search for: beta_z

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2002 May 15
1
calculating likelihood-based CI
...e a more efficient way to do it? obj<-glm(y~x+z....) #fit the full model beta.z<-seq(a, b, length=500) #where a<coef(obj)["z"]<b out<-list() for(i in 1:500){ out[[i]]<-update(obj,.~.-z+offset(I(beta.z[i]*z)) ) } dev.z<-sapply(out, function(x)x$dev) #profile dev for \beta_z range(beta.z[(dev.z-obj$dev)<3.8]) #likelihood based CI I understand that there could be some package to do it (for instance in MASS???), but I like know the code to use it with some nonstandard situations. best, vito -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-...