Title: Select Variables for Linear Models
Version: 1.0.0
Description: Provides variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) <doi:10.48550/arXiv.2510.02628>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: stats (≥ 4.2.2), GA (≥ 3.2.3), memoise (≥ 2.0.1)
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
LazyData: true
Depends: R (≥ 3.5.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-02-12 19:32:40 UTC; xushu
Author: Shuangshuang Xu [aut, cre]
Maintainer: Shuangshuang Xu <xshuangshuang@vt.edu>
Repository: CRAN
Date/Publication: 2026-02-17 16:00:08 UTC

Function for checking model hierarchy

Description

Function for checking model hierarchy

Usage

check_model_hierarchy(model)

Value

model hierarchy

A data frame contains dependent variable and continuous independent variables

Description

A data frame with seven columns. The independent variables are in the first six columns. The dependent variable is in the seventh column.

Usage

dat

Format

dat

A data frame.

Title: Fitting generalized linear models for the best model

Description

Description: glm.best is used to fit generalized linear model for the best model provided by modelselect.glm.

Usage

glm.best(
  object,
  family,
  method = "models",
  threshold = 0.95,
  x = FALSE,
  y = FALSE
)

Arguments

object

the model selection result from modelselect.glm.

family

a character string naming a family function describing the error distribution to be used in the model.

method

the criteria to do model select. method = "models" selects the best model by the highest posterior probabilities. method = "variables" selects the variables in the best model by the posterior inclusion probabilities which are larger than the threshold.

threshold

The threshold for variable selection. The variables with posterior inclusion probability larger than the threshold are selected in the best model. The default is 0.95.

x, y

logicals. If TRUE the corresponding components (the best model predictor matrix, the response) of the fit are returned.

Value

An object of class "glm", which is a list containing the following components:

coefficients

a named vector of coefficients.

residuals

the working residuals, that is the residuals in the final iteration of the IWLS fit.

fitted.values

the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function.

rank

the numeric rank of the fitted linear model.

family

the family object used.

linear.predictors

the linear fit on the link scale.

deviance

up to a constant, minus twice the maximized log-likelihood.

aic

A version of Akaike's An Information Criterion, minus twice the maximized log-likelihood plus twice the number of parameters, computed by the aic component of the family.

null.deviance

The deviance for the null model, comparable with deviance. The null model will include the offset, and an intercept if there is one in the model.

iter

the number of iterations of IWLS used.

weights

the working weights, that is the weights in the final iteration of the IWLS fit.

prior.weights

the weights initially supplied, a vector of 1s if none were.

df.residual

the residual degrees of freedom.

df.null

the residual degrees of freedom for the null model.

y

if requested, the response vector used.

converged

logical. Was the IWLS algorithm judged to have converged?

boundary

logical. Is the fitted value on the boundary of the allowable values?

model

if requested (the default), the model frame used.

call

the matched call.

formula

the formula supplied.

terms

the terms.object used.

data

the data argument.

threshold

the threshold used for method = "variables".

A data frame contains dependent variable and binary independent variables

Description

A data frame with seven columns. The independent variables are in the first six columns. The dependent variable is in the seventh column.

Usage

glmdat

Format

glmdat

A data frame.

Title: Fitting linear models for the best model

Description

Description: lm.best is used to fit linear model for the best model provided by modelselect.lm.

Usage

lm.best(object, method = "models", threshold = 0.95, x = FALSE, y = FALSE)

Arguments

object

the model selection result from modelselect.lm.

method

the criteria to do model select. method = "models" selects the best model by the highest posterior probabilities. method = "variables" selects the variables in the best model by the posterior inclusion probabilities which are larger than the threshold.

threshold

The threshold for variable selection. The variables with posterior inclusion probability larger than the threshold are selected in the best model. The default is 0.95.

x, y

logicals. If TRUE the corresponding components (the best model predictor matrix, the response) of the fit are returned.

Value

An object of class "lm", which is a list containing the following components:

coefficients

A named vector of coefficients.

residuals

The residuals, that is the response minus the fitted values.

fitted.values

The fitted mean values.

rank

The numeric rank of the fitted linear model.

df.residual

The residual degrees of freedom.

call

The matched call.

terms

The terms object used.

model

(If requested) the model frame used.

qr

(If requested) the QR decomposition of the design matrix.

xlevels

(If the model formula includes factors) a record of the levels of the factors.

contrasts

(If the model formula includes factors) the contrasts used.

offset

The offset used.

threshold

the threshold used for method = "variables".

Title: Variable selection for generalized linear models

Description

Description: use BIC to do variable selection.

Usage

modelselect.glm(
  formula,
  data,
  family,
  GA_var = 16,
  maxiterations = 2000,
  runs_til_stop = 1000,
  monitor = TRUE,
  popSize = 100,
  verbose = TRUE
)

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

data

an data frame containing the variables in the model.

family

a character string naming a family function describing the error distribution to be used in the model.

GA_var

if the number of variables is smaller than GA_var, then do exhaustive model search, otherwise use genetic algorithm to do stochastic model search.

maxiterations

the maximum number of iterations to run before the GA search is halted.

runs_til_stop

the number of consecutive generations without any improvement in the best fitness value before the GA is stopped.

monitor

a logical defaulting to TRUE showing the evolution of the search. If monitor = FALSE, any output is suppressed.

popSize

the population size.

verbose

Logical; if TRUE, print a brief summary of results.

Value

modelselect.glm returns a list containing the following components:

models

A data frame of candidate models' BIC and posterior probabilities, sorted by decreasing posterior probability

variables

A data frame of candidate variables' posterior inclusion probabilities

data

The data with variables in the formula.

The function glm.best is used to obtain the linear fitting to the best model by posterior probability or by controlling variables' posterior inclusion probabilities.

Title: Variable selection for linear models

Description

Description: use BIC to do variable selection.

Usage

modelselect.lm(
  formula,
  data,
  GA_var = 16,
  maxiterations = 2000,
  runs_til_stop = 1000,
  monitor = TRUE,
  popSize = 100,
  verbose = TRUE
)

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

data

an data frame containing the variables in the model.

GA_var

if the number of variables is smaller than GA_var, then do exhaustive model search, otherwise use genetic algorithm to do stochastic model search.

maxiterations

the maximum number of iterations to run before the GA search is halted.

runs_til_stop

the number of consecutive generations without any improvement in the best fitness value before the GA is stopped.

monitor

a logical defaulting to TRUE showing the evolution of the search. If monitor = FALSE, any output is suppressed.

popSize

the population size.

verbose

Logical; if TRUE, print a brief summary of results.

Value

modelselect.lm returns a list containing the following components:

models

A data frame of candidate models' BIC and posterior probabilities, sorted by decreasing posterior probability

variables

A data frame of candidate variables' posterior inclusion probabilities

data

The data with variables in the formula.

The function lm.best is used to obtain the linear fitting to the best model by posterior probability or by controlling variables' posterior inclusion probabilities.