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--- tips:multiple_imputation_with_mice_and_metafor [2020/07/10 11:15] – Wolfgang Viechtbauer
+++ tips:multiple_imputation_with_mice_and_metafor [2020/12/02 17:10] – Wolfgang Viechtbauer
@@ Line 133: / Line 133: @@
 One way of dealing with missing data is to make use of imputation techniques. The advantage of using [[wp>Imputation_(statistics)#Multiple_imputation|multiple imputation]] is that we not only impute once (and then pretend that the imputed values are free of any uncertainty), but multiple times from appropriate distributions, so that several imputed datasets are generated. The same analysis is then applied to each dataset and the results are pooled, taking into consideration not only the uncertainty in each fitted model, but also across models.
-The mice package allows us to automate this process and can be used in combination with the metafor package. First, we install and load the mice package:
+The [[https://cran.r-project.org/package=mice|mice]] package allows us to automate this process and can be used in combination with the metafor package. First, we install and load the mice package:
 <code rsplus>
 install.packages("mice")
 library(mice)
+</code>
+Also, due to a recent change in the mice package (that currently breaks compatibility with the metafor package), we need to create a little helper function to make things work again.
+<code rsplus>
+withold <- function (data, expr) {
+  call <- match.call()
+  analyses <- as.list(seq_len(data$m))
+  for (i in seq_along(analyses)) {
+    data.i <- complete(data, i)
+    analyses[[i]] <- eval(expr = substitute(expr), envir = data.i, enclos = parent.frame())
+    if (is.expression(analyses[[i]]))
+      analyses[[i]] <- eval(expr = analyses[[i]], envir = data.i, enclos = parent.frame())
+  }
+  object <- list(call = call, call1 = data$call, nmis = data$nmis, analyses = analyses)
+  oldClass(object) <- c("mira", "matrix")
+  object
+}
 </code>