eats up any runtime improvements. Note that for this final `cv_varsel()` run, we cannot make use of `cv_varsel.vsel()` (by applying `cv_varsel()` to `cvvs_fast` or `cvvs_fast_refit` instead of `refm_obj`) because of the change in `nterms_max` (here: 9, for `cvvs_fast` and `cvvs_fast_refit`: 20).

    # Refit the reference model K times:
    cv_fits <- run_cvfun(
      refm_obj,
      ### Only for the sake of speed (not recommended in general):
      K = 2
      ###
    )
    # For running projpred's CV in parallel (see cv_varsel()'s argument `parallel`):
    # Note: Parallel processing is disabled during package building to avoid issues
    use_parallel <- FALSE  # Set to TRUE for actual parallel processing
    if (use_parallel) {
      doParallel::registerDoParallel(ncores)
    }
    # Final cv_varsel() run:
    cvvs <- cv_varsel(
      refm_obj,
      cv_method = "kfold",
      cvfits = cv_fits,
      ### Only for the sake of speed (not recommended in general):
      method = "L1",
      nclusters_pred = 20,
      ###
      nterms_max = 9,
      parallel = use_parallel,
      ### In interactive use, we recommend not to deactivate the verbose mode:
      verbose = 0
      ###
    )
    # Tear down the CV parallelization setup:
    if (use_parallel) {
      doParallel::stopImplicitCluster()
      foreach::registerDoSEQ()
    }

#### Predictive performance plot from final `cv_varsel()` run

We can now select a final submodel size by looking at a predictive performance plot similar to the one created for the preliminary `cv_varsel()` run above. By default, the performance statistics are plotted on their actual scale and the uncertainty bars match this scale, but argument `deltas` of `plot.vsel()` offers two more options:

- With `deltas = TRUE`, the performance statistics are plotted on difference scale, i.e., as differences6 from the baseline model7 and the uncertainty bars match this scale,
- With `deltas = "mixed"`, the performance statistics (i.e., their point estimates) are plotted on the actual scale, but the uncertainty bars visualize the difference-scale uncertainty.

Since the difference-scale uncertainty is usually more helpful than the actual-scale uncertainty (at least with regard to the decision for a final submodel size), we plot with `deltas = TRUE` here (`deltas = "mixed"` would be another good choice). We also set `show_cv_proportions = TRUE` (via its global option) for illustrative purposes, which becomes clearer in section “Predictor ranking(s) from final `cv_varsel()` run and identification of the selected submodel”:

    options(projpred.plot_vsel_show_cv_proportions = TRUE)
    plot(cvvs, stats = "mlpd", deltas = TRUE)

#### Decision for final submodel size

Based on that final predictive performance plot, we decide for a submodel size. Usually, the aim is to find the smallest submodel size where the predictive performance of the submodels levels off and is close enough to the reference model’s predictive performance (the dashed red horizontal line).

Sometimes, the plot may be ambiguous because after reaching the reference model’s performance, the submodels’ performance may keep increasing (and hence become even better than the reference model’s performance8). In that case, one has to find a suitable trade-off between predictive performance (accuracy) and model size (sparsity) in the context of subject-matter knowledge.

Here, we decide for a submodel size of 7 because it seems to provide the best trade-off between sparsity and accuracy (size 7 is the smallest size where the submodel MLPD is close enough to the reference model MLPD and from size 7 on, the submodel MLPD levels off).

    size_decided <- 7

In section “Predictor ranking(s) from final `cv_varsel()` run and identification of the selected submodel”, the predictor ranking and the (CV) ranking proportions that are shown in the plot (below the submodel sizes on the x-axis) are explained in detail—and also how they could have been incorporated into our decision for a submodel size.

The `suggest_size()` function offered by projpred may help in the decision for a submodel size, but this is a rather heuristic method and needs to be interpreted with caution (see `?suggest_size`):

    suggest_size(cvvs, stat = "mlpd")

In this case, that heuristic gives the same final submodel size (`7`) as our manual decision.

#### Predictive performance table from final `cv_varsel()` run

A tabular representation of the plot created by `plot.vsel()` can be achieved via `summary.vsel()` or `performances()`. In contrast to `performances()`, the output of `summary.vsel()` contains more information than just the predictive performance results, which is also why there is a sophisticated `print()` method for objects of class `vselsummary` (the output of `summary.vsel()`). This method `print.vselsummary()` is also called by the shortcut method `print.vsel()` which can be applied to an object resulting from `varsel()` or `cv_varsel()`.

