predict.glm returns different results for the same model

On 27/04/2018 9:25 AM, Hadley Wickham wrote:

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?

The two objects m1 and m2 differ more than they should, so this looks like a
problem in glm, not just in predict.glm.

attr(m1$terms, "predvars")

list(claim, ns(wind, knots = c(25.4756277492997, 30.2270250736796,
35.4093171222502, 43.038645381669), Boundary.knots = c(12.9423820390783,
108.071583734075), intercept = FALSE))

attr(m2$terms, "predvars")

list(claim, splines::ns(wind, df = 5))

This appears to be due to a bug in the splines package.  There, the function
splines:::makepredictcall.ns looks like this:

makepredictcall.ns <- function(var, call)
{
    if(as.character(call)[1L] != "ns") return(call)
    at <- attributes(var)[c("knots", "Boundary.knots", "intercept")]
    xxx <- call[1L:2L]
    xxx[names(at)] <- at
    xxx
}

The test fails for m2, because as.character(call)[1L] is "splines::ns"
instead of "ns". I'll see if I can work out a better test and submit a
patch.