summary.mimids: Summarizes a 'mimids' Class Object
In MatchIt.mice: Matching Multiply Imputed Datasets

Description Usage Arguments Details Author(s) References See Also Examples

The summary.mimids() function summarizes an object of the mimids class.

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## S3 method for class 'mimids'
summary(object, n = 1, interactions = FALSE,
  addlvariables = NULL, standardize = FALSE, covariates = FALSE, ...)

`object`	This argument specifies an object of the `mimids` class.
`n`	This argument specifies the matched imputed dataset number, intended to summarize its matching profile. The input must be a positive integer. The default is `1`.
`interactions`	This argument specifies whether to show the balance of all squares and interactions of the covariates used in the matching procedure (for nearest neighbor matching method). The input must be a logical value. The default is `FALSE`
`addlvariables`	This argument specifies whether to provide balance measures on additional variables not included in the original matching procedure (for nearest neighbor matching method). The input should be a list. The default is `NULL`
`standardize`	This argument specifies whether to print out standardized versions of the balance measures, where the mean difference is standardized (divided) by the standard deviation in the original treated group (for nearest neighbor matching method). The input must be a logical value. The default is `FALSE`.
`covariates`	This argument specifies whether to include the covariates while reporting the matched sample sizes (for exact mathcing method). The input must be a logical value. The default is `FALSE`.
`...`	Additional arguments to be passed to the `summary.mimids()` function.

The matching profile of the mimids class objects is summarized.

Farhad Pishgar

Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3): 199-236. http://gking.harvard.edu/files/abs/matchp-abs.shtml

mimids

#Loading the 'dt.osa' dataset
data(dt.osa)

#Imputing missing data points in the'dt.osa' dataset
datasets <- mice(dt.osa, m = 5, maxit = 1,
                 method = c("", "", "mean", "", "polyreg", "logreg", "logreg"))

#Matching the imputed datasets, 'datasets'
matcheddatasets <- matchitmice(KOA ~ SEX + AGE + SMK, datasets,
                               approach = 'within', method = 'exact')

#Summarizing data of the first imputed dataset
summary.1 <- summary(matcheddatasets, n = 1)