mice: filter.mids – R documentation

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filter.mids

Subset rows of a mids object

Description

This function takes a mids object and returns a new mids object that pertains to the subset of the data identified by the expression in .... The expression may use column values from the incomplete data in .data$data.

Usage

## S3 method for class 'mids'
filter(.data, ..., .preserve = FALSE)

Arguments

`.data`	A `mids` object.
`...`	Expressions that return a logical value, and are defined in terms of the variables in `.data$data`. If multiple expressions are specified, they are combined with the `&` operator. Only rows for which all conditions evaluate to `TRUE` are kept.
`.preserve`	Relevant when the `.data` input is grouped. If `.preserve = FALSE` (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

Value

An S3 object of class mids

Note

The function calculates a logical vector include of length nrow(.data$data). The function constructs the elements of the filtered mids object as follows:

`data`	Select rows in `.data$data` for which `include == TRUE`
`imp`	Select rows each imputation `data.frame` in `.data$imp` for which `include == TRUE`
`m`	Equals `.data$m`
`where`	Select rows in `.data$where` for which `include == TRUE`
`blocks`	Equals `.data$blocks`
`call`	Equals `.data$call`
`nmis`	Recalculate `nmis` based on the selected `data` rows
`method`	Equals `.data$method`
`predictorMatrix`	Equals `.data$predictorMatrix`
`visitSequence`	Equals `.data$visitSequence`
`formulas`	Equals `.data$formulas`
`post`	Equals `.data$post`
`blots`	Equals `.data$blots`
`ignore`	Select positions in `.data$ignore` for which `include == TRUE`
`seed`	Equals `.data$seed`
`iteration`	Equals `.data$iteration`
`lastSeedValue`	Equals `.data$lastSeedValue`
`chainMean`	Set to `NULL`
`chainVar`	Set to `NULL`
`loggedEvents`	Equals `.data$loggedEvents`
`version`	Replaced with current version
`date`	Replaced with current date

Author(s)

Patrick Rockenschaub

Examples

imp <- mice(nhanes, m = 2, maxit = 1, print = FALSE)

# example with external logical vector
imp_f <- filter(imp, c(rep(TRUE, 13), rep(FALSE, 12)))

nrow(complete(imp))
nrow(complete(imp_f))

# example with calculated include vector
imp_f2 <- filter(imp, age >= 2 & hyp == 1)
nrow(complete(imp_f2))  # should be 5

mice

Multivariate Imputation by Chained Equations

v3.13.0

GPL-2 | GPL-3

Authors

Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Patrick Rockenschaub [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita Moreno-Betancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent Arel-Bundock [ctb]

Initial release

2021-01-26