[R] mice: selecting small subset of variables to impute from dataset with many variables (> 2500)

[Ebert,Timothy Aaron]

Maybe this is too simple but could you use the select() function from dplyr?
Tim

-----Original Message-----
From: R-help <r-help-bounces using r-project.org> On Behalf Of Bert Gunter
Sent: Thursday, July 14, 2022 2:10 PM
To: Ian McPhail <ivmcphail using gmail.com>
Cc: R-help <r-help using r-project.org>
Subject: Re: [R] mice: selecting small subset of variables to impute from dataset with many variables (> 2500)

[External Email]

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]) dat
  a b c d
1 1 a 4 e
2 2 b 5 f
3 3 c 6 g
> dat[,-c(2,4)]
  a c
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.

Cheers,
Bert

On Thu, Jul 14, 2022 at 11:00 AM Ian McPhail <ivmcphail using gmail.com> wrote:
>
> Hello,
>
> 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,
>
> Ian
>
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>
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