[R] Help with "non-integer #successes in a binomial glm"
Prof Brian Ripley
ripley at stats.ox.ac.uk
Tue Aug 9 08:24:02 CEST 2005
On Mon, 8 Aug 2005, Haibo Huang wrote:
> I had a logit regression, but don't really know how to
> handle the "Warning message: non-integer #successes in
> a binomial glm! in: eval(expr, envir, enclos)"
> problem. I had the same logit regression without
> weights and it worked out without the warning, but I
> figured it makes more sense to add the weights. The
> weights sum up to one.
Weights are case weights in a binomial GLM, that is w_i means `I have w_i
of these'. Do check out the theory in MASS (the book) or Nelder &
McCullagh. There are some circumstances when fractional weights make
sense (when this doing something other than fitting a glm, e.g. part of a
`mixture of experts' model) but they are unusual, hence the warning.
>
> Could anyone give me some hint? Thanks a lot!
>
> FYI, I have posted both regressions (with and without
> weights) below.
>
> Ed
>
>
>> setwd("P:/Work in Progress/Haibo/Hans")
>>
>> Lease=read.csv("lease.csv", header=TRUE)
>> Lease$ET <- factor(Lease$EarlyTermination)
>> SICCode=factor(Lease$SIC.Code)
>> Lease$TO=factor(Lease$TenantHasOption)
>> Lease$LO=factor(Lease$LandlordHasOption)
>> Lease$TEO=factor(Lease$TenantExercisedOption)
>>
>> RegA=glm(ET~1+TO,
> + family=binomial(link=logit), data=Lease)
>> summary(RegA)
>
> Call:
> glm(formula = ET ~ 1 + TO, family = binomial(link =
> logit), data = Lease)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
> -0.5839 -0.5839 -0.5839 -0.3585 2.3565
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -1.68271 0.02363 -71.20 <2e-16 ***
> TO1 -1.02959 0.09012 -11.43 <2e-16 ***
> ---
> Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.'
> 0.1 ` ' 1
>
> (Dispersion parameter for binomial family taken to be
> 1)
>
> Null deviance: 12987 on 15809 degrees of freedom
> Residual deviance: 12819 on 15808 degrees of freedom
> AIC: 12823
>
> Number of Fisher Scoring iterations: 5
>
>> setwd("P:/Work in Progress/Haibo/Hans")
>>
>> Lease=read.csv("lease.csv", header=TRUE)
>> Lease$ET <- factor(Lease$EarlyTermination)
>> SICCode=factor(Lease$SIC.Code)
>> Lease$TO=factor(Lease$TenantHasOption)
>> Lease$LO=factor(Lease$LandlordHasOption)
>> Lease$TEO=factor(Lease$TenantExercisedOption)
>>
>> RegA=glm(ET~1+TO,
> + family=binomial(link=logit), data=Lease,
> weights=PortionSF)
> Warning message:
> non-integer #successes in a binomial glm! in:
> eval(expr, envir, enclos)
>> summary(RegA)
>
> Call:
> glm(formula = ET ~ 1 + TO, family = binomial(link =
> logit), data = Lease,
> weights = PortionSF)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
>
> -0.055002 -0.003434 0.000000 0.000000 0.120656
>
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -1.120 2.618 -0.428 0.669
> TO1 -1.570 9.251 -0.170 0.865
>
> (Dispersion parameter for binomial family taken to be
> 1)
>
> Null deviance: 1.0201 on 9302 degrees of freedom
> Residual deviance: 0.9787 on 9301 degrees of freedom
> AIC: 4
>
> Number of Fisher Scoring iterations: 5
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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