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' imp_dat <- df2milist(lbpmilr, impvar="Impnr") # Step 2: Do repeated analyses across multiply imputed datasets ra <- with(imp_dat, expr=levene_test(Pain ~ factor(Carrying))) # Step 3: Pool repeated test results res <- pool_levenetest(ra, method="D1") res #> F_value df1 df2 P(>F) RIV #> [1,] 1.586703 2 115.3418 0.209032 0.1809493 #> attr(,"class") #>  "mipool"
library(miceafter) # Step 1: Turn data frame with multiply imputed datasets into object of 'milist' imp_dat <- df2milist(lbpmilr, impvar="Impnr") # Step 2: Do repeated analyses across multiply imputed datasets ra <- with(imp_dat, expr=propdiff_wald(Chronic ~ Radiation, strata = TRUE)) # Step 3: Pool repeated test results res <- pool_propdiff_nw(ra) res #> Prop diff CI L NW CI U NW #> [1,] 0.2786 0.1199 0.419 #> attr(,"class") #>  "mipool"
See for more functions the package website
The main functions of the package are the
mids2milist and the
with.milist functions. The
df2milist function turns a data frame with multiply imputed datasets into an object of class
list2milist does this for a list with multiply imputed datasets and the
mids2milist for objects of class
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
You can install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("mwheymans/miceafter")