How do I specify a class variable in mice for 2 level multiple imputation?
I have repeated measures data for some patients. Each patient is measured 3 times, but data can be missing in each measurement since some patients can be lost to followup or not show up for appointments.
Modifying data after mice and converting data back to mids object
I am unable to reproduce the error in question and cannot attach my full data. Any help understanding the error received would be appreciated.
Using adjustedCurve package after multiple imputation – error: arguments of zero length and no non-missing arguments to max
I am having some issues creating adjusted cumulative incidence function using Kaplan Meier estimator after mice. I repeatedly get an error and, being fairly new to R, I am not sure what it means.
Bug in as.mids()
I have the following problem:
There seems to be a problem with as.mids() in R. The values in a long-Format differ from those after as.mids() was applied. In the first I have a difference for scaled data, in the second the median split is incorrect after transformation.
Process data after mice imputation to have different dimensions and convert back to mids object
After performing imputation, I would like to perform a few data processing steps like recoding variables and removing rows, before converting the data to a mids object in order to fit models. However, mice seems to expect the same number of rows and throws an error that more rows are expected. Is there a way to change the imputed data shape and convert to mids object without getting an error? I’m happy to give a reproducible example if needed.
mice in R – pooled mean by group after imputation
Assume we use the following example from the mice package:
Inputting in Mice using a column Total as a condition R
ID Year Maths Eng Total 1 2010 20 15 35 1 2011 17 NA 40 2 2010 NA NA 46 2 2011 NA NA 39 3 2010 NA 15 41 3 2011 18 NA 43 4 2010 NA 15 NA 4 2011 NA 18 NA Hello, I am trying to use the mice package to […]