glmfitmiss (version 2.1.0)

simulateData: 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
)

Value

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

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

Examples

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