projpred: Projection predictive feature selection



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 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 and Vehtari (2023), and Catalina, Bürkner, and Vehtari (2021). A comparison to many other methods may also be found in Piironen and Vehtari (2017a). For details on how to cite projpred, see the projpred citation info on CRAN1.


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 get_refmodel.stanreg() or brms::get_refmodel.brmsfit() methods (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 help2.

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). Only note that in case of a brms fit, we recommend to specify argument brms_seed of brms::get_refmodel.brmsfit().


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))) )
[1] 20
# 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 500 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 core3. 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)
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 = 500,
  seed = 2052109, QR = TRUE, 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.


In projpred, the projection predictive variable selection consists of a search part and an evaluation part. The search part determines the solution path, i.e., the best submodel for each submodel size (the size is the number of predictor terms). The evaluation part determines the predictive performance of the submodels along the solution path.

There are two functions for performing the variable selection: varsel() and cv_varsel(). 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 validate_search = FALSE, see ?cv_varsel and below) and running the evaluation part on the corresponding test set of each CV fold. Because of this CV, cv_varsel() is recommended over varsel(). Thus, we use cv_varsel() here. Nonetheless, running varsel() first can offer a rough idea of the performance of the submodels (after projecting the reference model onto them). A more principled projpred workflow is work under progress.

Here, we use only some of the available arguments; see the documentation of cv_varsel() for the full list of arguments. By default, cv_varsel() runs a Pareto-smoothed importance sampling (PSIS, see Vehtari, Gelman, and Gabry 2017; Vehtari et al. 2022) leave-one-out (LOO) CV (see argument cv_method) which includes the search in the CV (see argument validate_search). Here, we set argument validate_search to FALSE to obtain rough preliminary results and make this vignette build faster. If possible (in terms of computation time), we recommend using the default of validate_search = TRUE to avoid overfitting in the selection of the submodel size. Here, we also set nclusters_pred to a low value of 20 only to speed up the building of the vignette. By modifying argument nterms_max, we impose a limit on the submodel size up to which the search is continued. Typically, one has to run the variable selection with a large nterms_max first (the default value may not even be large enough) and only after inspecting the results from this first run, one is able to set a reasonable nterms_max in subsequent runs. The value we are using here (9) is based on such a first run (which is not shown here, though).

cvvs <- cv_varsel(
  ### Only for the sake of speed (not recommended in general):
  validate_search = FALSE,
  nclusters_pred = 20,
  nterms_max = 9,
  seed = 411183

The first step after running varsel() or cv_varsel() should be the decision for a final submodel size. This should be the first step (in particular, before inspecting the solution path) in order to avoid a user-induced selection bias (which could occur if the user made the submodel size decision dependent on the solution path). To decide for a submodel size, there are several performance statistics we can plot as a function of the submodel size. Here, we use the mean log predictive density (MLPD; see argument stats of summary.vsel() for details) and the root mean squared error (RMSE). By default, the performance statistics are plotted on their original scale, but with deltas = TRUE, they are calculated as differences from a baseline model (which is the reference model by default, at least in the most common cases). Since the differences are usually of more interest (with regard to the submodel size decision), we directly plot with deltas = TRUE here (note that as validate_search = FALSE, this result is slightly optimistic, and the plot looks different when validate_search = TRUE is used):

plot(cvvs, stats = c("mlpd", "rmse"), deltas = TRUE, seed = 54548)

Based on that plot (see ?plot.vsel for a description), we would decide for a submodel size of 6 because that’s the point where the performance measures level off and are close enough to the reference model’s performance (note that since the plot is affected by validate_search = FALSE, this manual decision based on the plot is affected, too):

modsize_decided <- 6

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")
[1] 6
# We are using the same seed as in the plot() call above to ensure that the
# bootstrap intervals are exactly the same:
suggest_size(cvvs, stat = "rmse", seed = 54548)
[1] 6

With both performance statistics, we would get the same final submodel size (6) as by our manual decision (suggest_size() is also affected by validate_search = FALSE).

