params <-
list(EVAL = TRUE)

## ----SETTINGS-knitr, include=FALSE--------------------------------------------
stopifnot(require(knitr))
knitr::opts_chunk$set(
  dev = "png",
  dpi = 150,
  fig.asp = 0.618,
  fig.width = 7,
  out.width = "90%",
  fig.align = "center",
  comment = NA,
  eval = if (isTRUE(exists("params"))) params$EVAL else FALSE
)

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

## ----rstanarm_attach, message=FALSE-------------------------------------------
library(rstanarm)

## ----rh-----------------------------------------------------------------------
# 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)

## ----ref_fit------------------------------------------------------------------
# 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(507801)
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,
  ###
  QR = TRUE, refresh = 0
)

## ----projpred_attach, message=FALSE-------------------------------------------
library(projpred)

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

## ----plot_vsel_fast-----------------------------------------------------------
plot(cvvs_fast, stats = "mlpd", ranking_nterms_max = NA)

## ----cv_varsel, message=FALSE-------------------------------------------------
# For the CV parallelization (cv_varsel()'s argument `parallel`):
doParallel::registerDoParallel(ncores)
# Final cv_varsel() run:
cvvs <- cv_varsel(
  refm_fit,
  cv_method = "kfold",
  ### Only for the sake of speed (not recommended in general):
  K = 2,
  method = "L1",
  nclusters_pred = 20,
  ###
  nterms_max = 9,
  parallel = TRUE,
  ### In interactive use, we recommend not to deactivate the verbose mode:
  verbose = FALSE
  ### 
)
# Tear down the CV parallelization setup:
doParallel::stopImplicitCluster()
foreach::registerDoSEQ()

## ----plot_vsel----------------------------------------------------------------
plot(cvvs, stats = "mlpd", deltas = TRUE)

## ----size_man-----------------------------------------------------------------
size_decided <- 6

## ----size_sgg-----------------------------------------------------------------
suggest_size(cvvs, stat = "mlpd")

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

## ----ranking------------------------------------------------------------------
rk <- ranking(cvvs)

## ----cv_proportions-----------------------------------------------------------
( pr_rk <- cv_proportions(rk) )

## ----ranking_fulldata---------------------------------------------------------
rk[["fulldata"]]

## ----plot_cv_proportions------------------------------------------------------
plot(pr_rk)

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

## ----plot_cv_proportions_cumul------------------------------------------------
plot(cv_proportions(rk, cumulate = TRUE))

## ----project------------------------------------------------------------------
prj <- project(
  refm_fit,
  solution_terms = predictors_final,
  ### In interactive use, we recommend not to deactivate the verbose mode:
  verbose = FALSE
  ###
)

## ----as_matrix_prj------------------------------------------------------------
prj_mat <- as.matrix(prj)

## ----posterior_attach, message=FALSE------------------------------------------
library(posterior)

## ----smmry_prj----------------------------------------------------------------
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 pkgdown versions > 1.6.1 don't print the
# tibble correctly:
prj_smmry <- as.data.frame(prj_smmry)
print(prj_smmry, digits = 1)

## ----bayesplot_attach, message=FALSE------------------------------------------
library(bayesplot)

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

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

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

## ----proj_linpred-------------------------------------------------------------
prj_linpred <- proj_linpred(prj, newdata = dat_gauss_new, integrated = TRUE)
cbind(dat_gauss_new, linpred = as.vector(prj_linpred$pred))

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

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

## ----ref_fit_addv, eval=FALSE-------------------------------------------------
#  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
#  )

## ----ref_fit_addv_mlvl, eval=FALSE--------------------------------------------
#  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
#  )

