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amices.bsky.social
MICE
@amices.bsky.social
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Home of the #RStats imputation package {mice}. Amices is a place for people interested in solving missing data problems. See https://amices.org & https://github.com/amices. Posts by @oberman.bsky.social
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Reposted by MICE
thomvolker.bsky.social
Cool stuff!

Florian van Leeuwen and I implemented a prediction function in the #mice package that allows the incorporation of missing data uncertainty in a prediction interval.

The `predict_mi()` function is available in the current development version: github.com/amices/mice

#Rstats #statsky
Image of R code. To reproduce: library(ggplot2) library(dplyr) library(mice, warn.conflicts = FALSE) imp <- mice(nhanes, m = 5, maxit = 5, seed = 1, ignore = rep(c(FALSE, TRUE), c(20, 5)), print = FALSE) impdats <- complete(imp, "all") train <- lapply(impdats, function(dat) subset(dat, !imp$ignore)) test <- lapply(impdats, function(dat) subset(dat, imp$ignore)) fits <- lapply(train, function(dat) lm(age ~ bmi + hyp + chl, data = dat)) preds <- predict_mi(object = fits, newdata = test, pool = TRUE, interval = "prediction") preds preds %>% as.data.frame() %>% mutate(case = 1:nrow(preds), y = test[[1]]$age) %>% ggplot(aes(x = fit, y = case, col = rowSums(is.na(nhanes[imp$ignore,]))>0)) + geom_point() + geom_errorbar(aes(xmin = lwr, xmax = upr)) + theme_minimal() + scale_color_manual(values = mice::mdc(1:2), labels = c("observed", "missing")) + theme(legend.title = element_blank(), legend.position = "bottom") + labs(x = "prediction", title = "Pooled prediction intervals")
September 29, 2025 at 1:32 PM
Reposted by MICE
oberman.bsky.social
Is mean imputation effective?
Yes—at filling in the missings; but also
Yes—at biasing any subsequent analyses.
stefvanbuuren.name/fimd
February 3, 2025 at 6:39 PM
amices.bsky.social
📊 Struggling with missing data in your analyses? Join our 4-day course 'Advanced Techniques for Handling Missing Data' at @utrechtuniversity.bsky.social!

📅 24–27 Mar 2025
📍 Utrecht, NL
💶 €730 | 1.5 ECTS

Learn cutting-edge imputation with {mice} in R. Apply by March 10th!

#RStats #DataScience
Advanced Techniques for Handling Missing Data in analysis and prediction workflows | Utrecht Summer School
This 4-day course provides cutting-edge techniques for addressing missing data problems, focusing on the intersection of statistical theory and modern machine learning workflows.
utrechtsummerschool.nl
January 6, 2025 at 2:50 PM