What you’ll learn
- How to configure scopes and engines for Box–Cox t growth models,
insurance claims, and quine attendance.
- How to use
selection_table() and
effect_plot() to interpret the resulting fits.
- How to adjust bootstrap settings (
B,
pi_thr, parallel) for heavier real-world
analyses.
You may want to install optional engines for best results:
install.packages(c("glmnet","grpreg","SGL","future.apply"))
library(SelectBoost.gamlss)
library(gamlss)
library(MASS) # Insurance, quine
set.seed(1)
1. Growth data (BCT) — Dutch boys
We model height vs age with
BCT (Box–Cox t). Grouped penalties select among
smoothers and interactions with pubertal stage.
Increase B to 50 for real use.
set.seed(123)
# Load and CLEAN the Dutch boys growth data
utils::data("boys7482", package = "SelectBoost.gamlss")
# Keep only rows complete
boys <- boys7482[stats::complete.cases(boys7482),]
boys$gen <- as.factor(boys$gen)
# (Optional) keep levels actually present
boys <- droplevels(boys)
# Fit SelectBoost.gamlss on CLEANED data (no parallel for vignette stability)
fit_growth <- sb_gamlss(
hgt ~ 1, data = boys, family = gamlss.dist::BCT(),
mu_scope = ~ pb(age) + pb(age):gen,
sigma_scope = ~ pb(age),
nu_scope = ~ pb(age),
engine = "grpreg", engine_sigma = "grpreg", engine_nu = "grpreg",
grpreg_penalty = "grLasso",
B = 1, pi_thr = 0.6, pre_standardize = TRUE,
parallel = "none", trace = FALSE
)
# Peek at selection (first rows)
print(utils::head(selection_table(fit_growth), 12))
#> parameter term count prop
#> 1 mu pb(age) 0 0
#> 2 mu pb(age):gen 0 0
#> 3 sigma pb(age) 0 0
#> 4 nu pb(age) 0 0
# Effect plot for age on mu
if (requireNamespace("ggplot2", quietly = TRUE)) {
print(effect_plot(fit_growth, "age", boys, what = "mu"))
}
Increase B to 50 for real use.
set.seed(123)
# Stability curves over a small c0 grid (still on CLEANED data)
g <- sb_gamlss_c0_grid(
hgt ~ 1, data = boys, family = gamlss.dist::BCT(),
mu_scope = ~ pb(age) + pb(age):gen,
sigma_scope = ~ pb(age),
nu_scope = ~ pb(age),
c0_grid = seq(0.2, 0.8, by = 0.1),
B = 1, pi_thr = 0.6, pre_standardize = TRUE,
parallel = "none", trace = FALSE
)
plot_stability_curves(g, terms = c("pb(age)", "pb(age):gen"), parameter = "mu")
Interpretation. Expect pb(age) to be
stably selected for μ and σ; interactions like pb(age):gen
often appear around puberty.
2. Count data (PO) — Insurance claims
Fit Poisson with log-offset for exposure.
data(Insurance, package = "MASS")
ins <- transform(Insurance, logH = log(Holders))
fit_po <- sb_gamlss(
Claims ~ offset(logH), data = ins, family = gamlss.dist::PO(),
mu_scope = ~ Age + District + Group,
engine = "glmnet", glmnet_alpha = 1, # lasso
B = 100, pi_thr = 0.6, pre_standardize = FALSE,
parallel = "auto", trace = FALSE
)
selection_table(fit_po)
#> parameter term count prop
#> 1 mu Age 100 1
#> 2 mu District 100 1
#> 3 mu Group 100 1
3. Positive continuous (GA) — Old Faithful
Use Gamma for positive waiting times vs eruption
length.
data(faithful)
faith <- transform(faithful, eru2 = eruptions^2)
fit_ga <- sb_gamlss(
waiting ~ 1, data = faith, family = gamlss.dist::GA(),
mu_scope = ~ pb(eruptions) + eru2,
sigma_scope = ~ pb(eruptions),
engine = "glmnet", glmnet_alpha = 0.5, # elastic-net
B = 60, pi_thr = 0.6, pre_standardize = TRUE,
parallel = "auto", trace = FALSE
)
selection_table(fit_ga)
#> parameter term count prop
#> 1 mu pb(eruptions) 60 1
#> 2 mu eru2 60 1
#> 3 sigma pb(eruptions) 60 1
# Effect plot for eruptions on mu
if (requireNamespace('ggplot2', quietly = TRUE)) {
print(effect_plot(fit_ga, 'eruptions', faith, what = 'mu'))
}
4. Binary (BI) — mtcars transmission
Treat am (0/1) as Bernoulli.
mt <- transform(mtcars,
am = as.integer(am),
cyl = factor(cyl), gear = factor(gear), carb = factor(carb), vs = factor(vs))
fit_bin <- sb_gamlss(
am ~ 1, data = mt, family = gamlss.dist::BI(),
mu_scope = ~ wt + hp + qsec + cyl + gear + carb + vs,
engine = "grpreg", grpreg_penalty = "grLasso",
B = 80, pi_thr = 0.6, pre_standardize = TRUE,
parallel = "auto", trace = FALSE
)
selection_table(fit_bin)
#> parameter term count prop
#> 1 mu wt 0 0
#> 2 mu hp 0 0
#> 3 mu qsec 0 0
#> 4 mu cyl 0 0
#> 5 mu gear 0 0
#> 6 mu carb 0 0
#> 7 mu vs 0 0
5. Overdispersed counts (NBII) — quine absences
Negative Binomial II handles extra-Poisson
variation.
data(quine, package = "MASS")
fit_nb2 <- sb_gamlss(
Days ~ 1, data = quine, family = gamlss.dist::NBII(),
mu_scope = ~ Eth + Sex + Lrn + Age,
engine = "grpreg", grpreg_penalty = "grLasso",
B = 80, pi_thr = 0.6, pre_standardize = FALSE,
parallel = "auto", trace = FALSE
)
selection_table(fit_nb2)
#> parameter term count prop
#> 1 mu Eth 0 0
#> 2 mu Sex 0 0
#> 3 mu Lrn 0 0
#> 4 mu Age 0 0
Tips
- If you have many factor levels or smoothers, prefer
engine="grpreg"/"sgl" (group-aware).
- For very wide numeric designs, try
engine="glmnet" with
glmnet_alpha in \[0,1\].
- Use
pre_standardize=TRUE (fast C++ scaler) when
predictors are numeric and on different scales.
- For speed, enable parallel workers:
future::plan(multisession, workers = 4); parallel="auto".