mipo: Multiply Imputed Pooled Analysis

Description

The "mipo" object is generated by the lm.mids and glm.mids functions. The "mipo" class of objects has methods for the following generic functions: print, summary.

Usage

print.mipo(x,...)
summary.mipo(object,...)

Arguments

x, object

An object containing the m fit objects of a complete data analysis, plus some additional information.

...

not used.

Value

callThe call that created the mipo object.
call1The call that created the mira object that was used in 'call'.
call2The call that created the mids object that was used in 'call1'.
nmisAn array containing the number of missing observations per column.
mNumber of multiple imputations.
qhatAn m x npar matrix containing the complete data estimates for the npar paremeters of the m complete data analyses.
uAn m x npar x npar array containing the variance-covariance matrices of the m complete data analyses.
qbarThe average of complete data estimates.
ubarThe average of the variance-covariance matrix of the complete data estimes.
bThe between imputation variance-covariance matrix.
tThe total variance-covariance matrix.
rRelative increases in variance due to missing data
dfDegrees of freedom associated with the t-statistics.
fFractions of missing information.

References

Van Buuren, S. & Oudshoorn, C.G.M. (2000). Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Report PG/VGZ/00.038, TNO Prevention and Health, Leiden.