simulateMissDfYorX {glmfitmiss} | R Documentation |
Simulate missing covariate or missing responses data based on an input covariate data
Description
This function generates missing covariate or missing responses data. 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
simulateMissDfYorX(
dataCov,
truebeta = c(1, -1, 1, 5),
truealpha = c(-1, 5, -1, -1, -1, 0.01),
x2Mar = c(1, -1, -1),
ymiss = FALSE,
nsim = 1
)
Arguments
dataCov |
input data, the default number of covariates is 7 (5+2) |
truebeta |
the beta parameter to be used to generate binary responses 1/0 s |
truealpha |
to be used to generate nonignorable missing values based on the model |
x2Mar |
to be used to generate missing values in x2 based on the model |
ymiss |
to be used for missing responses, default is FALSE |
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 <- simulateMissDfYorX(demo_df, nsim=2)
testMissData <- simulated_df$dataMissing
head(testMissData)