Add missing values to a vector given a MCAR, MAR, or MNAR scheme

Description

Given an input vector, replace elements of this vector with missing values according to some scheme. Default method replaces input values with a MCAR scheme (where on average 10% of the values will be replaced with NAs). MAR and MNAR are supported by replacing the default FUN argument.

Usage

add_missing(y, fun = function(y, rate = 0.1, ...) rep(rate, length(y)), ...)

Arguments

Details

Given an input vector y, and other relevant variables inside (X) and outside (Z) the data-set, the three types of missingness are:

MCAR

Missing completely at random (MCAR). This is realized by randomly sampling the values of the input vector (y) irrespective of the possible values in X and Z. Therefore missing values are randomly sampled and do not depend on any data characteristics and are truly random

MAR

Missing at random (MAR). This is realized when values in the dataset (X) predict the missing data mechanism in y; conceptually this is equivalent to P(y = NA | X). This requires the user to define a custom missing data function

MNAR

Missing not at random (MNAR). This is similar to MAR except that the missing mechanism comes from the value of y itself or from variables outside the working dataset; conceptually this is equivalent to P(y = NA | X, Z, y). This requires the user to define a custom missing data function

Value

the input vector y with the sampled NA values (according to the FUN scheme)

Examples

set.seed(1)
y <- rnorm(1000)

## 10% missing rate with default FUN
head(ymiss <- add_missing(y), 10)

## 50% missing with default FUN
head(ymiss <- add_missing(y, rate = .5), 10)

## missing values only when female and low
X <- data.frame(group = sample(c('male', 'female'), 1000, replace=TRUE),
                level = sample(c('high', 'low'), 1000, replace=TRUE))
head(X)

fun <- function(y, X, ...){
    p <- rep(0, length(y))
    p[X$group == 'female' & X$level == 'low'] <- .2
    p
}

ymiss <- add_missing(y, X, fun=fun)
tail(cbind(ymiss, X), 10)

## missingness as a function of elements in X (i.e., a type of MAR)
fun <- function(y, X){
   # missingness with a logistic regression approach
   df <- data.frame(y, X)
   mm <- model.matrix(y ~ group + level, df)
   cfs <- c(-5, 2, 3) #intercept, group, and level coefs
   z <- cfs %*% t(mm)
   plogis(z)
}

ymiss <- add_missing(y, X, fun=fun)
tail(cbind(ymiss, X), 10)

## missing values when y elements are large (i.e., a type of MNAR)
fun <- function(y) ifelse(abs(y) > 1, .4, 0)
ymiss <- add_missing(y, fun=fun)
tail(cbind(y, ymiss), 10)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(SimDesign)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SimDesign/add_missing.Rd_%03d_medium.png", width=480, height=480)
> ### Name: add_missing
> ### Title: Add missing values to a vector given a MCAR, MAR, or MNAR scheme
> ### Aliases: add_missing
> 
> ### ** Examples
> 
> 
> set.seed(1)
> y <- rnorm(1000)
> 
> ## 10% missing rate with default FUN
> head(ymiss <- add_missing(y), 10)
 [1] -0.6264538         NA -0.8356286  1.5952808  0.3295078 -0.8204684
 [7]  0.4874291  0.7383247  0.5757814 -0.3053884
> 
> ## 50% missing with default FUN
> head(ymiss <- add_missing(y, rate = .5), 10)
 [1] -0.6264538         NA         NA         NA  0.3295078 -0.8204684
 [7]         NA         NA  0.5757814 -0.3053884
> 
> ## missing values only when female and low
> X <- data.frame(group = sample(c('male', 'female'), 1000, replace=TRUE),
+                 level = sample(c('high', 'low'), 1000, replace=TRUE))
> head(X)
   group level
1   male  high
2 female  high
3   male  high
4   male   low
5 female  high
6   male  high
> 
> fun <- function(y, X, ...){
+     p <- rep(0, length(y))
+     p[X$group == 'female' & X$level == 'low'] <- .2
+     p
+ }
> 
> ymiss <- add_missing(y, X, fun=fun)
> tail(cbind(ymiss, X), 10)
          ymiss  group level
991  -0.4826525   male  high
992          NA female   low
993   0.5128013   male  high
994   1.0489099   male   low
995   0.1210582 female   low
996  -0.3132929   male  high
997  -0.8806707   male  high
998  -0.4192869   male  high
999  -1.4827517   male  high
1000 -0.6973182   male  high
> 
> ## missingness as a function of elements in X (i.e., a type of MAR)
> fun <- function(y, X){
+    # missingness with a logistic regression approach
+    df <- data.frame(y, X)
+    mm <- model.matrix(y ~ group + level, df)
+    cfs <- c(-5, 2, 3) #intercept, group, and level coefs
+    z <- cfs %*% t(mm)
+    plogis(z)
+ }
> 
> ymiss <- add_missing(y, X, fun=fun)
> tail(cbind(ymiss, X), 10)
          ymiss  group level
991  -0.4826525   male  high
992  -0.6691135 female   low
993   0.5128013   male  high
994          NA   male   low
995   0.1210582 female   low
996  -0.3132929   male  high
997  -0.8806707   male  high
998  -0.4192869   male  high
999  -1.4827517   male  high
1000 -0.6973182   male  high
> 
> ## missing values when y elements are large (i.e., a type of MNAR)
> fun <- function(y) ifelse(abs(y) > 1, .4, 0)
> ymiss <- add_missing(y, fun=fun)
> tail(cbind(y, ymiss), 10)
                 y      ymiss
 [991,] -0.4826525 -0.4826525
 [992,] -0.6691135 -0.6691135
 [993,]  0.5128013  0.5128013
 [994,]  1.0489099         NA
 [995,]  0.1210582  0.1210582
 [996,] -0.3132929 -0.3132929
 [997,] -0.8806707 -0.8806707
 [998,] -0.4192869 -0.4192869
 [999,] -1.4827517 -1.4827517
[1000,] -0.6973182 -0.6973182
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>