search for: priors

Hi guys, I got a problem when I was trying to use lattice to do some plot. Below is one working example which can generate one curve for A and one curve for B in each subplot. However, I would like to just show the points for B, not connecting the dots. As for A, I still want a curve (dots are connected). Is there any people having any suggestions. Data are attached. Thanks a lot, --Jerry

Hi all, If I run LDA on the same data (2-class classification) with default(no priors specified in the lda function) vs. "prior=c(0.5, 0.5)", the results are different. The (0.5, 0.5) priors give better 1-classify-to-1 rate, and the proportional priors(default, no priors specified in the lda function) give better 0-classify-to-0 rate, for both training and testing data se...

Bugs item #11183, was opened at 2007-05-28 10:28 You can respond by visiting: http://rubyforge.org/tracker/?func=detail&atid=218&aid=11183&group_id=35 Category: Incorrect behavior Group: current Status: Open Resolution: None Priority: 3 Submitted By: Patrick McDonnell (kc9ddi) Assigned to: Kevin Smith (qualitycode) Summary: GLib errors cause crash Initial Comment: Hi - I''m

Dear R-helpers, To get curves for a pseudo cohort other than the one centered at the mean of the covariates, I have been trying to use the newdata argument to survfit with no success. Here is my model statement, the newdata and the ensuing error. What am I doing wrong? > summary(fit) Call: coxph(formula = Surv(Start, Stop, Event, type = "counting") ~ Week + LagAOO + Prior.f +

Dear R help, What am I doing wrong here? when I don't specify the priors it works just fine but when I specify the priors it breaks.? Does anyone know why and how I can fix it? ---- > N=20000 > ncontrol=ncases=50 > X <- as.matrix(rnorm(N,0,1)) > eta <- -5.3 + X * 1.7 > p <- exp(eta)/(1+exp(eta)) > Y <- rbinom(N,1,p) > controls <- samp...

Hi all, I'm hoping I might be able to get some help with some issues specifying priors for MCMCglmm. I'm trying to fit a gaussian glmm using MCMCglmm to a data set with two (correlated) response variables. The response variables are both logit-transformed proportions (there are a few reasons why I've chosen these with gaussian error over binomal glmm, which I won't go...

I am fairly new at using R/programming in general so I apologize if I am leaving crucial parts of the puzzle out, but here goes. First and foremost this is the error I am receiving: Error in muPriors[priors[, 1:2]] <- priors[, 3] : NAs are not allowed in subscripted assignments This occurs only when I am using observational priors and some number of nominal variables, it does not occur when I change to specifying the variables as ordinal, or if I do not use priors. Using a doctored ver...

Hi Im testing wxRuby. I have a problem when ruby try to load the load wx. Im newbie to ruby, maybe a make a simple mistake : Best regards. Install wxRuby-------------------------------------------------------------- pedro@la-vaca-azul:~$ sudo gem install wxruby2-preview Need to update 2 gems from http://gems.rubyforge.org .. complete Select which gem to install for your platform (i486-linux)

Bugs item #6633, was opened at 2006-11-12 16:46 You can respond by visiting: http://rubyforge.org/tracker/?func=detail&atid=218&aid=6633&group_id=35 Category: Incorrect behavior Group: None Status: Open Resolution: None Priority: 2 Submitted By: Alex Fenton (brokentoy) Assigned to: Kevin Smith (qualitycode) Summary: Serious (but harmless?) GTK warnings on Linux Initial Comment:

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){ 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)...

Hi, everyone, I am a new user of R. Recently, I tried the following commands, but couldn't make them work. If any one of you has some ideas about it, please help me. The commands are >std<-1000 >mean<-8000 >prior<-function(n){1/(sqrt(2*pi)*std)*exp(-1.0*(n-mean)^2/(2*std^2))} >plot(prior,1,15000) >post<-function(n){ + if(n < 9543) + 0 + else + prior(n)/n +

Hi! Could you please explain the difference between "prior" and "weight" in rpart? It seems to be the same. But in this case why including a weight option in the latest versions? For an unbalanced sampling what is the best to use : weight, prior or the both together? Thanks a lot. Aur?lie Davranche.

Hi all, I previously emailed about a multinomial model, and after seeking some additional help, realized that since my response/outcome variables are not mutually exclusive, I need to use a multi-response model that is *not* multinomial. I'm now trying to figure out how to specify the priors on the multi-response model. Any help would be much appreciated. My data look like this: X other focal village present r teaching Opp_teacher Dir_teacher Enh_teacher SocTol_teacher Eval_teacher Total_teacher f_Age f_Ed Age Ed1 61 10202 10213 0 15 0.250000000 2...

I have a problem in where i generate m independent draws from a binomial distribution, say draw1 = rbinom( m , size.a, prob.a ) then I need to use each draw to generate a beta distribution. So, like using a beta prior, binomial likelihood, and obtain beta posterior, m many times. I have not found out a way to vectorize draws from a beta distribution, so I have an explicit for loop

I'm wondering whether anyone has any insight into why the 'simulate' methods for the built-in glm() families (binomial, Poisson, Gamma ...) extract the prior weights using object$prior.weights rather than weights(object,"prior") ? At first I thought this was so that things work correctly when e.g. subset= and na.action=na.exclude are used. However, the current versions of

...t; I previously emailed about a multinomial model, and after seeking some > additional help, realized that since my response/outcome variables are not > mutually exclusive, I need to use a multi-response model that is *not* > multinomial. I'm now trying to figure out how to specify the priors on the > multi-response model. Any help would be much appreciated. > > My data look like this: > > X other focal village present r teaching Opp_teacher > Dir_teacher Enh_teacher SocTol_teacher Eval_teacher Total_teacher > f_Age f_Ed Age Ed1 61 10202 10213...

Hello I wonder whether someone can tell me what I am doing wrong. Here is the code (from Bayesian Computation with R - Chapter 2.3.R) that I am trying to run #################################### # Section 2.3 Using a Discrete Prior #################################### graphics.off() # Close all graphics rm(list=ls()) # Clear all variables library(LearnBayes)

Dear listers, I am making a trial to move from S3 to S4... I have created some classes of interest and they work acceptably well for the purpose. I am now wondering how to make them operate in a package. In clear when a package is loaded (eg library(mypackage)) where should I put the class descriptions:

Hi, Using the example in ?VSEM in the package BayesianTools I'm attempting to iteratively update the prior but find the plotTimeSeriesResults produces the following errors when I extend the VSEM example in BayesianTools. With the Code below (the errors) I get: " Error in quantile.default(x, probs = quantiles[i]) : missing values and NaN's not allowed if 'na.rm' is

Hi, I am trying to run th mclust package on a variable "Tuberculin indurations" recorded as mm. The file has only one variable. When I run the package I get NULL value for mu and sigma. Can anybody say why? This is the program: library("mclust") mc<-Mclust(x.trab,G=1:9,warn=TRUE) mc mc$mu sqrt(mc$sigmasq) and the output I get is > library("mclust")