Only now, after we have made a decision for the submodel size, we inspect further results from the variable selection and, in particular, the solution path. For example, we could simply print() the resulting cvvs object, but to create the summary table matching the predictive performance plot above (and to adjust the minimum number of printed significant digits), we slightly adapt the printing here:

print(summary(cvvs, stats = c("mlpd", "rmse"), deltas = TRUE, seed = 54548),
      digits = 1)

Family: gaussian 
Link function: identity 

Formula: y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10 + X11 + 
    X12 + X13 + X14 + X15 + X16 + X17 + X18 + X19 + X20
Observations: 100
Projection method: traditional
CV method: PSIS-LOO CV with search not included (i.e., a full-data search only)
Search method: L1
Maximum submodel size for the search: 9
Number of projected draws in the search: 1 (from clustered projection)
Number of projected draws in the performance evaluation: 20 (from clustered projection)

Performance evaluation summary with `deltas = TRUE`:
 size solution_terms mlpd.loo rmse.loo
    0           <NA>   -1.021    0.08    1.886    0.16
    1             X1   -0.835    0.08    1.389    0.15
    2            X14   -0.580    0.07    0.842    0.12
    3             X5   -0.494    0.08    0.686    0.12
    4            X20   -0.306    0.06    0.388    0.09
    5             X6   -0.217    0.06    0.264    0.08
    6             X3    0.005    0.05   -0.003    0.05
    7             X8    0.089    0.03   -0.091    0.03
    8            X11    0.117    0.02   -0.121    0.02
    9            X10    0.121    0.02   -0.123    0.02

The solution path can be seen in the print() output (column solution_terms), but it is also accessible through the solution_terms() function:

( soltrms <- solution_terms(cvvs) )
[1] "X1"  "X14" "X5"  "X20" "X6"  "X3"  "X8"  "X11" "X10"

Combining the decided submodel size of 6 with the solution path leads to the following terms (as well as the intercept) as the predictor terms of the final submodel:

( soltrms_final <- head(soltrms, modsize_decided) )
[1] "X1"  "X14" "X5"  "X20" "X6"  "X3" 

Post-selection inference

The project() function returns an object of class projection which forms the basis for convenient post-selection inference. By the following code, project() will project the reference model onto the final submodel once again4:

prj <- project(refm_fit, solution_terms = soltrms_final)

For more accurate results, we could have increased argument ndraws of project() (up to the number of posterior draws in the reference model). However, this increases the runtime, which we don’t want in this vignette.

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 submodel5. Beware that in case of clustered projection (i.e., a non-NULL argument nclusters in the project() call), the weights of the clusters need to be taken into account when performing post-selection (or, more generally, post-projection) inference.

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:

prj_drws <- as_draws_matrix(prj_mat)
prj_smmry <- summarize_draws(
  "median", "mad", function(x) quantile(x, probs = c(0.025, 0.975))
# Coerce to a `data.frame` because pkgdown versions > 1.6.1 don't print the
# tibble correctly:
prj_smmry <-
print(prj_smmry, digits = 1)
     variable median  mad 2.5% 97.5%
1 (Intercept)   0.05 0.10 -0.2   0.2
2          X1   1.38 0.09  1.2   1.6
3         X14  -1.12 0.09 -1.3  -0.9
4          X5  -0.91 0.11 -1.1  -0.7
5         X20  -1.12 0.11 -1.3  -0.9
6          X6   0.54 0.10  0.3   0.7
7          X3   0.78 0.10  0.6   1.0
8       sigma   1.13 0.09  1.0   1.3

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

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 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(, c(-1, 0, 1))),
) )
  X1 X14 X5 X20 X6 X3
1 -1  -1 -1  -1 -1 -1
2  0   0  0   0  0  0
3  1   1  1   1  1  1

