It may be so.
Also, when I try to run genmod with the factors Bact Host and their interaction, the warning about the Hessian matrix not being positive definite may be caused by that many zeroes.
The model runs ok with no interaction.
I took a look at this page: https://support.sas.com/rnd/app/stat/examples/GENMODZIP/roots.htm
but I am not sure if this will apply to my data, because I have categorical independent variables. And binomial.
Mr. Ksharp,
I thought that unbalanced data meant that there were too many observations in one class, as compared to another class in an analysis.
Are you suggesting that too many zeros in one class can be a special case of unbalanced data?
Regards,
Yes. unbalanced data is way too biased from 1:1. Like good:bad =99: 1.
You could oversample it into good:bad=30:70 and base that data to build a logistic model.
About your question, I am not able to follow , what does that ' special case ' mean ?
Which of those cases do you think applies here?
Have you tried changing your reference level? Are you interested in estimates on the categorical variables - I think you are so they're not just control variables here.
@igforek wrote:
What do you think about Dr. Allison insight into dummy variables?
https://statisticalhorizons.com/multicollinearity
@igforek wrote:
Unfortunately, I am right mow working on a computer that does not run SAS
or any other statistical software. I will try changing my reference this
afternoon, after 5 pm central time.
I am thinking case 3 may apply to my analysis. And yes, I am interested in
estimates on the categorical variables
Then his comments don't apply because it only does if it's a control variable or you're interested in the overall effects. So you need to find some other way to deal with the collinearity or re-frame your problem/experiment somehow. If you knew this was likely to happen, host and bacteria would match primarily, a different design should have been choosen .... unfortunately it's likely too late to change that.
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