pglm: Profile Boosting for Generalized Linear Models.

In pboost: Profile Boosting Framework for Parametric Models

Profile Boosting for Generalized Linear Models.

Description

pglm inherits the usage of the built-in function glm.

Usage

pglm(
  formula,
  family = gaussian,
  data,
  weights,
  subset,
  na.action,
  start = NULL,
  etastart,
  mustart,
  offset,
  control = list(...),
  model = TRUE,
  method = "glm.fit",
  x = FALSE,
  y = TRUE,
  singular.ok = TRUE,
  contrasts = NULL,
  ...,
  stopFun = "EBIC",
  keep = NULL,
  maxK = NULL,
  verbose = FALSE
)

pglm.fit(
  x,
  y,
  weights = rep.int(1, NROW(y)),
  start = NULL,
  etastart = NULL,
  mustart = NULL,
  offset = rep.int(0, NROW(y)),
  family = gaussian(),
  control = list(),
  intercept = TRUE,
  singular.ok = TRUE,
  stopFun = "EBIC",
  keep = NULL,
  maxK = NULL,
  verbose = FALSE
)

Arguments

formula	Parameter passed to glm.
family	Parameter passed to glm.
data	Parameter passed to glm.
weights	Parameter passed to glm.
subset	Parameter passed to glm.
na.action	Parameter passed to glm.
start	Parameter passed to glm.
etastart	Parameter passed to glm.
mustart	Parameter passed to glm.
offset	Parameter passed to glm.
control	Parameter passed to glm.
model	Parameter passed to glm.
method	Parameter passed to glm.
x	Parameter passed to glm.
y	Parameter passed to glm.
singular.ok	Parameter passed to glm.
contrasts	Parameter passed to glm.
...	Parameters passed to glm.
stopFun	Parameter passed to pboost.
keep	Parameter passed to pboost.
maxK	Parameter passed to pboost.
verbose	Parameter passed to pboost.
intercept	Parameter passed to glm.fit.

Value

A glm model object fitted on the selected features.

References

Zengchao Xu, Shan Luo and Zehua Chen (2022). Partial profile score feature selection in high-dimensional generalized linear interaction models. Statistics and Its Interface. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.4310/21-SII706")}

Examples

set.seed(2026)
n <- 200
p <- 100
x <- matrix(rnorm(n*p), n)

eta <- drop( x[, 1:3] %*% runif(3, 1.0, 1.5) )
y <- rbinom(n, 1, 1/(1+exp(-eta)))
DF <- data.frame(y, x)

## ---------- pboost ----------
pglm(y ~ ., "binomial", DF, verbose=TRUE)
pglm(y ~ ., "binomial", DF, stopFun=BIC, verbose=TRUE)

scoreLogistic <- function(object) {
   eta.hat <- object[["linear.predictors"]]
   return(object[["y"]] - 1/(1+exp(-eta.hat)))
}
(result <- pboost(x, y, glm, scoreLogistic, family="binomial", verbose=TRUE))
all.vars(formula(result)[[3]])

## ---------- frs ----------
fglm(y ~ ., "binomial", DF, verbose=TRUE)
fglm(y ~ ., "binomial", DF, stopFun=BIC, verbose=TRUE)

frs(x, y, glm, family="binomial", verbose=TRUE)

pboost documentation built on May 24, 2026, 9:08 a.m.