Hi all, Very surprising (to me!) and mystifying result from predict.glm(): the predictions vary depending on whether or not I use ns() or splines::ns(). Reprex follows:
library(splines) set.seed(12345) dat <- data.frame(claim = rbinom(1000, 1, 0.5)) mns <- c(3.4, 3.6) sds <- c(0.24, 0.35) dat$wind <- exp(rnorm(nrow(dat), mean = mns[dat$claim + 1], sd = sds[dat$claim + 1])) dat <- dat[order(dat$wind), ] m1 <- glm(claim ~ ns(wind, df = 5), data = dat, family = binomial) m2 <- glm(claim ~ splines::ns(wind, df = 5), data = dat, family = binomial) # The model coefficients are the same unname(coef(m1)) #> [1] 0.5194712 -0.8687737 -0.6803954 4.0838947 2.3908674 4.1564128 unname(coef(m2)) #> [1] 0.5194712 -0.8687737 -0.6803954 4.0838947 2.3908674 4.1564128 # But the predictions are not! newdf <- data.frame(wind = seq(min(dat$wind), max(dat$wind), length = 5)) unname(predict(m1, newdata = newdf)) #> [1] 0.51947119 0.03208719 2.82548847 3.90883496 4.06743266 unname(predict(m2, newdata = newdf)) #> [1] 0.5194712 -0.5666554 -0.1731268 2.8134844 3.9295814 Is this a bug? (Motivating issue from this ggplot2 bug report: https://github.com/tidyverse/ggplot2/issues/2426) Thanks! Hadley -- http://hadley.nz ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
