R: Converts an imputed dataset (long format) into a 'mids'...

as.mids {mice} | R Documentation

## Converts an imputed dataset (long format) into a `mids` object

### Description

This function converts imputed data stored in long format into an object of class `mids`. The original incomplete dataset needs to be available so that we know where the missing data are. The function is useful to convert back operations applied to the imputed data back in a `mids` object. It may also be used to store multiply imputed data sets from other software into the format used by `mice`.

### Usage

    as.mids(long, where = NULL, .imp = ".imp", .id = ".id")

### Arguments

`long` | A multiply imputed data set in long format, for example produced by a call to `complete(..., action = 'long', include = TRUE)`, or by other software.
`where` | A data frame or matrix with logicals of the same dimensions as `data` indicating where in the data the imputations should be created. The default, `where = is.na(data)`, specifies that the missing data should be imputed. The `where` argument may be used to overimpute observed data, or to skip imputations for selected missing values.
`.imp` | An optional column number or column name in `long`, indicating the imputation index. The values are assumed to be consecutive integers between 0 and `m`. Values `1` through `m` correspond to the imputation index, value `0` indicates the original data (with missings). By default, the procedure will search for a variable named `".imp"`.
`.id` | An optional column number or column name in `long`, indicating the subject identification. If not specified, then the function searches for a variable named `".id"`. If this variable is found, the values in the column will define the row names in the `data` element of the resulting `mids` object.

### Value

An object of class `mids`

### Note

The function expects the input data `long` to be sorted by imputation number (variable `".imp"` by default), and in the same sequence within each imputation block.

### Author(s)

Gerko Vink

### Examples

    # impute the nhanes dataset
    imp <- mice(nhanes, print = FALSE)
    # extract the data in long format
    X <- complete(imp, action = "long", include = TRUE)
    # create dataset with .imp variable as numeric
    X2 <- X

    # nhanes example without .id
    test1 <- as.mids(X)
    is.mids(test1)
    identical(complete(test1, action = "long", include = TRUE), X)

    # nhanes example without .id where .imp is numeric
    test2 <- as.mids(X2)
    is.mids(test2)
    identical(complete(test2, action = "long", include = TRUE), X)

    # nhanes example, where we explicitly specify .id as column 2
    test3 <- as.mids(X, .id = ".id")
    is.mids(test3)
    identical(complete(test3, action = "long", include = TRUE), X)

    # nhanes example with .id where .imp is numeric
    test4 <- as.mids(X2, .id = 2)
    is.mids(test4)
    identical(complete(test4, action = "long", include = TRUE), X)

    # example without an .id variable
    # variable .id not preserved
    X3 <- X[, -2]
    test5 <- as.mids(X3)
    is.mids(test5)
    identical(complete(test5, action = "long", include = TRUE)[, -2], X[, -2])

    # as() syntax has fewer options
    test7 <- as(X, "mids")
    test8 <- as(X2, "mids")
    test9 <- as(X2[, -2], "mids")
    rev <- ncol(X):1
    test10 <- as(X[, rev], "mids")

    # where argument copies also observed data into $imp element
    where <- matrix(TRUE, nrow = nrow(nhanes), ncol = ncol(nhanes))
    colnames(where) <- colnames(nhanes)
    test11 <- as.mids(X, where = where)
    identical(complete(test11, action = "long", include = TRUE), X)

[Package mice version 3.12.0 Index]