[This article was first published on Shige's Research Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
In my attempt to estimate a mixture regression model for the long-term impact of prenatal famine exposure, I compared results obtained from NPMLREG and Flexmix using the “rainfall” data set came with the package “forward” and was able to achieve identical results from:Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
m.np <- allvc(cbind(Cases, Total-Cases) ~ Rain, random= ~1|ID, data=rainfall, k=2, family=binomial(logit))
and
m.mix <- stepFlexmix(cbind(Cases, Total-Cases) ~ 1|ID, model=FLXMRglmfix(family=”binomial”, fixed=~Rain), k=2, nrep=5, data=rainfall)
To test the random effect model, I created the new variable ID by:
rainfall$ID <- seq(from=1, to=34, by=1)
With Flexmix, it is possible to go a step further and let the treatment variable to vary between latent groups by:
m.mix <- stepFlexmix(cbind(Cases, Total-Cases) ~ Rain|ID, model=FLXMRglmfix(family=”binomial”), k=2, nrep=5, data=rainfall)
To leave a comment for the author, please follow the link and comment on their blog: Shige's Research Blog.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.