With the miceafter
package you can apply statistical and
pooled analyses after multiple imputation. Therefore the name
‘miceafter’. The package contains a variety of statistical tests like
the pool_levenetest
function to pool Levene’s tests across
multiply imputed datasets or the pool_propdiff_nw function
to pool the difference between proportions according to method
Newcombe-Wilson. The package also contains a function
pool_glm
to pool and select linear and logistic regression
functions. Functions can also be used in combination with the
%>%
(Pipe) operator.
More and more statistical analyses and pooling functions will be added over time to form a framework of statistical tests that can be applied and pooled across multiply imputed datasets.
This example shows you how to pool the Levene test across 5 multiply imputed datasets. The pooling method that is used is method D1.
library(miceafter)
# Step 1: Turn data frame with multiply imputed datasets into object of 'milist'
<- df2milist(lbpmilr, impvar="Impnr")
imp_dat
# Step 2: Do repeated analyses across multiply imputed datasets
<- with(imp_dat, expr=levene_test(Pain ~ factor(Carrying)))
ra
# Step 3: Pool repeated test results
<- pool_levenetest(ra, method="D1")
res
res#> F_value df1 df2 P(>F) RIV
#> [1,] 1.586703 2 115.3418 0.209032 0.1809493
#> attr(,"class")
#> [1] "mipool"
library(miceafter)
library(magrittr)
%>%
lbpmilr df2milist(impvar="Impnr") %>%
with(expr=levene_test(Pain ~ factor(Carrying))) %>%
pool_levenetest(method="D1")
#> F_value df1 df2 P(>F) RIV
#> [1,] 1.586703 2 115.3418 0.209032 0.1809493
#> attr(,"class")
#> [1] "mipool"
library(miceafter)
# Step 1: Turn data frame with multiply imputed datasets into object of 'milist'
<- df2milist(lbpmilr, impvar="Impnr")
imp_dat
# Step 2: Do repeated analyses across multiply imputed datasets
<- with(imp_dat,
ra expr=propdiff_wald(Chronic ~ Radiation, strata = TRUE))
# Step 3: Pool repeated test results
<- pool_propdiff_nw(ra)
res
res#> Prop diff CI L NW CI U NW
#> [1,] 0.2786 0.1199 0.419
#> attr(,"class")
#> [1] "mipool"
See for more functions the package website
The main functions of the package are the df2milist
,
list2milist
, mids2milist
and the
with.milist
functions. The df2milist
function
turns a data frame with multiply imputed datasets into an object of
class milist
, the list2milist
does this for a
list with multiply imputed datasets and the mids2milist
for
objects of class mids
. These milist
object can
than be used with the with.milist
function to apply
repeated statistical analyses across the multiply imputed datasets.
Subsequently, pooling functions are available in the form of separate
pool
functions.
You can install the development version from GitHub with:
# install.packages("devtools")
::install_github("mwheymans/miceafter") devtools
Cite the package as:
Heymans (2021). miceafter: Data Analysis and Pooling after Multiple Imputation.
Martijn W 0.1.0. https://mwheymans.github.io/miceafter/ R package version