plm: Profile Boosting for Linear Models.

In pboost: Profile Boosting Framework for Parametric Models

Profile Boosting for Linear Models.

Description

plm inherits the usage of the built-in function lm.

Usage

plm(
  formula,
  data,
  subset,
  weights,
  na.action,
  method = "qr",
  model = TRUE,
  x = FALSE,
  y = FALSE,
  qr = TRUE,
  singular.ok = TRUE,
  contrasts = NULL,
  offset,
  ...,
  stopFun = EBIC,
  keep = NULL,
  maxK = NULL,
  verbose = FALSE
)

Arguments

formula	See pboost.
data	See pboost.
subset	Parameters passed to lm.
weights	Parameters passed to lm.
na.action	Parameters passed to lm.
method	Parameters passed to lm.
model	Parameters passed to lm.
x	Parameters passed to lm.
y	Parameters passed to lm.
qr	Parameters passed to lm.
singular.ok	Parameters passed to lm.
contrasts	Parameters passed to lm.
offset	Parameters passed to lm.
...	Parameters passed to lm.
stopFun	Parameters passed to pboost.
keep	Parameters passed to pboost.
maxK	Parameters passed to pboost.
verbose	Parameters passed to pboost.

Details

plm is an equivalent implementation to the sequential lasso method proposed by Luo and Chen(2014, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2013.877275")}).

Value

An lm 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")}

• Shan Luo and Zehua Chen (2014). A Sequential Lasso Method for Feature Selection with Ultra-High Dimensional Feature Space. Journal of the American Statistical Association, 109(507):223–232. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2013.877275")}

Examples

set.seed(2025)
n <- 300
p <- 200
x <- matrix(rnorm(n*p), n)

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

plm(y ~ ., DF, verbose=TRUE)
plm(y ~ ., DF, stopFun=BIC, verbose=TRUE)
pboost(y ~ ., DF, lm, residuals, EBIC, verbose=TRUE)

pboost documentation built on Jan. 9, 2026, 1:07 a.m.