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.
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.

References