Imputation

Changing the missing value imputation in vtreat

For this example, we will use the UnsupervisedTreatment, but the same parameters can be used with the other treatment plans as well.

A simple data example

Here we create a simple data set where the inputs have missing values.

library(vtreat)

## Loading required package: wrapr

d = data.frame(
    "x" = c(0, 1, 1000, NA),
    "w" = c(3, 6, NA, 100),
    "y" = c(0, 0, 1, 1)
)

knitr::kable(d)

x	w	y
0	3	0
1	6	0
1000	NA	1
NA	100	1

Some of the summary statistics of d. We're primarily interested in the inputs x and w.

summary(d)

##        x                w                y      
##  Min.   :   0.0   Min.   :  3.00   Min.   :0.0  
##  1st Qu.:   0.5   1st Qu.:  4.50   1st Qu.:0.0  
##  Median :   1.0   Median :  6.00   Median :0.5  
##  Mean   : 333.7   Mean   : 36.33   Mean   :0.5  
##  3rd Qu.: 500.5   3rd Qu.: 53.00   3rd Qu.:1.0  
##  Max.   :1000.0   Max.   :100.00   Max.   :1.0  
##  NA's   :1        NA's   :1

The default missing value imputation

By default, vtreat fills in missing values with the mean value of the column, and adds an advisory *_is_bad column to mark the location of the original missing values.

treatments <- designTreatmentsZ(d, 
                                varlist = c('x', 'w'), 
                                verbose = FALSE)
d_treated <- prepare(treatments, 
                     d)
d_treated$y <- d$y
knitr::kable(d_treated)

x	x_isBAD	w	w_isBAD	y
0.0000	0	3.00000	0	0
1.0000	0	6.00000	0	0
1000.0000	0	36.33333	1	1
333.6667	1	100.00000	0	1

Changing the imputation strategy

If you do not want to use the mean to fill in missing values, you can change the imputation function using the parameter missingness_imputation. Here, we fill in missing values with the median.

median2 <- function(x, wts) {
  median(x)
}

treatments <- designTreatmentsZ(d, 
                                varlist = c('x', 'w'), 
                                verbose = FALSE,
                                missingness_imputation = median2)
d_treated <- prepare(treatments, 
                     d)
d_treated$y <- d$y
knitr::kable(d_treated)

x	x_isBAD	w	w_isBAD	y
0	0	3	0	0
1	0	6	0	0
1000	0	6	1	1
1	1	100	0	1

You can also use a constant value instead of a function. Here we replace missing values with the value -1.

treatments <- designTreatmentsZ(d, 
                                varlist = c('x', 'w'), 
                                verbose = FALSE,
                                missingness_imputation = -1)
d_treated <- prepare(treatments, 
                     d)
d_treated$y <- d$y
knitr::kable(d_treated)

x	x_isBAD	w	w_isBAD	y
0	0	3	0	0
1	0	6	0	0
1000	0	-1	1	1
-1	1	100	0	1

Changing the imputation strategy per column

You can control the imputation strategy per column via the map imputation_map. Any column not named in the imputation map will use the imputation strategy specified by the missingness_imputation parameter (which is the mean by default).

Here we use the maximum value to fill in the missing values for x and the value 0 to fill in the missing values for w.

max2 <- function(x, wts) {
  max(x)
}

treatments <- designTreatmentsZ(d, 
                                varlist = c('x', 'w'), 
                                verbose = FALSE,
                                imputation_map = list(
                                  x = max2,
                                  w = 0
                                ))
d_treated <- prepare(treatments, 
                     d)
d_treated$y <- d$y
knitr::kable(d_treated)

x	x_isBAD	w	w_isBAD	y
0	0	3	0	0
1	0	6	0	0
1000	0	0	1	1
1000	1	100	0	1

If we don't specify a column, vtreat looks atmissingness_imputation (in this case, -1).

treatments <- designTreatmentsZ(d, 
                                varlist = c('x', 'w'), 
                                verbose = FALSE,
                                missingness_imputation = -1,
                                imputation_map = list(
                                  x = max2
                                ))
d_treated <- prepare(treatments, 
                     d)
d_treated$y <- d$y
knitr::kable(d_treated)

x	x_isBAD	w	w_isBAD	y
0	0	3	0	0
1	0	6	0	0
1000	0	-1	1	1
1000	1	100	0	1

If missingness_imputation is not specified, vtreat uses a weighted mean.

treatments <- designTreatmentsZ(d, 
                                varlist = c('x', 'w'), 
                                verbose = FALSE,
                                imputation_map = list(
                                  x = max2
                                ))
d_treated <- prepare(treatments, 
                     d)
d_treated$y <- d$y
knitr::kable(d_treated)

x	x_isBAD	w	w_isBAD	y
0	0	3.00000	0	0
1	0	6.00000	0	0
1000	0	36.33333	1	1
1000	1	100.00000	0	1