From h.wickham at gmail.com  Fri Apr 27 16:36:12 2018
From: h.wickham at gmail.com (Hadley Wickham)
Date: Fri, 27 Apr 2018 07:36:12 -0700
Subject: [Rd] predict.glm returns different results for the same model
In-Reply-To: <cf8ba3df-da8b-31b8-1324-ff48908507b1@gmail.com>
References: <CABdHhvGNmn3ssrwnkqNOJAJYqYNJkjO6Du1B+7NvDZzC0Dgh6w@mail.gmail.com>
 <cf8ba3df-da8b-31b8-1324-ff48908507b1@gmail.com>
Message-ID: <CABdHhvFLZcgHrsx3qHsKUEg-EJNgYexkD4MaoDZ-XvVHQJtcqw@mail.gmail.com>

On Fri, Apr 27, 2018 at 7:28 AM, Duncan Murdoch
<murdoch.duncan at gmail.com> wrote:
> 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.

Great, thanks!


-- 
http://hadley.nz



[Rd] predict.glm returns different results for the same model
Hadley Wickham