Imputing Missing Values

load library

library(missForest)
library(mice)

Make Missing Values

knitr::kable(summary(iris))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 setosa :50
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 versicolor:50
Median :5.800 Median :3.000 Median :4.350 Median :1.300 virginica :50
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 NA
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 NA
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 NA 
iris.mis = prodNA(iris, noNA = 0.1)
knitr::kable(summary(iris.mis))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.200 Min. :1.000 Min. :0.100 setosa :43
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.575 1st Qu.:0.300 versicolor:48
Median :5.700 Median :3.000 Median :4.300 Median :1.300 virginica :45
Mean :5.821 Mean :3.063 Mean :3.746 Mean :1.197 NA’s :14
3rd Qu.:6.400 3rd Qu.:3.350 3rd Qu.:5.100 3rd Qu.:1.800 NA
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 NA
NA’s :14 NA’s :15 NA’s :14 NA’s :18 NA

Treating continuous variables

iris.mis = subset(iris.mis, select = -c(Species))
md.pattern(iris.mis)
   Sepal.Length Petal.Length Sepal.Width Petal.Width   
93            1            1           1           1  0
13            0            1           1           1  1
13            1            1           0           1  1
11            1            0           1           1  1
16            1            1           1           0  1
 1            0            0           1           1  2
 1            1            0           0           1  2
 1            1            1           0           0  2
 1            1            0           1           0  2
             14           14          15          18 61

Impute missing values

  • PMM (Predictive Mean Matching) – For numeric variables
  • logreg(Logistic Regression) – For Binary Variables( with 2 levels)
  • polyreg(Bayesian polytomous regression) – For Factor Variables (>= 2 levels)
  • Proportional odds model (ordered, >= 2 levels)
imputed_Data = mice(iris.mis, m = 1, maxit = 50, method = "pmm", seed = 500)
summary(imputed_Data)
completeData = complete(imputed_Data)
knitr::kable(head(completeData))
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.1 3.5 1.4 0.2
4.9 3.0 1.4 0.2
4.7 3.0 1.3 0.2
4.6 3.1 1.5 0.2
5.0 3.6 1.4 0.2
5.4 3.9 1.7 0.4 
knitr::kable(head(iris.mis))
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.1 3.5 1.4 0.2
4.9 3.0 1.4 NA
4.7 NA 1.3 0.2
4.6 NA 1.5 NA
5.0 3.6 1.4 NA
5.4 3.9 1.7 0.4 
knitr::kable(head(iris))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa

2016-05-19