Specifically, to create the table matching the predictive performance plot above as closely as possible (and to also adjust the minimum number of printed significant digits), we may call `summary.vsel()` and `print.vselsummary()` as follows:

    smmry <- summary(cvvs,
                     stats = "mlpd",
                     type = c("mean", "lower", "upper"),
                     deltas = TRUE)
    print(smmry, digits = 1)

The generic function `performances()` (with main method `performances.vselsummary()` and the shortcut method `performances.vsel()`) essentially extracts the predictive performance results from the output of `summary.vsel()`:

    perf <- performances(smmry)
    str(perf)

#### Predictor ranking(s) from final `cv_varsel()` run and identification of the selected submodel

As indicated by its column name, the predictor ranking from column `ranking_fulldata` of the `summary.vsel()` output is based on the full-data search. This full-data predictor ranking is also what is shown in the second line of the x-axis tick labels of the predictive performance plot from section “Predictive performance plot from final `cv_varsel()` run”.

In case of `cv_varsel()` with `validate_search = TRUE`, there is not only the full-data search, but also fold-wise searches, implying that there are also fold-wise predictor rankings. All of these predictor rankings (the full-data one and—if available—the fold-wise ones) can be retrieved via `ranking()`:

    rk <- ranking(cvvs)

In addition to inspecting the full-data predictor ranking, it often makes sense to investigate the ranking proportions derived from the fold-wise predictor rankings (only available in case of `cv_varsel()` with `validate_search = TRUE`, which we have here) in order to get a sense for the variability in the ranking of the predictors. 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. To compute these ranking proportions, we use `cv_proportions()`:

    ( pr_rk <- cv_proportions(rk) )

The main diagonal of this matrix is contained in column `cv_proportions_diag` of the `summary.vsel()` output and is also shown in the third line of the x-axis tick labels of the predictive performance plot from section “Predictive performance plot from final `cv_varsel()` run” (due to `show_cv_proportions = TRUE` set via its global option).

Here, the ranking proportions are of little use as we have used `K = 2` (in the final `cv_varsel()` call above) for the sake of speed. Nevertheless, we can see that the two CV folds agree on the most relevant predictor term (`X1`) and the second most relevant predictor term (`X14`). Since the column names of the matrix returned by `cv_proportions()` follow the full-data predictor ranking, we can infer that `X1` and `X14` are also the most relevant predictor terms (in this order) in the full-data predictor ranking. To see this more explicitly, we can access element `fulldata` of the `ranking()` output:

    rk[["fulldata"]]

This is the same as column `ranking_fulldata` in the `summary.vsel()` output above (apart from the intercept).

Note that we have cut off the search at `nterms_max = 9` (which is smaller than the number of predictor terms in the full model, 20 here), so the ranking proportions in the `pr_rk` matrix do not need to sum to 100 % (neither column-wise nor row-wise).

The transposed matrix of ranking proportions can be visualized via `plot.cv_proportions()`:

    plot(pr_rk)

Apart from visualizing the variability in the ranking of the predictors (here, this is of little use because of `K = 2`), this plot will be helpful later.

To retrieve the predictor terms of the final submodel (except for the intercept which is always included in the submodels), we combine the chosen submodel size of 7 with the full-data predictor ranking:

    ( predictors_final <- head(rk[["fulldata"]], size_decided) )

At this place, it is again helpful to take the ranking proportions into account, but now in a cumulated fashion:

    plot(cv_proportions(rk, cumulate = TRUE))

This plot shows that the two fold-wise searches (as well as the full-data search, whose predictor ranking determines the order of the predictors on the y-axis) agree on the set of the 7 most relevant predictors: When looking at `<=7` on the x-axis, all tiles above and including the 7th main diagonal element are at 100 %. (Similarly, the two CV folds also agree on the set of the most relevant predictor—which is a singleton—and the set of the two most relevant predictors, but we already observed this above.)

Although not demonstrated here, the cumulated ranking proportions also could have guided the decision for a submodel size: From the plot of the cumulated ranking proportions, we can see that size 8 might have been an unfortunate choice because `X11` (which—by cutting off the full-data predictor ranking at size 8—would then have been selected as the 8th predictor in the final submodel) is not included among the first 8 terms by any CV fold. However, since `K = 2` is too small for reliable statements regarding the variability of the predictor ranking, we did not take the cumulated ranking proportions into account when we made our decision for a submodel size above.