Then proj_linpred() can calculate the linear predictors6 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 distributions7:

prj_linpred <- proj_linpred(prj, newdata = dat_gauss_new, integrated = TRUE)
cbind(dat_gauss_new, linpred = as.vector(prj_linpred$pred))
  X1 X14 X5 X20 X6 X3     linpred
1 -1  -1 -1  -1 -1 -1  0.50053930
2  0   0  0   0  0  0  0.04160798
3  1   1  1   1  1  1 -0.41732334

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 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 response8. 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, .seed = 762805)
# 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()9 and the poisson() family.

The families supported by projpred’s augmented-data projection (Weber and Vehtari 2023) are binomial()10 11, brms::cumulative(), rstanarm::stan_polr() fits, and brms::categorical()12 13. See ?extend_family (which is called by init_refmodel()) for an explanation how to apply the augmented-data projection to “custom” reference models. For “typical” reference models (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()14 15, poisson(), brms::cumulative(), and rstanarm::stan_polr() fits16. For all other families, you can try to use the latent projection (by setting latent = TRUE) and projpred should tell you 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. 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, multilevel17, and—as an experimental feature—additive18 terms.

Transferring this vignette (which employs a “typical” reference model) to such more complex problems is straightforward: 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,
  seed = 82616169, 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,
  seed = 4919670, 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,
  seed = 14209013, QR = TRUE, refresh = 0

In case of multilevel models, 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`).


Non-convergence of predictive performance

Sometimes, the ordering of the predictor terms in the solution path makes sense, but for an increasing submodel size, the performance measures of the submodels do not approach that of the reference model 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. Re-fitting 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 linked above.


If you are using varsel(), then the lack of CV in varsel() may lead to overconfident and overfitted results. In this case, try running cv_varsel() instead of varsel() (which you should in any case for your final results).

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). With suboptimality of the results, we mean that in such cases, the relevance of the group-level terms can be underestimated. According to the simulation-based case study from #353, the latent projection should be considered as a currently available remedy.

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 should be considered as a currently available remedy.

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). With suboptimality of the results, we mean that in such cases, the relevance of the group-level terms can be underestimated. According to the simulation-based case study from #353, the latent projection should be considered as a remedy in such cases.


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.
Catalina, Alejandro, Paul Bürkner, and Aki Vehtari. 2021. “Latent Space Projection Predictive Inference.” arXiv.
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.
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.
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.
Piironen, Juho, and Aki Vehtari. 2017a. “Comparison of Bayesian Predictive Methods for Model Selection.” Statistics and Computing 27 (3): 711–35.
———. 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.
———. 2017c. “Sparsity Information and Regularization in the Horseshoe and Other Shrinkage Priors.” Electronic Journal of Statistics 11 (2): 5018–51.
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.
Vehtari, Aki, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry. 2022. “Pareto Smoothed Importance Sampling.” arXiv.
Weber, Frank, and Aki Vehtari. 2023. “Projection Predictive Variable Selection for Discrete Response Families with Finite Support.” arXiv.

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

  2. We will cover custom reference models more deeply in a future vignette.↩︎

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

  4. During the forward search, the reference model has already been projected onto all candidate models (this was where arguments ndraws and nclusters of cv_varsel() came into play). For the evaluation of the submodels along the solution path, the reference model has been projected onto these submodels again (this was where arguments ndraws_pred and nclusters_pred of cv_varsel() came into play). In principle, one could use the re-projections from the evaluation part for post-selection inference, but due to a deficiency in the current implementation (see GitHub issue #168), we currently have to project once again.↩︎

  5. 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.↩︎

  6. 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).↩︎

  7. Beware that this statement is correct here because of 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 make this statement correct.↩︎

  8. 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.↩︎

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

  10. 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.↩︎

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

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

  13. More brms families might be supported in the future.↩︎

  14. 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.↩︎

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

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

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

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