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
)

plm.fit(
  x,
  y,
  offset = NULL,
  method = "qr",
  tol = 1e-07,
  singular.ok = TRUE,
  ...,
  stopFun = "EBIC",
  keep = NULL,
  maxK = NULL,
  verbose = FALSE
)

Arguments

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

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

A 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(2026)
n <- 300
p <- 200
x <- matrix(rnorm(n*p), n)

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

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

flm(y ~ ., DF, verbose=TRUE)
flm(y ~ ., DF, stopFun=BIC, verbose=TRUE)
frs(x, y, lm, verbose=TRUE)

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