There are two functions in R that seems almost similar yet different:
fitted()
predict() First let’s prepare some data first.
# Packages library(dplyr) # Data set.seed(123) dat <- iris %>% mutate(twoGp = sample(c("Gp1", "Gp2"), 150, replace = T), #create two group factor twoGp = as.
Some note I have written two post previously about multiple imputation using mice package:
A short note on multiple imputation
Variable selection for imputation model in {mice} This post probably my last post about multiple imputation using mice package.
Background Genetic algorithm is inspired by a natural selection process by which the fittest individuals be selected to reproduce. This algorithm has been used in optimization and search problem, and also, can be used for variable selection.
Some note I have written a short post about missing data and multiple imputation in mice package previously. This post will add to that previous post.
Imputation model Imputation model is the model that we use for our imputation approach.
Background Missing data is quite challenging to deal with. Deleting it may be the easiest solution, but may not be the best solution. Missing data can be categorised into 3 types (Rubin, 1976):