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Help with "non-integer #successes in a binomial glm"

2 messages · Haibo Huang, Brian Ripley

Original
Haibo Huang
# 
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
+ family=binomial(link=logit), data=Lease)
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
+ family=binomial(link=logit), data=Lease,
weights=PortionSF)
Warning message: 
non-integer #successes in a binomial glm! in:
eval(expr, envir, enclos)
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 Ripley
#
On Mon, 8 Aug 2005, Haibo Huang wrote:

            

      
      
      
    

        
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.