The mice
package implements
a method to deal with missing data. The package creates multiple
imputations (replacement values) for multivariate missing data. The
method is based on Fully Conditional Specification, where each
incomplete variable is imputed by a separate model. The MICE
algorithm
can impute mixes of continuous, binary, unordered categorical and
ordered categorical data. In addition, MICE can impute continuous
two-level data, and maintain consistency between imputations by means of
passive imputation. Many diagnostic plots are implemented to inspect the
quality of the imputations.
The mice
package can be installed from CRAN as follows:
install.packages("mice")
The latest version can be installed from GitHub as follows:
install.packages("devtools")
devtools::install_github(repo = "amices/mice")
library(mice, warn.conflicts = FALSE)
# show the missing data pattern
md.pattern(nhanes)
#> age hyp bmi chl
#> 13 1 1 1 1 0
#> 3 1 1 1 0 1
#> 1 1 1 0 1 1
#> 1 1 0 0 1 2
#> 7 1 0 0 0 3
#> 0 8 9 10 27
The table and the graph summarize where the missing data occur in the
nhanes
dataset.
# multiple impute the missing values
imp <- mice(nhanes, maxit = 2, m = 2, seed = 1)
#>
#> iter imp variable
#> 1 1 bmi hyp chl
#> 1 2 bmi hyp chl
#> 2 1 bmi hyp chl
#> 2 2 bmi hyp chl
# inspect quality of imputations
stripplot(imp, chl, pch = 19, xlab = "Imputation number")
In general, we would like the imputations to be plausible, i.e., values that could have been observed if they had not been missing.
# fit complete-data model
fit <- with(imp, lm(chl ~ age + bmi))
# pool and summarize the results
summary(pool(fit))
#> term estimate std.error statistic df p.value
#> 1 (Intercept) 9.08 73.09 0.124 4.50 0.9065
#> 2 age 35.23 17.46 2.017 1.36 0.2377
#> 3 bmi 4.69 1.94 2.417 15.25 0.0286
The complete-data is fit to each imputed dataset, and the results are combined to arrive at estimates that properly account for the missing data.
mice 3.0
Version 3.0 represents a major update that implements the following features:
blocks
: The main algorithm iterates over blocks. A block is simply
a collection of variables. In the common MICE algorithm each block
was equivalent to one variable, which - of course - is the default;
The blocks
argument allows mixing univariate imputation method
multivariate imputation methods. The blocks
feature bridges two
seemingly disparate approaches, joint modeling and fully conditional
specification, into one framework;
where
: The where
argument is a logical matrix of the same size
of data
that specifies which cells should be imputed. This opens
up some new analytic possibilities;
Multivariate tests: There are new functions D1()
, D2()
, D3()
and anova()
that perform multivariate parameter tests on the
repeated analysis from on multiply-imputed data;
formulas
: The old form
argument has been redesign and is now
renamed to formulas
. This provides an alternative way to specify
imputation models that exploits the full power of R’s native
formula’s.
Better integration with the tidyverse
framework, especially for
packages dplyr
, tibble
and broom
;
Improved numerical algorithms for low-level imputation function. Better handling of duplicate variables.
Last but not least: A brand new edition AND online version of Flexible Imputation of Missing Data. Second Edition.
See MICE: Multivariate Imputation by Chained Equations for more resources.
I’ll be happy to take feedback and discuss suggestions. Please submit these through Github’s issues facility.
mice
ampute
futuremice
: Wrapper for parallel MICE imputation through
futuresThe cute mice sticker was designed by Jaden M. Walters. Thanks Jaden!
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