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!
########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
*********************************
Simon J. Kiss, PhD
Assistant Professor, Wilfrid Laurier University
73 George Street
Brantford, Ontario, Canada
N3T 2C9
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