similar to: Bayesian logistic regression with a beta prior (MCMClogit)

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 ***

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

Dears, I have the below code for metropolis of the GLM logit (logistic regression) using a flat prior. Can someone help me modify the prior so that the model becomes hierarchical by using a flat prior for mu and sigma, the derived density for beta ~ N(mu, sigma^2)? Actually I took my code from a teacher that posted on the internet and modified it to the GLM logit but I can't adapt it to the

Hello everybody, I'm new to MCMClogit. I'm trying to use MCMClogit to fit a logistic regression model but I got some warnings I can't understand. My input data X is 32(tissue sample)*20(genes) matrix, each element in this matrix corresponds to the expression value of one particular gene in one of 32 samples. And the Y presents the corresponding classes (0-non cancer, 1-cancer)

Hi everyone: I am just wondering how can I use weights with MCMClogit function (in MCMCpack package). For example, in case of glm function as given below, there is weights option in the arguments. Aparently there is no option of using weights in MCMClogit. glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control =

I hope some of the authors of the package MCMCpack read this. I don't know if there is a way to set the response in the model formula of MCMClogit other than a (numeric) response vector. I think the MCMClogit in the MCMCpack needs some development so that the response in the formula could be set as a two-column matrix with the columns giving the numbers of successes and failures (just as in

Deal all, as MCMClogit does not allow for the specification of several chains, I have run my model 3 times with different random number seeds and differently dispersed multivariate normal priors. For example: res1 = MCMClogit(y~x,b0=0,B0=0.001,data=mydat, burnin=500, mcmc=5500, seed=1234, thin=5) res2 = MCMClogit(y~x,b0=1,B0=0.01,data=mydat, burnin=500, mcmc=5500, seed=5678, thin=5) res3 =

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've been testing some models with the MCMCpack library. I can run the process and get a nice model "object". I can easily see the summary and even plot it. I can't seem to figure out how to: 1) Access the final coefficients in the model 2) Turn the coefficients into a model so I can then run predictions using them. A summary command will SHOW Me the coefficients, but

Is there a way to recover Metropolis-step acceptance rates AFTER completing posterior draws? The immediate application is in the probit.bayes and logit.bayes models used by Zelig... which I believe is merely calling MCMCpack. So one strategy, to which I am fixing to resort, is to call, say, MCMClogit with verbose set to mcmc (or mcmc divided by an integer) and then look at my screen.

Hello! I have the following problem. My code: --snip-- ergebnisse <- rep(0, each=2) stichproben <- rep(0, each=2) for (i in seq(1:2)) { n <- dim(daten)[1] ix <- sample(n,200) # producing samples samp_i <- daten[ix,] stichproben[i] <- samp_i # doesn???t works # Calculation of the model: posterior_i <- MCMClogit(y ~ fbl.ind + fekq3 + febitda4 + fuvs + fkru +

Hello! So, the simulation works (drawing 100 samples and then calculate the model for each sample). Here is the code: --snip-- # sample size n=200 ergebnisse200 <- rep(0, each=100) stichproben200 <- vector(?list?, 100) default200 <- rep(0, each=100) for (i in seq(1:100)) { n <- dim(daten)[1] ix <- sample(n,200) samp_i <- daten[ix,] # draw samples y <- sum(samp_i$y)

I'm having some problems in installing some source packages under linux. As an example, MCMCpack. An error is raised when linking: > install.packages("MCMCpack") [...] * Installing *source* package 'MCMCpack' ... checking for C++ compiler default output file name... a.out checking whether the C++ compiler works... yes checking whether we are cross compiling... no checking

Sorry to bother your. I am trying to post my question for more than 10 times, but I still didn't see it. It drives my crazy!!! It is a test for posting some simple pure text. Chao

Hi, I want to know is there other functions in R to estimate qualitative response model besides multinom() in library nnet, if this is the only possibility, I have a question about the application: for example: there is three transportation choice : car, bus , subway. each alternative has own characteristic variables, I want to apply conditional logit model to analysis the choice of three

Hello to R people Does anybody know to calculate exact logistic regression in R? Does such option exist anywhere? Surprisingly, could not find it using search engine. It is hard to believe, however, that such useful function is not implemented in R yet? Could you help, please Thank you Denis

>>>>> "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 +

> 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]

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

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