similar to: Beta-Binomial Regression in R

I am using a random intercept model with SITEID as random and NAUSEA as outcome. Thanks. Anamika I tried using a dataset without missing values and changed my model statement accordingly but still get the same error. Follwoing in an excerpt. > anal.data <- read.table("nausea.txt", header=T, sep="\t") > list(names(anal.data)) [[1]] [1] "SITEID"

HI All: Does anyone know the code behind the qbeta function in R? I am using it to calculate exact confidence intervals and I am getting 'NaN' at places I shouldnt be. Heres the simple code I am using: k<-3 > x<-NULL > p<-rbeta(k,3,3)# so that the mean nausea rate is alpha/(alpha+beta) > min<-10 > max<-60 > n<-as.integer(runif(3,min,max)) > for(i in

> I am using a random intercept model with SITEID as random and NAUSEA as > outcome. > > I tried using a dataset without missing values and changed my model > statement accordingly but still get the same error. Follwoing in an excerpt. > > anal.data <- read.table("nausea.txt", header=T, sep="\t") > > list(names(anal.data)) > [[1]] > [1]

Hi: I have asked this question on Cross-Validated. So it might be a cross posting but havent received any responses to it. I am trying to see which distribution will best fit the data I am working on. The dataset is as following: Site Nausea headache Abdominal Distension 1 17 5 10 2 12

Hi:   I am using the following code to read a data set in txt in R and using the follwoing model. But it seems to give me an error ' expected variable name error pos 134022'. Any help is greatly appreciated.   Code: anal.data <- read.table("nausea.txt", header=T, sep="\t") list(names(anal.data)) attach(anal.data) n.samples <- dim(anal.data) [1] # number of data

Hi All: I had a basic question to ask. I am running R2WinBUGS so that I could automate the running of my model using 1000 simulated datasets. Below is the code I am using. The only problem I am having is the bugs output that comes out shows my parameters as nos with 1 decimal place after. I would want to have the parameters with 5 places after decimal. How would I specify that in my code for

Hi: Does Ellipse and dataellipse function in R produce the same ellipse? I wanted to see how the radius for the Ellipse function in R calculated. Also what is the var-covariance matrix, if any, assumed for the dataellipse function? Heres an example of the code where I am generating Multivariate normal data and creating ellipse using the 2 functions: library(car) library(mvtnorm) mu =

Hi: Is there a way we can set up WINBUGS to run 100 simulated datasets on the same model and output results? Or do we have to call in each dataset at a time and repeat the process 100 times manually? Thanks Anamika [[alternative HTML version deleted]]

Hi, I am trying to change a SAS macro to R. here is my code. I get an error at the last line. attach(fram) dset1<-cbind(AGE,BMI,DEATH) > BMIGRP<-cut(BMI,breaks=3,right=TRUE) > AGEGRP<-floor(AGE/10)-2 > dset<-cbind(AGEGRP,BMIGRP,DEATH) > maxage<-max(dset[,1]) > minage<-min(dset[,1]) > #maxcls<-dset[,2] > #mincls<-dset[,2] >

> > Hi All: > > I am using R to calculate exact 95% confidence interval using Clopper > Pearson method. I am using the following code but it seems to get into a > loop and not get out of it, it goes on forever although I am looping it > only 10 times across 63 sites with 10 observations per site. I was hoping > to get some help. > > Thanks > Anamika >

Hi All: I was looking for some help to code the equation of an ellipse in R, given I have a Bivariate Normal distribution (X,Y) with mean (m1,m2) and var-covariance matrix (var1,cov12,cov12,var2). Thanks Anamika [[alternative HTML version deleted]]

Trevor, Thanks for your reply. That doesnot help Any other suggestions? Anamika On Tue, Aug 10, 2010 at 11:58 AM, Trevor Davies <tdavies@mathstat.dal.ca>wrote: > I think you need to load is R2WinBUGS again. > > require(R2WinBUGS) > > Trevor > > > Hi All: > > > > I am getting an error while trying to plot in R2Winbugs > > > > *Error in

Hi, I cant understand where I am going wrong.Below is my code.I would really appreciate your help. Thanks. > genfile<-read.table("c:/tina/phd/bs871/hw/genfile.txt",skip=1) > > #read in SNP data > snp.dat <- as.matrix(genfile) > snp.name <- scan("c:/tina/phd/bs871/hw/genfile.txt",nline=1,what="character") Read 100 items

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

Dear R users - I have a list of patient identifiers and diagnoses from inpatient admissions. I would like to reorganize the list, presently in a long format to a wide format in reshape, but in the absence of a "time" element, I am uncertain how to do this - any help greatly appreciated. ID Dx A nausea A diabetes A kidney failure A heart attack A fever B fever B

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){

Hi folks, I have a question about how to efficiently produce random numbers from Beta and Binomial distributions. For Beta distribution, suppose we have two shape vectors shape1 and shape2. I hope to generate a 10000 x 2 matrix X whose i th rwo is a sample from reta(2,shape1[i]mshape2[i]). Of course this can be done via loops: for(i in 1:10000) { X[i,]=rbeta(2,shape1[i],shape2[i]) } However,

Hi, I am trying to runn a age-period-cohort model, but here is what I am having problem with, hope you can help me! This is what I am trying to do: sumzero_a<-((A-min(A))/5+1) - mean((A-min(A))/5+1) where A is my age variable (numeric, the mid-point of a five-year age group), but I got the following error: Error in min(..., na.rm = na.rm) : invalid 'mode' of argument I am pretty

R-experts: I would like to explore the partial proportional odds models of Peterson and Harrell (Applied Statistics 1990, 39(2): 205-217) for a dataset that I am analyzing. I have not been able to locate a R package that implements these models. Is anyone aware of existing R functions, packages, etc... that might be used to implement the partial proportional odds models? Brant Inman

Hi there, I am using JRClient to build logistic regression model in the following manner : Rconnection c = new Rconnection(); c.eval("KSN<-read.table(\"/Users/amine/Documents/Research/ Tools/R/D2R1.txt\",header=T,sep=\",\")"); c.eval("result <- glm(Nausea ~ Kaletra*Sustiva, family = binomial(link = logit), data =KSN)");