Multiple imputation with MICE - very simple version for rough completion of a dataset (useful for things like doing a dirty PCA)

MICE_simple

data(cars)
#make some missing values
cars[sample(1:50,size = 10),1]<-NA
cars[sample(1:50,size = 10),2]<-NA

require(mice)
#impute a bunch of data sets
cars.mice<-mice(cars,m=5,maxit=50,meth='pmm',seed=500)

#look at the completions data set
cars.mice$imp$speed

#plot density of values imputed to see how well they worked on average
plot(density(cars.mice$imp$speed[,1]))
lines(density(cars.mice$imp$speed[,2]),col="red")
lines(density(cars.mice$imp$speed[,3]),col="red")
lines(density(cars.mice$imp$speed[,3]),col="blue")
lines(density(cars.mice$imp$speed[,4]),col="green")
lines(density(cars.mice$imp$speed[,4]),col="pink")

#grab one you like and complete the data

cars.completed1<-complete(cars.mice,action = 1)
cars.completed2<-complete(cars.mice,action = 2)
cars.completed3<-complete(cars.mice,action = 3)
cars.completed4<-complete(cars.mice,action = 4)
cars.completed5<-complete(cars.mice,action = 5)

#do whatever you want to do with them.

plot(cars.completed1$speed,cars.completed2$speed,pch=16)