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
Match or weight each imputed dataset using
weightthem(), resulting in an object of the
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
An object of the
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.,
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/
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#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)
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