R help - Aug 2005 - Help with "non-integer #successes in a binomial glm"

Hi,

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. 

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

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