My question is how do I go about adding the imputed data to the quakes.missing data frame?
I've created a reproducible example below.
library(Hmisc)
library(missForest) #load packages
data("quakes")
quakes
quakes.missing <- prodNA(quakes, noNA = 0.1) #create missing values
summary(is.na(quakes.missing)) #confirm that data is missing
impute_quakes <- aregImpute(~ lat + long + depth + mag + stations, data = quakes.missing, n.impute = 5)
impute_quakes
Since you have 5 imputations, you'd have 5 full data frames, you can pull them out with a function like this:
fill_data <- function(impute = impute_quakes, data = quakes.missing, im = 1) {
cbind.data.frame(impute.transcan(x = impute,
imputation = im,
data = data,
list.out = TRUE,
pr = FALSE))
}
full_dat1 <- fill_data(im = 1)
full_dat2 <- fill_data(im = 2)
...
(also, I'm sure you are aware, but Hmisc
also has a great function fit.mult.impute
so you don't need to pull out full data frames in order to perform analyses)