In a real-world application, we might also be able to incorporate the full-data predictor ranking into our decision for a submodel size (usually, this requires to also take into account the variability of the predictor ranking, as reflected by the—possibly cumulated—ranking proportions). For example, the predictors might be associated with different measurement costs, so that we might want to select a costly predictor only if the submodel size at which it would be selected (according to the full-data predictor ranking, but taking into account that there might be variability in the ranking of the predictors) comes with a considerable increase in predictive performance.

## Post-selection inference

The `project()` function returns an object of class `projection` which forms the basis for convenient post-selection inference. By the following `project()` call, we project the reference model onto the final submodel once again9:

    prj <- project(
      refm_obj,
      predictor_terms = predictors_final,
      ### In interactive use, we recommend not to deactivate the verbose mode:
      verbose = 0
      ###
    )

Next, we create a matrix containing the projected posterior draws stored in the depths of `project()`’s output:

    prj_mat <- as.matrix(prj)

This matrix is all we need for post-selection inference. It can be used like any matrix of draws from MCMC procedures, except that it doesn’t reflect a typical posterior distribution, but rather a projected posterior distribution, i.e., the distribution arising from the deterministic projection of the reference model’s posterior distribution onto the parameter space of the final submodel10. Beware that in case of clustered projection (i.e., a non-`NULL` argument `nclusters` in the `project()` call), the projected draws have different (i.e., nonconstant) weights, which needs to be taken into account when performing post-selection (or, more generally, post-projection) inference, see `as_draws_matrix.projection()` (`proj_linpred()` and `proj_predict()` offer similar functionality via arguments `return_draws_matrix` and `nresample_clusters`, respectively11).

### Marginals of the projected posterior

The posterior package provides a general way to deal with posterior distributions, so it can also be applied to our projected posterior. For example, to calculate summary statistics for the marginals of the projected posterior:

    library(posterior)

    prj_drws <- as_draws_matrix(prj_mat)
    prj_smmry <- summarize_draws(
      prj_drws,
      "median", "mad", function(x) quantile(x, probs = c(0.025, 0.975))
    )
    # Coerce to a `data.frame` because some pkgdown versions don't print the
    # tibble correctly:
    prj_smmry <- as.data.frame(prj_smmry)
    print(prj_smmry, digits = 1)

A visualization of the projected posterior can be achieved with the bayesplot package, for example using its `mcmc_intervals()` function:

    library(bayesplot)

    bayesplot_theme_set(ggplot2::theme_bw())
    mcmc_intervals(prj_mat) +
      ggplot2::coord_cartesian(xlim = c(-1.5, 1.6))

Note that we only visualize the 1-dimensional marginals of the projected posterior here. To gain a more complete picture, we would have to visualize at least some 2-dimensional marginals of the projected posterior (i.e., marginals for pairs of parameters).

For comparison, consider the marginal posteriors of the corresponding parameters in the reference model:

    refm_mat <- as.matrix(refm_fit)
    mcmc_intervals(refm_mat, pars = colnames(prj_mat)) +
      ggplot2::coord_cartesian(xlim = c(-1.5, 1.6))

Here, the reference model’s marginal posteriors differ only slightly from the marginals of the projected posterior. This does not necessarily have to be the case.

### Predictions

Predictions from the final submodel can be made by `proj_linpred()` and `proj_predict()`.

We start with `proj_linpred()`. For example, suppose we have the following new observations:

    ( dat_gauss_new <- setNames(
      as.data.frame(replicate(length(predictors_final), c(-1, 0, 1))),
      predictors_final
    ) )

Then `proj_linpred()` can calculate the linear predictors12 for all new observations from `dat_gauss_new`. Depending on argument `integrated`, these linear predictors can be averaged across the projected draws (within each new observation). For instance, the following computes the expected values of the new observations’ predictive distributions (beware that the following code refers to the Gaussian family with the identity link function; for other families—which usually come in combination with a different link function—one would typically have to use `transform = TRUE` in order to achieve such expected values):

    prj_linpred <- proj_linpred(prj, newdata = dat_gauss_new, integrated = TRUE)
    cbind(dat_gauss_new, linpred = as.vector(prj_linpred[["pred"]]))

If `dat_gauss_new` also contained response values (i.e., `y` values in this example), then `proj_linpred()` would also evaluate the log predictive density at these (conditional on each of the projected parameter draws if `integrated = FALSE` and integrated over the projected parameter draws—before taking the logarithm—if `integrated = TRUE`).

