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Use this tag for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`.
4
votes
Accepted
Multiple Imputation and Regression Model Diagnostics
The data are usually not pooled when MI is performed, at least not according to the paradigm described by Rubin (1987). Rather what's being pooled is the parameter estimates obtained from each dataset …
3
votes
Pooling across quantile regression analyses from multiple imputed datasets (quantreg, MICE)
This method is a bit more difficult to apply, because there is currently no R function that automatizes this (that I am aware of). … bs1 <- with(implist, boot.rq(y = mpg, x = model.matrix(mpg ~ wt), R = 5000)$B)
bs1.pooled <- do.call(rbind, bs1)
bs1.ci <- apply(bs1.pooled, 2, quantile, probs=c(.025, .975))
t(bs1.ci)
# 2.5% …
1
vote
Measures of goodness-of-fit using multiply imputed data in Zelig
This answer is concerned with the likelihood-ratio test only. I am unsure if there is a "general" paradigm of combining such measures.
For the LR-test you can look into the works of Meng & Rubin (199 …
2
votes
Do I need to adjust the degrees of freedom returned by pool.compare() in MICE?
The formula is not much more difficult to apply than that of Barnard & Rubin, and it is also implemented in a couple of R packages. …