with.mimids: Evaluates an Expression in Matched Imputed Datasets

In FarhadPishgar/MatchIt.mice: Matching Multiply Imputed Datasets

Description

The with() function performs a computation on each of the n imputed datasets. The typical sequence of steps to do a matching procedure on the imputed datasets are:

1. Impute the missing data points by the mice function (from the mice package), resulting in a multiple imputed dataset (an object of the mids class);

2. Match each imputed dataset using a matching model by the matchitmice() function, resulting in an object of the mimids class;

3. Fit the model of interest (scientific model) on each matched dataset by the with() function, resulting in an object of the mira class;

4. Pool the estimates from each model into a single set of estimates and standard errors, resulting in an object of the mipo class.

Usage

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

Arguments

data	This argument specifies an object of the mimids class, typically produced by a previous call to the function matchitmice().
expr	This argument specifies an expression of the usual syntax of R formula. See help(formula) for details.
...	Additional arguments to be passed to expr.

Details

The with() performs a computation on each of the imputed datasets.

Value

This function returns an object of the mira class (multiply imputed repeated analyses).

Author(s)

Extracted from the mice package written by Stef van Buuren et al. with few changes

References

Stef van Buuren and Karin Groothuis-Oudshoorn (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3): 1-67. https://www.jstatsoft.org/v45/i03/

See Also

matchitmice

Examples

#Loading the dataset
data(dataset)

#Multiply imputing the missing values
imputed.datasets <- mice(dataset, m = 5, maxit = 10,
                         method = c("", "", "", "mean", "polyreg", "logreg", "logreg", "logreg"))

#Matching the multiply imputed datasets
matched.datasets <- matchitmice(OSP ~ AGE + SEX + BMI + RAC + SMK, imputed.datasets,
                                approach = 'within', method = 'nearest')

#Analyzing the matched datasets
models <- with(data = matched.datasets,
               exp = glm(KOA ~ OSP, family = binomial))

FarhadPishgar/MatchIt.mice documentation built on Sept. 18, 2019, 11:52 a.m.