With `proj_predict()`, we can obtain draws from predictive distributions based on the final submodel. In contrast to `proj_linpred(<...>, integrated = FALSE)`, this encompasses not only the uncertainty arising from parameter estimation, but also the uncertainty arising from the observation (or “sampling”) model for the response13. This is useful for what is usually termed a posterior predictive check (PPC), but would have to be termed something like a posterior-projection predictive check (PPPC) here:

    prj_predict <- proj_predict(prj)
    # Using the 'bayesplot' package:
    ppc_dens_overlay(y = dat_gauss$y, yrep = prj_predict)

This PPPC shows that our final projection is able to generate predictions similar to the observed response values, which indicates that this model is reasonable, at least in this regard.

## Supported types of models

In principle, the projection predictive variable selection requires only little information about the form of the reference model. Although many aspects of the reference model coincide with those from the submodels if a “typical” reference model object is used, this does not need to be the case if a “custom” reference model object is used (see section “Reference model” above for the definition of “typical” and “custom” reference model objects). This explains why in general, the following remarks refer to the submodels and not to the reference model.

In the following and throughout projpred’s documentation, the term “traditional projection” is used whenever the projection type is neither “augmented-data” nor “latent” (see below for a description of these).

Apart from the `gaussian()` response family used in this vignette, projpred’s traditional projection also supports the `binomial()`14 and the `poisson()` family.

The families currently supported by projpred’s augmented-data projection (Weber, Glass, and Vehtari 2025) are `binomial()`15 16, `brms::cumulative()`, `rstanarm::stan_polr()` fits, and `brms::categorical()`17. See `?extend_family` (which is called by `init_refmodel()`) for an explanation how to apply the augmented-data projection to “custom” reference model objects. For “typical” reference model objects (i.e., those created by `get_refmodel.stanreg()` or `brms::get_refmodel.brmsfit()`), the augmented-data projection is applied automatically if the family is supported by the augmented-data projection and neither `binomial()` nor `brms::bernoulli()`. For applying the augmented-data projection to the `binomial()` (or `brms::bernoulli()`) family, see `?extend_family` as well as `?augdat_link_binom` and `?augdat_ilink_binom`. Finally, we note that there are some restrictions with respect to the augmented-data projection; projpred will throw an informative error if a requested feature is currently not supported for the augmented-data projection.

The latent projection (Catalina, Bürkner, and Vehtari 2021) is a quite general principle for extending projpred’s traditional projection to more response families. The latent projection is applied when setting argument `latent` of `extend_family()` (which is called by `init_refmodel()`) to `TRUE`. The families for which full latent-projection functionality (in particular, `resp_oscale = TRUE`, i.e., post-processing on the original response scale) is currently available are `binomial()`18 19, `poisson()`, `brms::cumulative()`, and `rstanarm::stan_polr()` fits20. For all other families, it is worth trying the latent projection (by setting `latent = TRUE`); projpred will state if any features are not available and how to make them available. More details concerning the latent projection are given in the corresponding latent-projection vignette. For example, section “Example: Negative binomial distribution” of that vignette illustrates the use of the latent projection for a negative binomial model, section “Example: Weibull distribution with right-censored observations” for a Weibull model with right-censored observations, and section “Example: Log-normal distribution with right-censored observations” for a log-normal model with right-censored observations. The latter two situations are known as parametric survival (or time-to-event) analyses. Note that there are some restrictions with respect to the latent projection; projpred will throw an informative error if a requested feature is currently not supported for the latent projection.

On the side of the predictors, projpred not only supports linear main effects as shown in this vignette, but also interactions, multilevel21, and—as an experimental feature—additive22 terms.

Transferring this vignette to such more complex problems is straightforward (also because this vignette employs a “typical” reference model object): Basically, only the code for fitting the reference model via rstanarm or brms needs to be adapted. The projpred code stays almost the same. Only note that in case of multilevel or additive reference models, some projpred functions then have slightly different options for a few arguments. See the documentation for details.

