On Dec 10, 2012, at 5:40 AM, Simon Kiss wrote:
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!
I also got:
2: glm.fit: algorithm did not converge
And the gender coefficient was NA.
Your problem is not reproducible because no seed was set. Does this
suggest any issues?
with(mat.test, table(trust, gender) )
gender
trust Female Male
notrust 300 100
trust 0 200
--
David.
########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
--
David Winsemius, MD
Alameda, CA, USA
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