Pools Estimates by Rubin's Rules
pool() pools estimates from the ana;yses done withi neach imputed dataset. The typical sequence of steps to do a matching procedure on the imputed datasets are:
Impute the missing values using the mice() function (from the mice package) or the amelia() function (from the Amelia package), resulting in a multiple imputed dataset (an object of the mids or amelia class);
Match or weight each imputed dataset using matchthem() or weightthem(), resulting in an object of the mimids or wimids class;
Check the extent of balance of covariates across the matched datasets (using functions in cobalt);
Fit the statistical model of interest on each matched dataset by the with() function, resulting in an object of the mimira class; and
Pool the estimates from each model into a single set of estimates and standard errors, resulting in an object of the mipo class.
pool(object, dfcom = NULL)
object |
An object of the |
dfcom |
A positive number representing the degrees of freedom in the data analysis. The default is |
pool() function averages the estimates of the model and computes the total variance over the repeated analyses by Rubin’s rules. It calls mice::pool() after computing the model degrees of freedom.
This function returns an object of the mipo class. Methods for mipo objects (e.g., print(), summary, etc.) are available in mice, which does not need to be attached to use them.
Stef van Buuren and Karin Groothuis-Oudshoorn (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3): 1-67. https://www.jstatsoft.org/v45/i03/
#Loading libraries
library(MatchThem)
library(survey)
#Loading the dataset
data(osteoarthritis)
#Multiply imputing the missing values
imputed.datasets <- mice::mice(osteoarthritis, m = 5)
#Weighting the multiply imputed datasets
weighted.datasets <- weightthem(OSP ~ AGE + SEX + BMI + RAC + SMK,
imputed.datasets,
approach = 'within',
method = 'ps')
#Analyzing the weighted datasets
models <- with(weighted.datasets,
svyglm(KOA ~ OSP, family = quasibinomial))
#Pooling results obtained from analyzing the datasets
results <- pool(models)
summary(results)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.