hello dear list! since we want to do a model analysis and some people
would like to see pseudo-R^2 values for different types of glm of a
logistic regression, i've decided to write a function that computes
either nagelkerkes normed pseudo-R or cox & snells pseudo-R. however, i
am not clear as in the decisive step, i need to calculate the log of
(maximum likelihood estimates of model divided by mle of null model). i
am well aware of the functions stats::mle and stats::logLik as well as
of Design::lrm. however, I'm not sure wheter mle helps me at all and I
am uncertain about the logLik call I have implemented:
#cox&snell
lambda<- -2*log((logLik(null.model)[1]/logLik(model)[1]))
out<-1-exp(-lambda/n)
#nagelkerke
lambda<- -2*log( logLik(model)[1]/logLik(null.model)[1] )
lambda2<- -2*log( logLik(model)[1] )
out<-(1-exp(-lambda/n))/(1-exp(-lambda2/n))
can anyone help me out?
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