similar to: multivariate ordered probit regression---use standard bivariate normal distribution?

Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

Hi, I'm new to R and I've run into a problem that I'm not really sure how to express properly in the language. I've got a data table that I've read from a file containing some simple information about the performance of 4 algorithms. The columns are the name of the algorithm, the problem instance and the resulting score on that problem (if it wasn't solved I mark that

Hi everyone. I have these data: probClass<-seq(0,0.9,0.1) prob1<-c(0.0070,0.0911,0.1973,0.2949,0.3936,0.5030,0.5985,0.6869,0.7820,0.8822) prob2<-c(0.0066,0.0791,0.2358,0.3478,0.3714,0.3860,0.6667,0.6400,0.7000,1.0000) # which I'm plotting as follows: plot(probClass,prob1,xlim=c(0,1),ylim=c(0,1),xaxs='i',yaxs='i',type="n") lines(probClass,prob1)

I care a lot about R's speed. So I decided to give REvolution's R (http://revolution-computing.com/) a try, which bills itself as an optimized R. Note that I used the free version. My machine is a Intel core 2 duo under Windows XP professional. The code I run is in the end of this post. First, the regular R 1.9. It takes 2 minutes and 6 seconds, CPU usage 50% Next, REvolution's R.

First, I love R and am grateful to be using this free and extremely high quality software. Recently I have been comparing two algorithms and naturally I programmed in R first. It is so slow that I can almost feel its pain. So I decided to do a comparison with Java. To draw 500,0000 truncated normal, Java program takes 2 second and R takes 72 seconds. Not a computer science major, I find it hard

Dear all, For my package "ref" I have implemented extensive regression testing. It now fails to compile since primitives "dim" and "dimnames" (and their assignment methods) no longer allow for additional arguments. I was using an additional argument "ref" with several methods. For "].refdata" it still works, with "dim.refdata" no longer.

I have following the message "dim(refdata) and dimnames(refdata) no longer allow parameter ref=TRUE, use dim(derefdata(refdata)), dimnames(derefdata(refdata)) instead" when I loaded data.table. Is it from the package ref? Could it be fixed? Or there is something wrong with my installation? > library(data.table) Loading required package: ref dim(refdata) and dimnames(refdata) no

I managed to solve the problem myself without using this code. thx 2011-11-24 12:26 keltezéssel, Kehl Dániel írta: > Dear Community, > > I am trying to write code for the following problem. > Lets assume we have a beta distribution. > I know one quantile, lets say, 10% of the mass lies above .8, that is > between .8 and 1. > In addition, I know that the average of this

Dear all, I would like to create a code for semiparametric Klein and Spady's estimator. For that I created a function that provides the log-likelihood function for each observation (so it is a function of betas and i, where i denotes the observation). Now, in order to maximize the log-likelihood function, I have to sum these log-likelihood functions for each i and so to get another function

Anyone know how to get p-values for the t-values from the coefficients produced in vglm? Attached is the code and output ? see comment added to output to show where I need p-values + print(paste("********** Using VGAM function gamma2 **********")) + modl2<- vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c") + print(coef(modl2,matrix=TRUE))

How does one ideally handle and display multidimenstional contingency tables in R v. 2.6.2? E.g.: > prob1<- data.frame(victim=c(rep('white',4),rep('black',4)), + perp=c(rep('white',2),rep('black',2),rep('white',2),rep('black',2)), + death=rep(c('yes','no'),4), count=c(19,132,11,52,0,9,6,97)) > prob1 victim perp

Dear, When i multiply the y of a regression by 10, I would expect that the coefficient would be multiply by 10 and the z value to stay constant. Here some reproducible code to support the case. *Ex 1* library(mvtnorm) library(VGAM) set.seed(1) x=rmvnorm(1000,sigma=matrix(c(1,0.75,0.75,1),2,2))

Hello, I want to generate data set from Cox PH model with gamma frailty effects. theta(parameter for frailty distribution)=2 beta=1.5 n=300 cluster size=30 number of clusters=10 I think I should first generate u from Gamma(Theta,theta) and then using this theta I could not decide how I should generate the survival times? Is there any package for this? or any document you could suggest? Any

Hi Folks, I wonderif someone who is familiar with the details of vglm in the VGAM package can assist me. I'm new to using it, and there doesn;t seem much in the documentation that's relevant to the question below. Say I have a vector x of 0/1 responses and another vector y of 0/1 responses, these in fact being a bivariate set of 0/1 responses equivalent to cbind(x,y). E.g.

Dear R-list, Hi everyone, Im trying to make an analysis of multidimensional contingency tables using R. I' working with the Agresti example where you have the data from 3 categories. The thing is how can I do the analisys using the G2 statistics. Somebody can send me an Idea? I attach the program where you can find the data. Best Regards, > prob1<-

Hi dear R users, 1) I would like to know if there is a simple way to define a vglm family which would be a mix of poisson variables and bernoulli variables (0/1 response) for idea this would be invoked like this: vglm(...,family=mixpoissonmultinom(npoisson,n01response)) where the n's give the number of each type of response. 2) and a simpler question : How to use constraints in rrvglm?

Hi All, I'm fitting an proportional odds model using vglm() from VGAM. My response variable is the severity of diseases, going from 0 to 5 (the severity is actually an ordered factor). The independent variables are: 1 genetic marker, time of medical observation, age, sex. What I *need* is a p-value for the genetic marker. Because I have ~1.5 million markers I'd rather not faffing

R-Experts: I am using the vglm function of the VGAM library to perform proportional odds ordinal logistic regression. The issue that I would like help with concerns the format in which the response variable must be provided for this function to work correctly. Consider the following example: ------ library(VGAM) library(MASS) attach(pneumo) pneumo # Inspect the format of the original dataset

Hello I have a rather simple code and for some reason it produces an error message. If someone can tell me why and how to fix it, I would be very greatful. Thank you in advance. ##### create data set.seed(10) n <- 200 # number of training points n.test <- 200 # number of test points p<-2 # dimension of input space z <-

The symptom triggering this email is that an S4 summary method sometimes refuses to be invoked, even when a package is explicitly loaded, if the first load of the package is implicit. It may or may not be specific to 'summary' methods and/or the 'VGAM' package. I've sent to R-devel because (i) it looks like some kind of bug to me, but I'm not sure; (ii) it's not