Warning message: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!

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Liz

On Mon, Dec 10, 2012 at 8:40 AM, Simon Kiss <sjkiss at gmail.com> wrote:

R-help at r-project.org

	[[alternative HTML version deleted]]

Hi there
I'm trying to fit a logistic regression model to data that looks  
very similar to the data in the sample below.   I don't understand  
why I'm getting this error; none of the data are proportional and  
the weights are numeric values.  Should I be concerned about the  
warning about non-integer successes in my binomial glm? If I should  
be, how do I go about addressing it?
I'm pretty sure the weights in the data frame are sampling weights.

What follows is the result of str() on my data, the series of  
commands I'm using to fit the model, the responses I'm getting and  
then some code to reproduce the data and go through the same steps  
with that code.  One last (minor) question.  When calling svyglm on  
the sample data, I actually get some information about the model  
fitting results as well as the error about non-integer successes.   
In my real data, you only get the warning. Calling summary(mod1) on  
the real data does return information about the coefficients and the  
model fitting.

I'm grateful for any help. I'm aware that the topic of non-integer  
successes has been addressed before, but I could not find my answer  
to this question.

Yours, Simon Kiss

######str() on original data
str(mat1)
'data.frame':	1001 obs. of  5 variables:
$ prov  : Factor w/ 4 levels "Ontario","PQ",..: 2 2 2 2 2 2 2 2 2  
2 ...
$ edu   : Factor w/ 2 levels "secondary","post-secondary": 2 2 2 1 1  
2 2 2 1 1 ...
$ gender: Factor w/ 2 levels "Male","Female": 1 1 2 2 2 2 1 1 2 2 ...
$ weight: num  1.145 1.436 0.954 0.765 0.776 ...
$ trust : Factor w/ 2 levels "no trust","trust": 2 1 1 1 1 2 1 2 1  
2 ...

#######Set up survey design
des.1<-svydesign(~0, weights=~weight, data=mat1)

#######model and response to svyglm
mod1<-svyglm(trust ~ gender+edu+prov, design=des.1,  
family='binomial').

Warning message:
In eval(expr, envir, enclos) : non-integer #successes in a binomial  
glm!

2: glm.fit: algorithm did not converge

########Model Summary
summary(mod1)

Call:
svyglm(formula = trust ~ gender + edu + prov, design = des.1,
   family = "binomial")

Survey design:
svydesign(~0, weights = ~weight, data = mat1)

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)
(Intercept)       -0.625909   0.156560  -3.998 6.87e-05 ***
genderFemale       0.013519   0.140574   0.096    0.923
edupost-secondary -0.011569   0.141528  -0.082    0.935
provPQ            -0.006614   0.172105  -0.038    0.969
provatl            0.335166   0.297860   1.125    0.261
provwest          -0.053862   0.174826  -0.308    0.758
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

(Dispersion parameter for binomial family taken to be 1.002254)

Number of Fisher Scoring iterations: 4




#########Attempt To Reproduce The Problem
########Data
mat.test<-data.frame(edu=c(rep('secondary', 300), rep('post- 
secondary', 300)), prov=c(rep('ON', 200), rep('PQ', 200), rep('AB',  
200)), trust=c(rep('trust',200), rep('notrust',400)),  
gender=c(rep('Male', 300), rep('Female', 300)), weight=rnorm(600,  
mean=1, sd=0.3))
#######Survey Design object
test<-svydesign(~0, weights=~weight, data=mat.test)

#####Call To svyglm
svyglm(trust ~ edu+prov+gender, design=test, family='binomial')

#Reults
Independent Sampling design (with replacement)
svydesign(~0, weights = ~weight, data = mat.test)

Call:  svyglm(formula = trust ~ edu + prov + gender, design = test,
   family = "binomial")

Coefficients:
(Intercept)  edusecondary        provON        provPQ    genderMale
 -2.658e+01    -8.454e-04     5.317e+01    -1.408e-02            NA

Degrees of Freedom: 599 Total (i.e. Null);  596 Residual
Null Deviance:	    759.6
Residual Deviance: 3.406e-09 	AIC: 8
Warning messages:
1: In eval(expr, envir, enclos) :
 non-integer #successes in a binomial glm!
2: glm.fit: algorithm did not converge