makemar: Creates artificial missing at random missingness

In CALIBERrfimpute: Multiple Imputation Using MICE and Random Forest

Creates artificial missing at random missingness

Description

Introduces missingness into x1 and x2 into a data.frame of the format produced by simdata, for use in the simulation study. The probability of missingness depends on the logistic of the fully observed variables y and x3; hence it is missing at random but not missing completely at random.

Usage

makemar(simdata, prop = 0.2)

Arguments

simdata	simulated dataset created by simdata.
prop	proportion of missing values to be introduced in x1 and x2.

Details

This function is used for simulation and testing.

Value

A data.frame with columns:

y	dependent variable, based on the model y = x1 + x2 + x3 + normal error
x1	partially observed continuous variable
x2	partially observed continuous or binary (factor) variable
x3	fully observed continuous variable
x4	variable not in the model to predict y, but associated with x1, x2 and x3; used as an auxiliary variable in imputation

See Also

simdata

Examples

set.seed(1)
mydata <- simdata(n=100)
mymardata <- makemar(mydata, prop=0.1)
# Count the number of missing values
sapply(mymardata, function(x){sum(is.na(x))})
# y x1 x2 x3 x4 
# 0 11 10  0  0 

CALIBERrfimpute documentation built on July 4, 2024, 3:02 p.m.