Hi, and thanks in advance.

I have used the following to try to obtain singly-imputed values for missing data comprising no more than 15%
of any variable in the data:

> library(Hmisc)
> some.df = read.csv("N:/.../some.csv", header = TRUE, stringsAsFactors = TRUE)
> some.trans <- transcan(~ contin.var1 + contin.var2 + categ.var1 + categ.var2,
> categorical =c("categ.var1","categ.var2"),
> transformed = TRUE,
> imputed = TRUE,
> impcat = "score",
> data = some.df,
> iter.max = 100,
> shrink = TRUE,
> method = "canonical"
> )
> attach(some.df,pos=1)
> some.df.imputed <- impute(some.trans)

It seems to run, but the object "some.df.imputed" isn't a dataframe, and R issues the message

> Imputed missing values with the following frequencies
> and stored them in variables with their original names:
>
> contin.var1     contin.var2
>         13                    9

which seems to imply that the categorical variables were not imputed.  What I want, simply put, is a dataframe
with imputed values for both the continuous and categorical variables.  I'm sure I'm doing something silly.
Any help would be greatly appreciated.


Thanks,
David




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