similar to: Metropolis code help

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 ***

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (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){

Dear r-users, Does anyone know how to draw time-dependent survival curves? Example: Event outcome: CHD Time-dependent covariate: NSAID use, which changes over time for each subject I'm interested in survival curves stratified by NSAID use. I'd like to implement Simon & Makuch (1984) method. Is there a R package/function to draw this graph?

Hi, I have a dataset with the binary outcome Y(0,1) and 4 covariates (X1,X@,X#,X$). I am trying to use MCMClogit to model logistic regression using MCMC. I am getting an error where it doesnt identify the covariates ,although its reading in correctly. The dataset is a sample of actual dataset. Below is my code: > ####################### > > > #retreive data > # considering four

I do the these passages: library(Geneland) set.seed(1) data <- simdata(nindiv=200, coord.lim=c(0,1,0,1) , number.nuclei=5 , allele.numbers=rep(10,20), IBD=FALSE, npop=2, give.tess.grid=FALSE) geno <- data$genotypes coord <- t(data$coord.indiv) path.mcmc <-

>>>>> "WSt" == Werner Stahel <stahel@stat.math.ethz.ch> writes: [to me privately] WSt> I do not want to call this a bug, but it bugged me. Please try the WSt> following: WSt> tform <- sqrt(RADAI) ~ sqrt(RADAI.e) + TAGE.ej + SPITAL + ARZT + DAS28 + WSt> SJC + TJC + DGA + HAQ + PGA + PAIN + YEAR.SYM + YEAR.DIA + WSt> ALTER +

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]]

Hi All, I'm trying to run a simulation of host-pathogen evolution based around individuals. What I need to have is a dataframe or table of some description - describing all the individuals of a pathogen population (so far I've implemented this as a matrix): ID No_of_Effectors Effectors (Sequences) [1,] 0001 3 ## 3

I was playing around with the ghyp package and simulated series of t-distributed variables when suddenly i was not able to reproduce the log likelihood values reported by the package. When trying to reproduce the likelihood values, I summed the log(dt(x,v)) values and it worked with some simulated series but not all. Is there any obvious flaws with this script? library("ghyp")

I've wrote some code to simulate a random walk in 2 dimensions on a lattice. Basically I want to add something in to make it plot it point by point so you can see what is going on. Heres my code for the random walk in 2d RW2D<-function(N) { i<-0 xdir<-0 ydir<-0 xpos<-vector() xpos[1]<-xdir ypos<-vector() ypos[1]<-ydir for (i in 1:N-1)