For example, to apply projpred to the `VerbAgg` dataset from the lme4 package, a corresponding multilevel reference model for the binary response `r2` could be created by the following code:

    data("VerbAgg", package = "lme4")
    refm_fit <- stan_glmer(
      r2 ~ btype + situ + mode + (btype + situ + mode | id),
      family = binomial(),
      data = VerbAgg,
      QR = TRUE, refresh = 0
    )

As an example for an additive (non-multilevel) reference model, consider the `lasrosas.corn` dataset from the agridat package. A corresponding reference model for the continuous response `yield` could be created by the following code (note that `pp_check(refm_fit)` gives a bad PPC in this case, so there’s still room for improvement):

    data("lasrosas.corn", package = "agridat")
    # Convert `year` to a `factor` (this could also be solved by using
    # `factor(year)` in the formula, but we avoid that here to put more emphasis on
    # the demonstration of the smooth term):
    lasrosas.corn$year <- as.factor(lasrosas.corn$year)
    refm_fit <- stan_gamm4(
      yield ~ year + topo + t2(nitro, bv),
      family = gaussian(),
      data = lasrosas.corn,
      QR = TRUE, refresh = 0
    )

As an example for an additive multilevel reference model, consider the `gumpertz.pepper` dataset from the agridat package. A corresponding reference model for the binary response `disease` could be created by the following code:

    data("gumpertz.pepper", package = "agridat")
    refm_fit <- stan_gamm4(
      disease ~ field + leaf + s(water),
      random = ~ (1 | row) + (1 | quadrat),
      family = binomial(),
      data = gumpertz.pepper,
      QR = TRUE, refresh = 0
    )

In case of multilevel models (currently only non-additive ones), projpred has two global options that may be relevant for users: `projpred.mlvl_pred_new` and `projpred.mlvl_proj_ref_new`. These are explained in detail in the general package documentation (available online or by typing `?`projpred-package``).

## Troubleshooting

### Non-convergence of predictive performance

Sometimes, the predictor ranking makes sense, but for an increasing submodel size, the predictive performance of the submodels does not approach the reference model’s predictive performance so that the submodels exhibit a predictive performance that stays worse than the reference model’s. There are different reasons that can explain this behavior (the following list might not be exhaustive, though):

1. The reference model’s posterior may be so wide that the default `ndraws_pred` could be too small. Usually, this comes in combination with a difference in predictive performance which is comparatively small. Increasing `ndraws_pred` should help, but it also increases the computational cost. Refitting the reference model and thereby ensuring a narrower posterior (usually by employing a stronger sparsifying prior) should have a similar effect.
2. For non-Gaussian models, the discrepancy may be due to the fact that the penalized iteratively reweighted least squares (PIRLS) algorithm might have convergence issues (Catalina, Bürkner, and Vehtari 2021). In this case, the latent-space approach by Catalina, Bürkner, and Vehtari (2021) might help, see also the latent-projection vignette.

### Overfitting

If `varsel()` is used, the lack of a CV in `varsel()` may lead to overconfident and overfitted results. In this case, we recommend `cv_varsel()` instead of `varsel()` (`cv_varsel()` should be used for final results anyway).

Similarly, `cv_varsel()` with `validate_search = FALSE` is more prone to overfitting than `cv_varsel()` with `validate_search = TRUE`.

### Issues with the traditional projection

For multilevel binomial models, the traditional projection may not work properly and give suboptimal results, see #353 on GitHub (the underlying issue is described in lme4 issue #682). By suboptimal results, we mean that the relevance of the group-level terms can be underestimated. According to the simulation-based case study from #353, the latent projection might help in that case.

For multilevel Poisson models, the traditional projection may take very long, see #353. According to the simulation-based case study from #353, the latent projection might help in that case.

Finally, as illustrated in the Poisson example of the latent-projection vignette, the latent projection can be beneficial for non-multilevel models with a (non-Gaussian) family that is also supported by the traditional projection, at least in case of the Poisson family and L1 search.

### Issues with the augmented-data projection

For multilevel models, the augmented-data projection seems to suffer from the same issue as the traditional projection for the binomial family (see above), i.e., it may not work properly and give suboptimal results, see #353 (the underlying issue is probably similar to the one described in lme4 issue #682). By suboptimal results, we mean that the relevance of the group-level terms can be underestimated. According to the simulation-based case study from #353, the latent projection might help in such cases.

