makemar {CALIBERrfimpute} | R Documentation |
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.
makemar(simdata, prop = 0.2)
simdata |
simulated dataset created by |
prop |
proportion of missing values to be introduced in x1 and x2. |
This function is used for simulation and testing.
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 |
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