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mice (version 3.16.0)

mipo: mipo: Multiple imputation pooled object

Description

The mipo object contains the results of the pooling step. The function pool generates an object of class mipo.

Usage

mipo(mira.obj, ...)

# S3 method for mipo summary( object, type = c("tests", "all"), conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ... )

# S3 method for mipo print(x, ...)

# S3 method for mipo.summary print(x, ...)

process_mipo(z, x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE)

Value

The summary method returns a data frame with summary statistics of the pooled analysis.

Arguments

mira.obj

An object of class mira

...

Arguments passed down

object

An object of class mipo

conf.int

Logical indicating whether to include a confidence interval. The default is FALSE.

conf.level

Confidence level of the interval, used only if conf.int = TRUE. Number between 0 and 1.

exponentiate

Flag indicating whether to exponentiate the coefficient estimates and confidence intervals (typical for logistic regression).

x

An object of class mipo

z

Data frame with a tidied version of a coefficient matrix

Details

An object class mipo is a list with elements: call, m, pooled and glanced.

The pooled elements is a data frame with columns:

estimatePooled complete data estimate
ubarWithin-imputation variance of estimate
bBetween-imputation variance of estimate
tTotal variance, of estimate
dfcomDegrees of freedom in complete data
dfDegrees of freedom of $t$-statistic
rivRelative increase in variance
lambdaProportion attributable to the missingness
fmiFraction of missing information

The names of the terms are stored as row.names(pooled).

The glanced elements is a data.frame with m rows. The precise composition depends on the class of the complete-data analysis. At least field nobs is expected to be present.

The process_mipo is a helper function to process a tidied mipo object, and is normally not called directly. It adds a confidence interval, and optionally exponentiates, the result.

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. tools:::Rd_expr_doi("10.18637/jss.v045.i03")

See Also

pool, mids, mira