On Mon, 11 Apr 2011, Sacha Viquerat wrote:
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.
You can look at the pR2() function in the "pscl" package which provides
various flavors of pseudo R-squared for "glm", "multinom", and "polr"
objects. The idea is to extract the observed log-likelihood using
logLik(), then update the model to obtain the null model only and call
logLik() again. From the two log-likelihoods and the associated number of
observations, the pseudo R-squared are computed using pR2Work(), see
pscl:::pR2.glm and pscl:::pR2Work.
hth,
Z
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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and provide commented, minimal, self-contained, reproducible code.
