I have a dataset with more 15 independent variables trying explain a binary outcome. The results seemed dubious and the confidence interval profiling failed by providing lower bounds of the confint larger than the upper bounds-- I found the variable creating this mess which is a four category variable. Furthermore, I understand that it is stupid to use the var==2 as a reference category since there are no "Yes" outcomes for that value, hence giving trouble for all the other values. I could relevel and just have problems with that level giving a huge SE, or maybe I should collapse level two and three. However, I would prefer not to - given the actual interpretation of the variable. Any ideas how I can sneak around this?
with(w,table(outcome,var)
2 3 4 5 <NA> Sum
No 35 226 281 463 0 1005
Yes 0 18 36 268 0 322
<NA> 0 0 0 0 0 0
Sum 35 244 317 731 0 1327
> glm(outcome~var,family=binomial,data=w)->l
> summary(l)
Call:
glm(formula = outcome ~ var, family = binomial, data = w)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.95571 -0.95571 -0.49101 -0.00036 2.28333
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -16.57 405.60 -0.041 0.967
var3 14.04 405.60 0.035 0.972
var4 14.51 405.60 0.036 0.971
var5 16.02 405.60 0.039 0.968
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1470.6 on 1326 degrees of freedom
Residual deviance: 1313.6 on 1323 degrees of freedom
AIC: 1321.6
Number of Fisher Scoring iterations: 15
> confint(l)
Waiting for profiling to be done...
2.5 % 97.5 %
(Intercept) -140.643404 2.508492
var3 306.741916 188.855827 ###
var4 -4.058638 141.355062
var5 -3.034361 140.211069
There were 40 warnings (use warnings() to see them)
