combine_impute_and_nonimpute_20181116.R

# Stackoverflow: https://stackoverflow.com/questions/53338606/combine-imputed-and-non-imputed-data
# Combine imputed and non imputed data
id <- c(1,2,3,4,5,6,7,8,9,10)
age <- c(60,NA,90,55,60,61,77,67,88,90)
bmi <- c(30,NA,NA,23,24,NA,27,23,26,21)
time <- c(62,88,85,NA,68,62,89,62,70,99)
dat <- data.frame(id, age, bmi, time)
dat

id <- c(1,2,3,4,5,6,7,8,9,10)
m1 <- c(60,78,90,55,60,61,77,67,88,90)
m2 <- c(30,44,35,23,24,22,27,23,26,21)
m3 <- c(62,88,85,78,68,62,89,62,70,99)
dat2 <- data.frame(id, m1, m2, m3)
dat2

# install.packages("mice")
library(mice)
impdat <- mice(dat, 
               seed = 2018, 
               maxit = 10, 
               m = 5)
impdat
# Class: mids
# Number of multiple imputations:  5 
# Imputation methods:
#   id   age   bmi  time 
# "" "pmm" "pmm" "pmm" 
# PredictorMatrix:
#   id age bmi time
# id    0   1   1    1
# age   1   0   1    1
# bmi   1   1   0    1
# time  1   1   1    0

impdat = complete(impdat)
impdat

# id age bmi time
# 1   1  60  30   62
# 2   2  60  24   88
# 3   3  90  24   85
# 4   4  55  23   89
# 5   5  60  24   68
# 6   6  61  24   62
# 7   7  77  27   89
# 8   8  67  23   62
# 9   9  88  26   70
# 10 10  90  21   99

final_data = cbind(impdat, dat2)
final_data
# id age bmi time id m1 m2 m3
# 1   1  60  30   62  1 60 30 62
# 2   2  60  24   88  2 78 44 88
# 3   3  90  24   85  3 90 35 85
# 4   4  55  23   89  4 55 23 78
# 5   5  60  24   68  5 60 24 68
# 6   6  61  24   62  6 61 22 62
# 7   7  77  27   89  7 77 27 89
# 8   8  67  23   62  8 67 23 62
# 9   9  88  26   70  9 88 26 70
# 10 10  90  21   99 10 90 21 99