Hello,
You can use mice() argument predictorMatrix to tell mice() which
variables/blocks are used when imputing which column. If the column
vector is set to zeros, no column or block will used in its imputation.
library(mice)
predmat <- matrix(1L, ncol(nhanes2), ncol(nhanes2),
dimnames = list(names(nhanes2), names(nhanes2)))
diag(predmat) <- 0L
predmat[, "bmi"] <- 0L
predmat
#> age bmi hyp chl
#> age 0 0 1 1
#> bmi 1 0 1 1
#> hyp 1 0 0 1
#> chl 1 0 1 0
Then use argument where to skip the variables you do not want imputed.
Note that this is not the same as not being imputed according to
variables shown above as rownames of predmat.
The default of where is the matrix is.na(nhanes2) so make a copy of this
matrix then set column "bmi" to FALSE. Then call mice().
predmat <- matrix(1L, ncol(nhanes2), ncol(nhanes2),
dimnames = list(names(nhanes2), names(nhanes2)))
diag(predmat) <- 0L
predmat[, "bmi"] <- 0L
predmat
#> age bmi hyp chl
#> age 0 0 1 1
#> bmi 1 0 1 1
#> hyp 1 0 0 1
#> chl 1 0 1 0
not_bmi <- is.na(nhanes2)
not_bmi[, "bmi"] <- FALSE
ini_all <- mice(nhanes2, print = FALSE)
ini_bmi <- mice(nhanes2,
predictorMatrix = predmat,
where = not_bmi,
print = FALSE)
cmpl_all <- complete(ini_all)
head(cmpl_all)
#> age bmi hyp chl
#> 1 20-39 28.7 no 187
#> 2 40-59 22.7 no 187
#> 3 20-39 30.1 no 187
#> 4 60-99 27.5 yes 284
#> 5 20-39 20.4 no 113
#> 6 60-99 20.4 no 184
cmpl_bmi <- complete(ini_bmi)
head(cmpl_bmi)
#> age bmi hyp chl
#> 1 20-39 NA no 187
#> 2 40-59 22.7 no 187
#> 3 20-39 NA no 187
#> 4 60-99 NA yes 206
#> 5 20-39 20.4 no 113
#> 6 60-99 NA yes 184
Hope this helps,
Rui Barradas
Às 18:59 de 14/07/2022, Ian McPhail escreveu:
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
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