### Speed

There are many ways to speed up projpred, but in general, such speed-ups lead to results that are less accurate and hence should only be considered as giving preliminary results. Some speed-up possibilities are:

1. Using `cv_varsel()` with `validate_search = FALSE` instead of `validate_search = TRUE`. In case of `cv_method = "LOO"`23, `cv_varsel()` with `validate_search = FALSE` has comparable runtime to `varsel()`, but accounts for some overfitting, namely that induced by `varsel()`’s in-sample predictions during the predictive performance evaluation. However, as explained in section “Variable selection” (see also section “Overfitting”), `cv_varsel()` with `validate_search = FALSE` is more prone to overfitting than `cv_varsel()` with `validate_search = TRUE`.

2. Using `cv_varsel()` with subsampled PSIS-LOO CV (Magnusson et al. 2020), see argument `nloo` of `cv_varsel()`.

3. Using `cv_varsel()` with \(K\)-fold CV instead of PSIS-LOO CV. Whether this provides a speed improvement mainly depends on the number of observations, whether PSIS-LOO CV could be subsampled (and—if yes—how small argument `nloo` of `cv_varsel()` could be set while still obtaining reliable results), and the complexity of the reference model. Note that PSIS-LOO CV is often more accurate than \(K\)-fold CV if argument `K` is (much) smaller than argument `nloo` of `cv_varsel()`.

4. Using a “custom” reference model object with a dimension reduction technique for the predictor data (e.g., by computing principal components from the original predictors, using these principal components as predictors when fitting the reference model, and then performing the variable selection in terms of the original predictor terms). Examples are given in Piironen, Paasiniemi, and Vehtari (2020) and Pavone et al. (2022). A short example for a custom reference model object is also given in section “Examples” of the `?init_refmodel` help. This approach makes sense if there is a large number of predictor variables, in which case this aims at improving the runtime required for fitting the reference model and hence improving the runtime of \(K\)-fold CV.

5. Using `varsel()` with its argument `d_test` for evaluating predictive performance on a hold-out dataset instead of doing this with `cv_varsel()`’s CV approach. Typically, the hold-out approach requires a large amount of data.

6. Reducing `nterms_max` in `varsel()` or `cv_varsel()`. The resulting predictive performance plot(s) should be inspected to ensure that the search is not terminated too early (i.e., before the submodel performance levels off), which would indicate that `nterms_max` has been reduced too much.

7. Reducing argument `nclusters` (of `varsel()` or `cv_varsel()`) below `20` and/or setting `nclusters_pred` to some non-`NULL` (and smaller than `400`, the default for `ndraws_pred`) value. If setting `nclusters_pred` as low as `nclusters` (and using forward search), `refit_prj` can instead be set to `FALSE`, see below.

8. Using L1 search (see argument `method` of `varsel()` or `cv_varsel()`) instead of forward search. Note that L1 search implies `nclusters = 1` and is not always supported. In general, forward search is more accurate than L1 search and hence more desirable (see section “Details” in `?varsel` or `?cv_varsel` for a more detailed comparison of the two). The issue demonstrated in the Poisson example of the latent-projection vignette is related to this.

9. Setting argument `refit_prj` (of `varsel()` or `cv_varsel()`) to `FALSE`, which basically means to set `ndraws_pred = ndraws` and `nclusters_pred = nclusters`, but in a more efficient (i.e., faster) way. In case of L1 search, this means that the L1-penalized projections of the regression coefficients are used for the predictive performance evaluation, which is usually undesired (Piironen, Paasiniemi, and Vehtari 2020, sec. 4). In case of forward search, this issue does not exist.

10. Parallelizing costly parts of the CV implied by `cv_varsel()` (this was demonstrated in the example above; see argument `parallel` of `cv_varsel()`). When using `project()`, parallelizing the projection might also help (see the general package documentation available online or by typing `?`projpred-package``).

