1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
|
---
title: "Donor based Imputation Methods"
author: Wolfgang Rannetbauer
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Donor based Imputation Methods}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 6,
fig.align = "center"
)
```
## Overview
In addition to Model based Imputation Methods (see `vignette("modelImp")`) the `VIM` package also presents donor based imputation methods, namely Hot-Deck Imputation, k-Nearest Neighbour Imputation and fast matching/imputation based on categorical variable.
This vignette showcases the functions `hotdeck()` and `kNN()`, which can both be used to generate imputations for several variables in a
dataset. Moreover, the function `matchImpute()` is presented, which is in contrast a imputation method based on categorical variables.
## Data
The following example demonstrates the functionality of `hodeck()` and `kNN()` using a subset of `sleep`. The columns have been selected deliberately to include some interactions between the missing values.
```{r setup, message=F}
library(VIM)
library(magrittr)
dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
dataset$BodyWgt <- log(dataset$BodyWgt)
dataset$Span <- log(dataset$Span)
aggr(dataset)
```
The plot indicates several missing values in `Dream`, `NonD`, and `Span. `
```{r}
sapply(dataset, function(x)sum(is.na(x)))
```
## Imputation
The call of the functions is straightforward. We will start by just imputing `NonD` based on the other variables. Besides imputing missing variables for a single variable, these functions also support imputation of multiple variables.
For `matchImpute()` suitable donors are searched based on matching of the categorical variables.
```{r}
imp_hotdeck <- hotdeck(dataset, variable = "NonD") # hotdeck imputation
imp_knn <- kNN(dataset, variable = "NonD") # kNN imputation
imp_match <- matchImpute(dataset, variable = "NonD", match_var = c("BodyWgt","Span")) # match imputation
aggr(imp_knn, delimiter = "_imp")
aggr(imp_match, delimiter = "_imp")
```
We can see that `kNN()` imputed all missing values for `NonD` in our dataset. The same is true for the values imputed via `hotdeck()`.
The specified variables in `matchImpute()` serve as a donor and enable imputation for `NonD`.
### Diagnosing the results
As we can see in the next two plots, the origninal data structure of `NonD` and `Span` is preserved by `hotdeck()`. `kNN()` reveals the typically procedure of methods, which are based on similar data points weighted by the distance.
```{r, fig.height=5}
imp_hotdeck[, c("NonD", "Span", "NonD_imp")] %>%
marginplot(delimiter = "_imp")
imp_knn[, c("NonD", "Span", "NonD_imp")] %>%
marginplot(delimiter = "_imp")
```
`matchImpute()` works by sampling values from the suitable donors and also provides reasonable results.
```{r, fig.height=5}
imp_match[, c("NonD", "Span", "NonD_imp")] %>%
marginplot(delimiter = "_imp")
```
## Performance of method
In order to validate the performance of `kNN()` and to highlight the ability to impute different datatypes the `iris` dataset is used. Firstly, some values are randomly set to `NA`.
```{r}
data(iris)
df <- iris
colnames(df) <- c("S.Length","S.Width","P.Length","P.Width","Species")
# randomly produce some missing values in the data
set.seed(1)
nbr_missing <- 50
y <- data.frame(row = sample(nrow(iris), size = nbr_missing, replace = TRUE),
col = sample(ncol(iris), size = nbr_missing, replace = TRUE))
y<-y[!duplicated(y), ]
df[as.matrix(y)] <- NA
aggr(df)
sapply(df, function(x) sum(is.na(x)))
```
We can see that there are missings in all variables and some observations reveal missing values on several points.
```{r}
imp_knn <- kNN(df)
aggr(imp_knn, delimiter = "imp")
```
The plot indicates that all missing values have been imputed by `kNN()`. The following table displays the rounded first five results of the imputation for all variables.
```{r echo=F,warning=F}
library(reactable)
results <- cbind("TRUE1" = as.numeric(iris[as.matrix(y[which(y$col==1),])]),
"IMPUTED1" = round(as.numeric(imp_knn[as.matrix(y[which(y$col==1),])]),2),
"TRUE2" = as.numeric(iris[as.matrix(y[which(y$col==2),])]),
"IMPUTED2" = round(as.numeric(imp_knn[as.matrix(y[which(y$col==2),])]),2),
"TRUE3" = as.numeric(iris[as.matrix(y[which(y$col==3),])]),
"IMPUTED3" = round(as.numeric(imp_knn[as.matrix(y[which(y$col==3),])]),2),
"TRUE4" = as.numeric(iris[as.matrix(y[which(y$col==4),])]),
"IMPUTED4" = round(as.numeric(imp_knn[as.matrix(y[which(y$col==4),])]),2),
"TRUE5" = (iris[as.matrix(y[which(y$col==5),])]),
"IMPUTED5" = (imp_knn[as.matrix(y[which(y$col==5),])]))[1:5,]
reactable(results, columns = list(
TRUE1 = colDef(name = "True"),
IMPUTED1 = colDef(name = "Imputed"),
TRUE2 = colDef(name = "True"),
IMPUTED2 = colDef(name = "Imputed"),
TRUE3 = colDef(name = "True"),
IMPUTED3 = colDef(name = "Imputed"),
TRUE4 = colDef(name = "True"),
IMPUTED4 = colDef(name = "Imputed"),
TRUE5 = colDef(name = "True"),
IMPUTED5 = colDef(name = "Imputed")
),
columnGroups = list(
colGroup(name = "S.Length", columns = c("TRUE1", "IMPUTED1")),
colGroup(name = "S.Width", columns = c("TRUE2", "IMPUTED2")),
colGroup(name = "P.Length", columns = c("TRUE3", "IMPUTED3")),
colGroup(name = "P.Width", columns = c("TRUE4", "IMPUTED4")),
colGroup(name = "Species", columns = c("TRUE5", "IMPUTED5"))
),
striped = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
|
