mice: mipo – R documentation

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mice

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 class 'mipo'
summary(
  object,
  type = c("tests", "all"),
  conf.int = FALSE,
  conf.level = 0.95,
  exponentiate = FALSE,
  ...
)

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

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

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

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:

`estimate`	Pooled complete data estimate
`ubar`	Within-imputation variance of `estimate`
`b`	Between-imputation variance of `estimate`
`t`	Total variance, of `estimate`
`dfcom`	Degrees of freedom in complete data
`df`	Degrees of freedom of $t$-statistic
`riv`	Relative increase in variance
`lambda`	Proportion attributable to the missingness
`fmi`	Fraction 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.

Value

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

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. https://www.jstatsoft.org/v45/i03/