Pools the mean of one column over the m completed datasets in a
gmm_imputation, returning the estimate with a standard error, degrees of
freedom, confidence interval, and fraction of missing information. The
default method = "analytic" computes the between-imputation variance in
closed form – the exact \(m \to \infty\) limit, with no Monte-Carlo
noise – from the mixture conditional, and splits the total variance into
complete-data, imputation, and parameter parts. method = "rubin" instead
applies Rubin's rules to the drawn completions (useful as a check).
Usage
proxy_pool(object, column, method = c("analytic", "rubin"))Arguments
Details
For a regression or any other model estimand, do not pool here: convert the
imputations to a mice object with as_mids() and pool with
mice::pool(), which is the established workflow and reports the same
diagnostics.
See also
proxy_fmi(), as_mids() to pool models with mice::pool().
Other imputation:
as_mids(),
gmm_complete(),
gmm_imputation(),
gmm_impute(),
mechanism,
proxy_fmi(),
proxy_mnar_sensitivity()
Examples
set.seed(1)
x1 <- rnorm(150); x2 <- x1 + rnorm(150)
x2[runif(150) < 0.3] <- NA
imp <- gmm_impute(cbind(x1, x2), N = 1L, m = 10L, seed = 1L)
proxy_pool(imp, "x2") # analytic column mean
#> term estimate std.error statistic df conf.low conf.high fmi
#> 1 x2 0.03396067 0.1171525 0.2898843 3583.756 -0.1957316 0.2636529 0.1765858