## Help for old friends

This documents describes the changes between mice 2.46.0 and mice 3.0.0. Most code written for versions mice 2.12 - mice 2.46.0 should run unchanged. I have tried to minimize the changes to the function arguments, but it was not possible to remain 100% backward compatible. This document outlines the visible changes, and suggests ways how to adapt old code to mice 3.0.

### mice function arguments

Argument 2.46.0 3.0.0
method length ncol(data) length length(blocks)
predictorMatrix ncol(data) rows and columns length(blocks) rows, ncol(data) columns
blocks not defined new argument
visitSequence integer, arbitrary length character, arbitrary length
form character, length ncol(data) deprecated
formulas not defined named list of formulas
blots not defined named list of alist
diagnostics deprecated
imputationMethod deprecated, use method
defaultImputationMethod deprecated, use defaultMethod

No changes were made for the following arguments: data, m, where, post, defaultMethod, maxit, printFlag, seed and data.init.

If blocks is not specified, then each variable is allocated to a separate block. In that case, length(blocks) is identical to ncol(data), and the method in mice 3.0.0 reduces to variable-by-variable imputation, as in mice 2.46.0 and before.

Argument visitSequence may still be specified as integer or numeric, but it will internally be converted into character using the column names in data.

An existing function call to mice using the old form argument may result in an error Argument "formulas" not a list. The advice is to specify the formula as a list, e.g.,

library(mice, warn.conflicts = FALSE)
imp <- mice(nhanes,
formulas = list(hyp ~ bmi,
chl ~ age + hyp + bmi,
bmi ~ age + hyp + chl),
print = FALSE, m = 1, maxit = 1, seed = 1)
imp$formulas #>$hyp
#> hyp ~ bmi
#>
#> $chl #> chl ~ age + hyp + bmi #> #>$bmi
#> bmi ~ age + hyp + chl

### pool() function uses broom

mice 2.46.0 and before used coef() and vcov() to extract the parameters from the complete-data model. There were two problems with this approach: 1) coef() or vcov() were often not defined for the analysis of interest, 2) their output was not standard across procedures. Older versions of mice therefore needed quite some custom code to extract the parameters and their variance estimates.

mice 3.0.0 uses the broom package for this task. The advantage is that the standardised output. The downside is that (still) many packages offer no defined tidy.xxx() and glance.xxx() functions. In those cases, the user sees the error message Error: No tidy methods for objects of class xxx. See the pool() documentation for what to do in those cases.

### pool() for mixed models requires library(broom.mixed)

mice automatically loads the broom package. Tidiers for mixed models live in the broom.mixed packages and this is NOT automatically loaded. If you want to pool the results of mixed model, issue library(broom.mixed) before calling the pool() function.