ImputeRobust: Multiple Imputation with GAMLSS
The ImputeRobust package adds to https://cran.r-project.org/web/packages/mice/index.html an imputation method based on generalized additive models for location, scale, and shape introduced by de Jong (2012), de Jong, van Buuren and Spiess (2016). It has been tested mostly with continous variables, count, and binary data.
Installation
The latest version can be installed from GitHub as follows:
install.packages("devtools")
devtools::install_git(url = "https://github.com/dsalfran/ImputeRobust",
branch = "master")Main functions
The main function in the ImputeRobust package is mice.impute.gamlss() which model the response distribution with a Normal distribution. All other functions are variants of the main function with a different fixed response distribution.
| Function Name | Response distribution |
|---|---|
mice.impute.gamlss() |
Normal |
mice.impute.gamlssBI() |
Binomial |
mice.impute.gamlssJSU() |
Johnson's SU |
mice.impute.gamlssPO() |
Poisson |
mice.impute.gamlssTF() |
t-distribution |
mice.impute.gamlssZIBI() |
Zero inflated binomial |
mice.impute.gamlssZIP() |
Zero inflated Poisson |
Examples
The package is intended to be used with mice, adding new methods based on GAMLSS. For example, let's say we have a data sets with missing values:
# First lines of the data set
head(sample.data)
#> X.1 X.2 X.3 X.4 y
#> 1 -0.89662664 NA NA NA 6.943309
#> 2 -1.75990198 NA NA 1 5.272678
#> 3 0.60351069 4 -2.330010 0 3.669100
#> 4 -0.05911172 2 -1.010365 0 -2.995331
#> 5 0.95021728 4 NA 1 5.736138
#> 6 1.61066030 NA NA 1 18.415576# Missing pattern
md.pattern(sample.data)
#> X.1 y X.4 X.2 X.3
#> 125 1 1 1 1 1 0
#> 39 1 1 1 1 0 1
#> 64 1 1 1 0 0 2
#> 22 1 1 0 0 0 3
#> 0 0 22 86 125 233# Create the imputed data sets
imputed.sets <- mice(sample.data,
method = c("gamlss", "gamlssPO",
"gamlssJSU", "gamlssBI", "gamlss"),
visitSequence = "monotone",
maxit = 1, seed = 97123)In the following plot the red circles represent the imputed values and the blue circles the observed data. 
References
de Jong, R., van Buuren, S. & Spiess, M. (2016) Multiple Imputation of Predictor Variables Using Generalized Additive Models. Communications in Statistics -- Simulation and Computation, 45(3), 968--985.
de Jong, Roel. (2012). “Robust Multiple Imputation.” Universität Hamburg. .
Rigby, R. A., and Stasinopoulos, D. M. (2005). Generalized Additive Models for Location, Scale and Shape. Journal of the Royal Statistical Society: Series C (Applied Statistics) 54 (3): 507–54.