R: Evaluate an expression in multiple imputed datasets

with.mids {mice} | R Documentation

## Evaluate an expression in multiple imputed datasets

### Description

Performs a computation of each of imputed datasets in data.

### Usage

    ## S3 method for class 'mids'
    with(data, expr, ...)

### Arguments

`data` | An object of type `mids`, which stands for 'multiply imputed data set', typically created by a call to function `mice()`.
`expr` | An expression to evaluate for each imputed data set. Formula's containing a dot (notation for "all other variables") do not work.
`...` | Not used

### Value

An object of S3 class `mira`

### Note

Version 3.11.10 changed to tidy evaluation on a quosure. This change should not affect any code that worked on previous versions. It turned out that the latter statement was not true (#292). Version 3.12.2 reverts to the old `with()` function.

### Author(s)

Karin Oudshoorn, Stef van Buuren 2009, 2012, 2020

### References

van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate Imputation by Chained Equations in `R`. Journal of Statistical Software, 45(3), 1-67. doi: 10.18637/jss.v045.i03

### See Also

`mids`, `mira`, `pool`, `D1`, `D3`, `pool.r.squared`

### Examples

    imp <- mice(nhanes2, m = 2, print = FALSE, seed = 14221)

    # descriptive statistics
    getfit(with(imp, table(hyp, age)))

    # model fitting and testing
    fit1 <- with(imp, lm(bmi ~ age + hyp + chl))
    fit2 <- with(imp, glm(hyp ~ age + chl, family = binomial))
    fit3 <- with(imp, anova(lm(bmi ~ age + chl)))

[Package mice version 3.14.0 Index]