Introduce NA's not at Random in a data.frame
This function takes a data.frame and injects missing values not-at-random, but according to a glm logit model. The MNARData object contains the coefficients and the predictor variables.
Note that the data.frame in the first argument should only contain the variables that are to be injected.
This assumes no covariance between missingness for different variables.
Also note, this probably only works for binary categorical predictor variables.
mtcars_mnar_data = MNARData(
coef=matrix(c(2,-1,0, 1,0,2),3,2),
data=as.matrix(cbind(rep(1, nrow(mtcars)), mtcars[c('vs','am')])))
mtcars_na = mnar_inject(mtcars[c('hp','mpg')], mtcars_mnar_data)
head(mtcars_na)
Simulate a Likert data set with Missing Values and Misfitting Items
Misfitting items are for now loosely defined: They are just random responses without any structure. A more sophisticated way will be to simulate items drawn from other structures (e.g. loading to more than one factors etc).
Return a list with the requested data.frames, by default a complete set and a MAR injected one. Additionally, a third element mnar is added to the list if requested with mnar=TRUE.
foo_mnar_data = function(n_obs=500){
MNARData(
coef = matrix(c(2,-1,0,0),2,2),
data = cbind(
category1 = c(rep(0,n_obs/2), rep(1,n_obs/2)),
category2 = c(rep(0,n_obs/2), rep(1,n_obs/2))))
}
foo = sim_likert(mnar=T, mnar_data=foo_mnar_data(), mnar_cols=c('Item1', 'Item2'))
str(foo)