[R] mice: selecting small subset of variables to impute from dataset with many variables (> 2500)
bgunter@4567 @end|ng |rom gm@||@com
Thu Jul 14 20:09:52 CEST 2022
If I understand your query correctly, you can use negative indexing to
omit variables. See ?'[' for details.
> dat <- data.frame (a = 1:3, b = letters[1:3], c = 4:6, d = letters[5:7])
a b c d
1 1 a 4 e
2 2 b 5 f
3 3 c 6 g
1 1 4
2 2 5
3 3 6
Of course you have to know the numerical index of the columns you wish
to omit, but somethingh of the sort seems unavoidable in any case.
On Thu, Jul 14, 2022 at 11:00 AM Ian McPhail <ivmcphail using gmail.com> wrote:
> I am looking for some advice on how to select subsets of variables for
> imputing when using the mice package.
> From Van Buuren's original mice paper, I see that selecting variables to be
> 'skipped' in an imputation can be written as:
> ini <- mice(nhanes2, maxit = 0, print = FALSE)
> pred <- ini$pred
> pred[, "bmi"] <- 0
> meth <- ini$meth
> meth["bmi"] <- ""
> With the last two lines specifying the the "bmi" variable gets skipped over
> and not imputed.
> And I have come across other examples, but all that I have seen lay out a
> method of skipping variables where EVERY variable is named (as "bmi" is
> named above). I am wondering if there is a reasonably easy way to select
> out approximately 30 variables for imputation from a larger dataset with
> around 2500 variables, without having to name all 2450+ other variables.
> Thank you,
> [[alternative HTML version deleted]]
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