https://github.com/indenkun/missmech
To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR).
https://github.com/indenkun/missmech
missing-data r
Last synced: 12 months ago
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To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR).
- Host: GitHub
- URL: https://github.com/indenkun/missmech
- Owner: indenkun
- License: gpl-2.0
- Created: 2024-01-19T00:59:11.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-05T12:21:39.000Z (about 2 years ago)
- Last Synced: 2025-01-27T08:11:45.583Z (about 1 year ago)
- Topics: missing-data, r
- Language: R
- Homepage:
- Size: 97.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
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README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# MissMech
The main purpose of this package is to test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). As a by-product, however, this package can impute incomplete data, is able to perform a test to determine whether data have a multivariate normal distribution or whether the covariances for several populations are equal. The test of MCAR follows the methodology proposed by Jamshidian and Jalal (2010).
It is based on testing equality of covariances between groups consisting of identical missing data patterns. The data are imputed, using two options of normality and distribution free, and the test of equality of covariances between groups with identical missing data patterns is performed also with options of assuming normality (Hawkins test) or non-parametrically.
The user, can optionally use her own method of data imputation as well. Multiple imputation is an option as a diagnostic tool to
help identify cases or variables that contribute to rejection of MCAR, when the MCAR test is rejecetd (See Jamshidian and Jalal, 2010 for details).
As explained in Jamshidian, Jalal, and Jansen (2014), this package can also be used for imputing missing data, test of multivariate normality, and test of equality of covariances between several groups when data are complete.
## Installation
You can install the released version of `MissMech` from CRAN with:
```r
install.packages("MissMech")
```
ans also, you can install the development version of `MissMech` like so from GitHub:
``` r
require("remotes")
remotes::install_github("indenkun/MissMech")
```
## Example
```{r example}
library(MissMech)
# -- Example 1: Data are MCAR and normally distributed
n <- 300
p <- 5
pctmiss <- 0.2
set.seed(1010)
y <- matrix(rnorm(n * p),nrow = n)
missing <- matrix(runif(n * p), nrow = n) < pctmiss
y[missing] <- NA
out <- TestMCARNormality(data=y)
print(out)
# --- Prints the p-value for both the Hawkins and the nonparametric test
summary(out)
# --- Uses more cases
# out1 <- TestMCARNormality(data=y, del.lesscases = 1)
# print(out1)
#---- performs multiple imputation
Out <- TestMCARNormality (data = y, imputation.number = 10)
summary(Out)
boxplot(Out)
#-- Example 2: Data are MCAR and non-normally distributed (t distributed with d.f. = 5)
n <- 300
p <- 5
pctmiss <- 0.2
set.seed(1010)
y <- matrix(rt(n * p, 5), nrow = n)
missing <- matrix(runif(n * p), nrow = n) < pctmiss
y[missing] <- NA
out <- TestMCARNormality(data=y)
print(out)
# Perform multiple imputation
Out_m <- TestMCARNormality (data = y, imputation.number = 20)
boxplot(Out_m)
# One may impute the data using a method other than the methods available in the package
# MissMech. If object "yimputed" set to be imputed data using other methods, e.g. k nearest
# neighbor imputation, then in MissMech it can be implemented as follow
# See also Jamshidian, Jalal, and Jansen (2014) for more information.
# out_k <- TestMCARNormality(data = y, imputed.data = yimputed)
# print(out_k)
#-- Example 3: Data are MAR (not MCAR), but are normally distributed
n <- 300
p <- 5
r <- 0.3
mu <- rep(0, p)
sigma <- r * (matrix(1, p, p) - diag(1, p))+ diag(1, p)
set.seed(110)
eig <- eigen(sigma)
sig.sqrt <- eig$vectors %*% diag(sqrt(eig$values)) %*% solve(eig$vectors)
sig.sqrt <- (sig.sqrt + sig.sqrt) / 2
y <- matrix(rnorm(n * p), nrow = n) %*% sig.sqrt
tmp <- y
for (j in 2:p){
y[tmp[, j - 1] > 0.8, j] <- NA
}
out <- TestMCARNormality(data = y, alpha =0.1)
print(out)
#-- Example 4: Multiple imputation; data are MAR (not MCAR), but are normally distributed
n <- 300
p <- 5
pctmiss <- 0.2
set.seed(1010)
y <- matrix (rnorm(n * p), nrow = n)
missing <- matrix(runif(n * p), nrow = n) < pctmiss
y[missing] <- NA
Out <- OrderMissing(y)
y <- Out$data
spatcnt <- Out$spatcnt
g2 <- seq(spatcnt[1] + 1, spatcnt[2])
g4 <- seq(spatcnt[3] + 1, spatcnt[4])
y[c(g2, g4), ] <- 2 * y[c(g2, g4), ]
out <- TestMCARNormality(data = y, imputation.number = 20)
print(out)
boxplot(out)
# Removing Groups 2 and 4
y1= y[-seq(spatcnt[1]+1,spatcnt[2]),]
out <- TestMCARNormality(data=y1,imputation.number = 20)
print(out)
boxplot(out)
#-- Example 5: Test of homoscedasticity for complete data
n <- 50
p <- 5
r <- 0.4
sigma <- r * (matrix(1, p, p) - diag(1, p)) + diag(1, p)
set.seed(1010)
eig <- eigen(sigma)
sig.sqrt <- eig$vectors %*% diag(sqrt(eig$values)) %*% solve(eig$vectors)
sig.sqrt <- (sig.sqrt + sig.sqrt) / 2
y1 <- matrix(rnorm(n * p), nrow = n) %*% sig.sqrt
n <- 75
p <- 5
y2 <- matrix(rnorm(n * p), nrow = n)
n <- 25
p <- 5
r <- 0
sigma <- r * (matrix(1, p, p) - diag(1, p)) + diag(2, p)
y3 <- matrix(rnorm(n * p), nrow = n) %*% sqrt(sigma)
ycomplete <- rbind(y1 ,y2 ,y3)
y1 [ ,1] <- NA
y2[,c(1 ,3)] <- NA
y3 [ ,2] <- NA
ygroup <- rbind(y1, y2, y3)
out <- TestMCARNormality(data = ygroup, method = "Hawkins", imputed.data = ycomplete)
print(out)
# ---- Example 6, real data
data(agingdata)
TestMCARNormality(agingdata, del.lesscases = 1)
```
Examples are detailed in Jamshidian, Jalal, and Jansen (2014).
For imptation using the K-nearest neighbor method, the paper uses the `kNNImpute()` function from the `imputation` package, but the this package has now been removed from CRAN.
## References
Jamshidian M, Jalal S. Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. Psychometrika. 2010 Dec;75(4):649-674. doi:10.1007/s11336-010-9175-3.
Jamshidian, M., Jalal, S., & Jansen, C. (2014). MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR). Journal of Statistical Software, 56(6), 1–31. doi:10.18637/jss.v056.i06.
## Note
The `MissMech` package was taken over by the current package maintainer from the previous maintainer, Mortaza Jamshidian, and resubmitted to conform to the current CRAN policy.
The basic code is the same as in the previous 1.0.2 versions.
Mortaza Jamshidian, package maintainer and author, has given me permission to change the maintainer.
## Licence
GPL (>= 2)