similar to: Pull Coefficients from MCMCpack models

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 =

I'm running a long MCMC chain that is generating samples for 22 variables. I have each run of the chain as a row in a matrix. So: Chain[,1] is the column with all the samples for variable one. Chain[,2] is the column with all the samples for variable 2, etc. I'd like to fit all 22 on a single page to print a nice summary. It is OK if the graphs are small, I just need to show the

Hi, I want to create variable names from within my code, but can't find any documentation for this. An example is probably the best way to illustrate. I am reading data in from a file, doing a bunch of stuff, and want to generate variables with my output. (I could make a "list of lists" and name all the elements, but I really want separate variables.) ################# #This is

Hi, Our discussions about 64 bit R has led me to another thought. I have a nice dual core 3.0 chip inside my Linux Box (Running Fedora 11.) Is there a version of R that would take advantage of BOTH cores?? (Watching my system performance meter now is interesting, Running R will hold a single core at 100% perfectly, but the other core sites idle.) Thanks! -- Noah

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, I have a strange one for the group. We have a system that predicts probabilities using a fairly standard svm (e1017). We are looking at probabilities of a binary outcome. The input data is generated by a perl script that calculates a bunch of things, fetches data from a database, etc. We train the system on 30,000 examples and then test the system on an unseen set of 5,000 records.

Hi, I have a data.frame that I need to scale. I've been using the scale function and it works nicely. Some of the libraries I'm testing won't accept negative values for data, so I need to find a way to scale the data from 0 to 1 Any ideas? Thans!

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 =

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

Hi, I need a bit of guidance with the sapply function. I've read the help page, but am still a bit unsure how to use it. I have a large data frame with about 100 columns and 30,000 rows. One of the columns is "group" of which there are about 2,000 distinct "groups". I want to normalize (sum to 1) one of my variables per-group. Normally, I would just write a huge

Hello, I'm using the svm function from the e1071 package. It works well and gives me nice results. I'm very curious to see the actual coefficients calculated for each input variable. (Other packages, like RapidMiner, show you this automatically.) I've tried looking at attributes for the model and do see a "coefficients" item, but printing it returns an NULL result.

I've completed an experiment and want to summarize the results. There are two things I like to create. 1) A simple count of things from the data.frame with predictions 1a) Number of predictions with probability greater than x 1b) Number of predictions with probability greater than x that are really true In SQL, this would be, "Select count(predictions) from

Hi, I am trying to figure out a "short" way to access two values output from the sort function. >x <- c(3,4,3,6,78,3,1,2) >sort(x, index.return=T) $x [1] 1 2 3 3 3 4 6 78 $ix [1] 7 8 1 3 6 2 4 5 It would be great to do something like this (doesn't work.): c(y, indexes) <- sort(x, index.return=T) But that doesn't work. I CAN grab the output of sort in a

Hi, I use the max function often to find the top value from a matrix or column of a data.frame. Now I'm looking to find the top 2 (or three) values from my data. I know that I could sort the list and then access the first two items, but that seems like the "long way". Is there some way to access "max_2" or similar? Thanks! -- Noah

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.

Hi, I'm looking for an easy way to discretize factors in R I've noticed that the lm function does this automatically with a nice result. If I have group <- c("A", "B","B","C","C","C") and run: lm(result ~ x1 + group) The lm function has split the group into separate binary variables {0,1} before performing the

Hello I believe that I'm getting very close in my modeling application. I've come across a challenge that I am unable to solve and would really appreciate the group's opinion. I've been using the val.prob function from the Design library (Thanks Frank!!) to both evaluate and visualize my model. From the scores and graph, it appears as my model is very accurate in

Hi, Is there a way to build up a vector, item by item. In perl, we can "push" an item onto an array. How can we can do this in R? I have a loop that generates values as it goes. I want to end up with a vector of all the loop results. In perl it woud be: for(item in list){ result <- 2*item^2 (Or whatever formula, this is just a pseudo example) Push(@result_list,

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

Hi, An example data set is: group level color A 1 "blue" A 1 "Red" B 1 "blue" B 2 "Red" A 2 "Red" B 2 "Red" B 2 "blue" B 2 "blue" A 2 "blue" A 2 "Red"