search for: priore

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 sets. However,

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 <- sample(seq_len(N),

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 into).

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

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){

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

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

1. (Mainly) Statistical issues are generally off topic on this list. You might want to try the r-sig-mixed-models list instead. 2. However, I think a better answer is to seek local statistical expertise in order to have an extended discussion about your research intent in order to avoid producing yet more irreproducible psychological research. Cheers, Bert Bert Gunter "The trouble with

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

X.Org Security Advisory: October 29, 2024 Issues in X.Org X server prior to 21.1.14 and Xwayland prior to 24.1.4 ======================================================================== An issue has been found in the X server and Xwayland implementations published by X.Org for which we are releasing security fixes for in xorg-server-21.1.14 and xwayland-24.1.4. 1) CVE-2024-9632 can be triggered