# [R] From THE R BOOK -> Warning: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!

Yihui Xie xie at yihui.name
Tue Mar 30 17:12:37 CEST 2010

```In a Binomial GLM, typically y is a factor with two levels (indicating
success/failure) instead of a numeric vector on [0, 1]. Perhaps the
description in the book is not so clear. You should interpret "data on
proportions" as the observations from a Binomial distribution (rather
than we observed some proportion data which fell in [0,1]). E.g.

y=rbinom(10, size = 1, prob = .3); x=rnorm(y)
# or y = factor(y)
glm(y~x, family = binomial)

Regards,
Yihui
--
Yihui Xie <xieyihui at gmail.com>
Phone: 515-294-6609 Web: http://yihui.name
Department of Statistics, Iowa State University
3211 Snedecor Hall, Ames, IA

On Tue, Mar 30, 2010 at 9:51 AM, Corrado <ct529 at york.ac.uk> wrote:
> Dear friends,
>
> I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on
> proportion data.
>
> I use glm(y~x1+,family=binomial)
>
> y is a proportion in (0,1), and x is a real number.
>
> I get the error:
>
> In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!
>
> But that is exactly what was suggested in the book, where there is no
> mention of a similar warning. Where am I going wrong?
>
> Here is the output:
>
>> glm(response.prepared~x,data=dddd,family=binomial)
>
> Call:  glm(formula = response.prepared ~ x, family = binomial, data = dddd)
>
> Coefficients:
> (Intercept)            x    -0.3603       0.4480
> Degrees of Freedom: 510554 Total (i.e. Null);  510553 Residual
> Null Deviance:      24420
> Residual Deviance: 23240        AIC: 700700
> Warning message:
> In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!
>>
>
>
>
> Regards
> --
>
> PhD Researcher
> Global Climate Change and Biodiversity
> Area 18,Department of Biology
> University of York, York, YO10 5YW, UK
> Phone: + 44 (0) 1904 328645, E-mail: ct529 at york.ac.uk
>
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