ssvs_mi: Perform SSVS on Multiply Imputed Datasets

In sabainter/SSVS: Functions for Stochastic Search Variable Selection (SSVS)

Perform SSVS on Multiply Imputed Datasets

Description

This function performs Stochastic Search Variable Selection (SSVS) analysis on multiply imputed datasets for a given set of predictors and a response variable. It supports continuous response variables and calculates aggregated results across multiple imputations.

Usage

ssvs_mi(
  data,
  y,
  x,
  imp,
  imp_num = 5,
  interval = 0.9,
  continuous = TRUE,
  progress = FALSE
)

Arguments

data	A dataframe containing the variables of interest, including an .imp column for imputation identifiers.
y	The response variable (character string).
x	A vector of predictor variable names.
imp	The imputation variable.
imp_num	The number of imputations to process (default is 5).
interval	Confidence interval level for summary results (default is 0.9).
continuous	If TRUE, treat the response variable as continuous. If FALSE, treat the response variable as binary.
progress	Logical indicating whether to display progress (default is FALSE).

Value

An ssvs_mi object containing aggregated results across imputations that can be used in summary().

Examples

# example 1: continuous response variable
data(imputed_mtcars)
outcome <- 'qsec'
predictors <- c('cyl', 'disp', 'hp', 'drat', 'wt', 'vs', 'am', 'gear', 'carb','mpg')
imputation <- '.imp'
results <- ssvs_mi(data = imputed_mtcars, y = outcome, x = predictors, imp = imputation)

# example 2: binary response variable
data(imputed_affairs)
outcome <- "hadaffair"
predictors <- c("gender", "age", "yearsmarried", "children", "religiousness",
"education", "occupation", "rating")
imputation <- '.imp'
results <- ssvs_mi(data = imputed_affairs, x = predictors, y = outcome, continuous = FALSE, imp = imputation)

sabainter/SSVS documentation built on April 17, 2025, 12:48 p.m.