11. Using `varsel.vsel()` or `cv_varsel.vsel()` to re-use previous search results for new performance evaluation(s). This is helpful if the performance evaluation part is run multiple times based on the same search results (e.g., when only arguments `ndraws_pred` or `nclusters_pred` of `varsel()` or `cv_varsel()` are changed). In the example above, this was illustrated when `cv_varsel()` was applied to `cvvs_fast` instead of `refm_obj` to yield `cvvs_fast_refit`. In that example, search and performance evaluation were effectively run separately by the `cv_varsel()` calls yielding `cvvs_fast` and `cvvs_fast_refit`, respectively. This is because `cv_varsel()` with `refit_prj = FALSE` (used for `cvvs_fast`) has almost only the computational cost of the search (the performance evaluation with `refit_prj = FALSE` has almost no computational cost) and `cv_varsel.vsel()` (used for `cvvs_fast_refit`) has almost only the computational cost of the performance evaluation (the search in `cv_varsel.vsel()` has no computational cost at all because the previous search results from the `vsel` object are re-used).

12. Using `run_cvfun()` in case of repeated \(K\)-fold CV with the same \(K\) reference model refits. The output of `run_cvfun()` is typically used as input for argument `cvfits` of `cv_varsel.refmodel()` (so in order to have a speed improvement, the output of `run_cvfun()` needs to be assigned to an object which is then re-used in multiple `cv_varsel()` calls).

## References

Catalina, Alejandro, Paul-Christian Bürkner, and Aki Vehtari. 2022. “Projection Predictive Inference for Generalized Linear and Additive Multilevel Models.” In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, edited by Gustau Camps-Valls, Francisco J. R. Ruiz, and Isabel Valera, 151:4446–61. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v151/catalina22a.html.
Catalina, Alejandro, Paul Bürkner, and Aki Vehtari. 2021. “Latent Space Projection Predictive Inference.” arXiv. https://doi.org/10.48550/arXiv.2109.04702.
Dupuis, Jérome A., and Christian P. Robert. 2003. “Variable Selection in Qualitative Models via an Entropic Explanatory Power.” Journal of Statistical Planning and Inference 111 (1–2): 77–94. https://doi.org/10.1016/S0378-3758(02)00286-0.
Goutis, Constantinos, and Christian P. Robert. 1998. “Model Choice in Generalised Linear Models: A Bayesian Approach via Kullback-Leibler Projections.” Biometrika 85 (1): 29–37.
Magnusson, Måns, Michael Riis Andersen, Johan Jonasson, and 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, 108:341–51. 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): 128–47. https://doi.org/10.1214/24-STS949.
Pavone, Federico, Juho Piironen, Paul-Christian Bürkner, and Aki Vehtari. 2022. “Using Reference Models in Variable Selection.” Computational Statistics. https://doi.org/10.1007/s00180-022-01231-6.
Piironen, Juho, Markus Paasiniemi, and Aki Vehtari. 2020. “Projective Inference in High-Dimensional Problems: Prediction and Feature Selection.” Electronic Journal of Statistics 14 (1): 2155–97. https://doi.org/10.1214/20-EJS1711.
Piironen, Juho, and Aki Vehtari. 2017a. “Comparison of Bayesian Predictive Methods for Model Selection.” Statistics and Computing 27 (3): 711–35. https://doi.org/10.1007/s11222-016-9649-y.
———. 2017b. “On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior.” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, edited by Aarti Singh and Jerry Zhu, 54:905–13. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v54/piironen17a.html.
———. 2017c. “Sparsity Information and Regularization in the Horseshoe and Other Shrinkage Priors.” Electronic Journal of Statistics 11 (2): 5018–51. https://doi.org/10.1214/17-EJS1337SI.
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. https://doi.org/10.1007/s11222-016-9696-4.
Vehtari, Aki, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry. 2022. “Pareto Smoothed Importance Sampling.” arXiv. https://doi.org/10.48550/arXiv.1507.02646.
Weber, Frank, Änne Glass, and Aki Vehtari. 2025. “Projection Predictive Variable Selection for Discrete Response Families with Finite Support.” Computational Statistics 40 (2): 701–21. https://doi.org/10.1007/s00180-024-01506-0.

