If I have a data frame contain 3 variables :
origdata <- data.frame(
age <- c(22, 45, 50, 80, 55, 45, 60, 24, 18, 15),
bmi <- c(22, 24, 26, 27, 28, 30, 27, 25.5, 18, 25),
hyp <- c(1, 2, 4, 3, 1, 2, 1, 5, 4, 5) )
I created MCAR (missing complete at random) data :
halpha <- 0.1
# MCAR for attribute (1) age:
mcar <- runif(10, min = 0, max = 1)
age.mcar <- ifelse(mcar < alpha, NA, origdata$age)
# MCAR for attribute (2) bmi:
mcar <- runif(10, min = 0, max = 1)
bmi.mcar <- ifelse(mcar < alpha, NA, origdata$bmi)
# MCAR for attribute (3) hyp:
mcar <- runif(10, min = 0, max = 1)
hyp.mcar <- ifelse(mcar < alpha, NA, origdata$hyp)
After that I used the mice package to impute the missing value as follows:
install.packages("mice")
library("mice")
imp <- mice(df, 10) # 10 is mean 10 iteration imputing data
fill1 <- complete(imp, 1) # dataset 1
fill2 <- complete(imp, 2) # dataset 2
allfill <- complete(imp, "long") # all iterations together
My question is: I want to find RMSE for all 10 datasets individually by using a loop. This is my RMSE equation :
RMSE <- sqrt((sum((origdata - fill)^2)) / sum(is.na(df)))
I mean to make a loop to find the RMSE for each imputed dataset individually:
RMSE1 (for dataset #1)
RMSE2 (for dataset #2)
...
RMSE10 (for dataset #10)
And I also want to know which dataset is best for impute NAs.
loop in R:
m <- imp$m # number of imputations
RSME <- rep(NA, m)
for (i in seq_len(m)) {
fill <- complete(imp, i)
RMSE[i] <- (sqrt((sum((orgdata - fill)^2))/sum(is.na(x))))
}