search for: diuretic

I have the model below, for which I run a logistic regression including the interaction term (NSAID*Diuretic) ------------------------ fit1=glm(resp ~ nsaid+diuretic+I(nsaid*diuretic), family= binomial,data=w) NSAID Diuretic Present Absent 0 0 185 6527 0 1 53 1444 1 0 42 1293 1 1 25 253 Coefficients Std. Error z value Pr(>|z|) (Intercept) -3.56335 0.07456 -47.794 < 2e-16 *** NSAID 0.1363...

...archical version. Here is the original code of the teacher with both flat prior on betas and a hierarchical version: www.stats.uwo.ca/faculty/murdoch/458/metropolis.r Below is My code with a flat prior on beta only (I'd like also to have the hierarchical version!) X<- cbind(1,DF$nsaid,DF$diuretic,DF$diuretic*DF$nsaid) y<- DF$Var3 Metropolis <- function(logtarget, start, R = 1000, sd = 1) { parmcount <- length(start) sims <- matrix(NA, nrow=R, ncol = parmcount) colnames(sims) <- names(start) sims[1,] <- start oldlogalpha <- logtarget(start) acce...

...(please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){ sum(dbeta(beta,shape1,shape2, log=T)) } posterior <- MCMClogit(nausea~nsaid*diuretic, data=w, verbose=2000,burnin = 1000, mcmc = 10000, user.prior.density=logpriorfun,shape1=1,shape2=1) user.prior.density(beta.start) == -Inf. Error in MCMClogit(nausea ~ nsaid * diuretic, data = w, verbose = 2000, : Respecify and call MCMClogit() again. -- View this message in context:...

Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]