pool.lori: The pool.lori method aggregates lori multiple imputation...

In lori: Imputation of High-Dimensional Count Data using Side Information

The pool.lori method aggregates lori multiple imputation results. Multiple imputation allows to produce estimates of missing values, as well as intervals of variability. The classical procedure is to perform multiple imputation using the mi.lori method, and to aggregate them using the pool.lori method.

Description

The pool.lori method aggregates lori multiple imputation results. Multiple imputation allows to produce estimates of missing values, as well as intervals of variability. The classical procedure is to perform multiple imputation using the mi.lori method, and to aggregate them using the pool.lori method.

Usage

pool.lori(res.mi)

Arguments

res.mi

a multiple imputation result from the function mi.lori

Value

pool.impute	a list containing the pooled means (mean) and variance (var) of the imputed values
pool.alpha	a list containing the pooled means (mean) and variance (var) of the row effects
pool.beta	a list containing the pooled means (mean) and variance (var) of the column effects
pool.epsilon	a list containing the pooled means (mean) and variance (var) of the covariate effects
pool.theta	a list containing the pooled means (mean) and variance (var) of the interactions

Examples

X <- matrix(rnorm(50), 25)
Y <- matrix(rpois(25, 1:25), 5)
res <- mi.lori(Y, X, 10, 10, 2)
poolres <- pool.lori(res)

lori documentation built on April 4, 2025, 5:24 a.m.