simulateData: Simulate data based on an input covariate data

In glmfitmiss: Fitting GLMs with Missing Data in Both Responses and Covariates

Simulate data based on an input covariate data

Description

This function generates missing data both in the response variables as well as in the predictors. The missing data generation in the last two supplied covariates will be generated based on a predefined mechanisms. Missing data generation in the response variable will be based on the suppilied true alpha.

Usage

simulateData(
  dataCov,
  truebeta = c(1, -1, 1, 5),
  truealpha = c(-1, 5, -1, -1, -1, 0.01),
  nsim = 2
)

Arguments

dataCov	input data, the default number of covariates is 7 (5+2)
truebeta	the beta parameter to be used to generate binary response values 1/0 s logit(y=1)=x1+x2+x3
truealpha	to be used to generate nonignorable missing values based on the model logit(R=1)=y+x1+x2+x3+x4+..
nsim	number of simulated dataset, default is 2

Value

returns a list with original data called originalData and a data with imputed missing values dataMissing

Examples

demo_df <- simulateCovariateData(100, nCov=6)
simulated_df <- simulateData(demo_df, nsim=2)
testMissData <- simulated_df$dataMissing
head(testMissData)

glmfitmiss documentation built on June 8, 2025, 1:59 p.m.