1. The citation information can be accessed offline by typing `print(citation("projpred"), bibtex = TRUE)` within R.↩︎

2. More generally, the number of chains is split up as evenly as possible among the number of CPU cores.↩︎

3. Here, the `get_refmodel()` call is quite fast, but in other situations, it may take a while, so in general, it is better to create the `refmodel` object once explicitly and then to re-use it.↩︎

4. Currently, neither `varsel()` nor `cv_varsel()` (not even `cv_varsel()` with `validate_search = TRUE`) guard against overfitting in the selection of the submodel size. This is why we added “approximately” to “valid post-selection inference” in section “Introduction”. Typically, however, the overfitting induced by the size selection should be comparatively small (Piironen and Vehtari 2017a).↩︎

5. Analogous functionality has been implemented for `varsel()`, namely in `varsel.vsel()`.↩︎

6. For the geometric mean predictive density (GMPD; see argument `stats` of `summary.vsel()` and `plot.vsel()`), `deltas = TRUE` estimates the GMPD ratio (not difference) vs. the baseline model.↩︎

7. For the definition of the baseline model, see argument `baseline` of `summary.vsel()` and `plot.vsel()`; in the most common cases, the default baseline model is the reference model.↩︎

8. In general, only `cv_varsel()` with `validate_search = TRUE` allows to judge whether the submodels perform better than the reference model or not. Such a judgment is generally not possible with `varsel()` or `cv_varsel()` with `validate_search = FALSE`.↩︎

9. During the search, the reference model is projected onto all candidate models (this is where arguments `ndraws` and `nclusters` of `varsel()` and `cv_varsel()` come into play; note that in case of an L1 search, the projection is L1-penalized). For the evaluation of the submodels along the predictor ranking returned by the search, the reference model is projected onto these submodels again (this is where arguments `ndraws_pred` and `nclusters_pred` of `varsel()` and `cv_varsel()` come into play; note that this only holds if argument `refit_prj` of `varsel()` and `cv_varsel()` is set to `TRUE`, as by default). Within `project()`, `refit_prj = FALSE` allows to re-use the submodel fits (that is, the projections) from the full-data search of a `vsel` object, but 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`—or `nclusters = 1` in case of L1 search), which would then imply the same coarseness for a `project()` call where `refit_prj` is set to `FALSE`. In general, we want the final projection (that post-selection inference is based on) to be as accurate as possible, so here we call `project()` with the defaults of `refit_prj = TRUE` and `ndraws = 400`. For more accurate results, we could increase argument `ndraws` of `project()` (up to the number of posterior draws in the reference model). However, this would increase the runtime, which we don’t want in this vignette.↩︎

10. In general, this implies that projected regression coefficients do not reflect isolated effects of the predictors. For example, especially in case of highly correlated predictors, it is possible that projected regression coefficients “absorb” effects from predictors that have been excluded in the projection.↩︎

11. `proj_predict()` also has an argument `return_draws_matrix`, but it simply converts the return value type. In `proj_predict()`, different weights of the projected draws are taken into account via argument `nresample_clusters`.↩︎

12. `proj_linpred()` can also transform the linear predictor to response scale, but here, this is the same as the linear predictor scale (because of the identity link function).↩︎

13. In case of the Gaussian family we are using here, the uncertainty arising from the observation model is the uncertainty due to the residual standard deviation.↩︎

14. Via `brms::get_refmodel.brmsfit()`, the `brms::bernoulli()` family is supported as well.↩︎

15. Currently, the augmented-data support for the `binomial()` family does not include binomial distributions with more than one trial. In such a case, a workaround is to de-aggregate the Bernoulli trials which belong to the same (aggregated) observation, i.e., to use a “long” dataset.↩︎

16. Like the traditional projection, the augmented-data projection also supports the `brms::bernoulli()` family via `brms::get_refmodel.brmsfit()`.↩︎

17. For the augmented-data projection based on a “typical” brms reference model object, brms version 2.17.0 or later is needed.↩︎

18. Currently, the latent-projection support for the `binomial()` family does not include binomial distributions with more than one trial. In such a case, a workaround is to de-aggregate the Bernoulli trials which belong to the same (aggregated) observation, i.e., to use a “long” dataset.↩︎

19. Like the traditional projection, the latent projection also supports the `brms::bernoulli()` family via `brms::get_refmodel.brmsfit()`.↩︎

20. For the latent projection based on a “typical” brms reference model object, brms version 2.19.0 or later is needed.↩︎

21. Multilevel models are also known as hierarchical models or models with partially pooled, group-level, or—in frequentist terms—random effects.↩︎

22. Additive terms are also known as smooth terms.↩︎

23. In case of `cv_method = "kfold"`, the runtime advantage of `validate_search = FALSE` compared to `validate_search = TRUE` is by far not as large as in case of `cv_method = "LOO"`.↩︎