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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/

See Also


mice

Multivariate Imputation by Chained Equations

v3.13.0
GPL-2 | GPL-3
Authors
Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Patrick Rockenschaub [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita Moreno-Betancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent Arel-Bundock [ctb]
Initial release
2021-01-26

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