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.
First of all, this write up is mean for a beginner in R.
Things can be done in many ways in R. In facts, R has been very flexible in this regard compared to other statistical softwares.
I have heard quite a several times that apply function is faster than loop function in R. Loop function is said to be inefficient, though in certain situation loop is the only way.