The mira object is generated by the with.mids() function.
The as.mira()
function takes the results of repeated complete-data analysis stored as a
list, and turns it into a mira object that can be pooled.
Details
In versions prior to mice 3.0 pooling required only that
coef() and vcov() methods were available for fitted
objects. This feature is no longer supported. The reason is that vcov()
methods are inconsistent across packages, leading to buggy behaviour
of the pool() function. Since mice 3.0+, the broom
package takes care of filtering out the relevant parts of the
complete-data analysis. It may happen that you'll see the messages
like No method for tidying an S3 object of class ... or
Error: No glance method for objects of class .... The royal
way to solve this problem is to write your own glance() and tidy()
methods and add these to broom according to the specifications
given in https://broom.tidymodels.org.
The mira class of objects has methods for the
following generic functions: print, summary.
Many of the functions of the mice package do not use the
S4 class definitions, and instead rely on the S3 list equivalent
oldClass(obj) <- "mira".
Slots
-
#'
.Data:Object of class
"list"containing the following slots:call:The call that created the object.
call1:The call that created the
midsobject that was used incall.nmis:An array containing the number of missing observations per column.
analyses:A list of
mcomponents containing the individual fit objects from each of themcomplete data analyses.
References
van Buuren S and Groothuis-Oudshoorn K (2011). mice:
Multivariate Imputation by Chained Equations in R. Journal of
Statistical Software, 45(3), 1-67.
doi:10.18637/jss.v045.i03