similar to: vector or list of matrices corresponding to an observation

Hi All, What is the R function for computing multinomial distribution, e.g. f(2,1,3; 2/9, 1/6, 11/18, 6)? That is, a total of 6 trials, event 1's p1=2/9, x1=2, event 2's p2=1/6, x2=1, and event 3's p3=11/18, x3=3. thanks, Johnny [[alternative HTML version deleted]]

Hi All, I am attempting to build a Multinomial Logit model with dummy variables of the following form: Dependent Variable : 0-8 Discrete Choices Dummy Variable 1: 965 dummy varsghpow at student.monash.edu.augh@gp1.com Dummy Variable 2: 805 dummy vars The data set I am using has the dummy columns pre-created, so it's a table of 72,381 rows and 1770 columns. The first 965 columns represent

anacor-0.9.0 is on CRAN (by De Leeuw and Mair) anacor does correspondence analysis and canonical correspondence analysis. It can make row plots, column plots, joint plots, Benz?cri plots, regression plots, and transformation plots. Where appropriate, plots can be in 3d using either rgl or scatterplot3d. Row and column points can be in standard scaling, Benz?cri scaling, Goodman scaling,

anacor-0.9.0 is on CRAN (by De Leeuw and Mair) anacor does correspondence analysis and canonical correspondence analysis. It can make row plots, column plots, joint plots, Benz?cri plots, regression plots, and transformation plots. Where appropriate, plots can be in 3d using either rgl or scatterplot3d. Row and column points can be in standard scaling, Benz?cri scaling, Goodman scaling,

Hello R users, I am performing a multinomial logit regression and would like to constrain a few model coefficients to be equal. Here is my model: multi <- vglm(case123con ~ SNP_A1+SNP_A2+age, multinomial, work.analy) where case123con is a four level categorical variable (case 1, case 2, case 3, control) and SNP_A1 and SNP_A2 are indicator functions (yes/no). The output of this

Hi Folks, According to the PDF file blogit.pdf in the Zelig documentation: "Use the bivariate logistic regression model ["blogit"] if you have two binary dependent variables (Y1,Y2), and and wish to model them jointly as a function of some explanatory variables. Each pair of dependent variables (Yi1,Yi2) has four potential outcomes, (Yi1=1,Yi2=1), (Yi1=1,Yi2=0),

I present you with a function that solves a problem that has bugged me for many years. I think the problem may be general enough to at least consider adding this function, or a revamped version of it, to the 'stats' package, with the other multinomial functions reside. I'm using R to export data to text files, which are input data for an external model written in C++. Parts of the

I have several demographic variables with which I want to explain the ordered choice of individuals within a survey in an ordered choice (probit or logit, this is not important) framework. Standard ordered choice estimations of course just give me aggregate/average parameter estimates. For my task it would however be useful to estimate or extract "hypothetical" individual-level parameter

Hello Amelie, I don't have an answer to your question, but I just wanted to point out this page I noticed recently ( http://hlplab.wordpress.com/2009/05/07/multinomial-random-effects-models-in-r/), which might be helpful. I'm also interested in figuring out how to do a multinomial glmm, so if you find out anything I'd be happy to hear more about it! Based on what I've found so

I'd like to have a three dimensional array of matrices. I thought I could construct a five dimensional array to have the three dimensional array of matrices. However, not all of the matrices in the array have the same dimensions, which seems to mean I can't use a five dimensional array. What I'd like is this: A = matrix(1:4,2,2) B = matrix(1:25,5,5) C = matrix(1,3,3) D =

Hello, I'm most grateful for your time to read this. I have a uber size 30GB file of 6 million records and 3000 (mostly categorical data) columns in csv format. I want to bootstrap subsamples for multinomial regression, but it's proving difficult even with my 64GB RAM in my machine and twice that swap file , the process becomes super slow and halts. I'm thinking about generating

Dear R-Helpers, I have a short question related to heatmap.2 function. I wanted to change the dimension of each cell in the heatmap drawn by this function. here is a reproducible example from the help page library(gplots) data(mtcars) x <- as.matrix(mtcars) rc <- rainbow(nrow(x), start=0, end=.3) cc <- rainbow(ncol(x), start=0, end=.3) hv <- heatmap.2(x, col=cm.colors(255),

Hello all, I am working with a 1000x1000 matrix, and I would like to return a 1000x1000 matrix that tells me which value in the matrix is greater than a theshold value (1 or 0 indicator). i have tried mat2<-as.matrix(as.numeric(mat1>0.25)) but that returns a 1:100000 matrix. I have also tried for loops, but they are grossly inefficient. THanks for all your help in advance. Lanre

I'm looking for something like Brian Ripley's glmmPQL that will handle multinomial data. Does anyone know of anything? Thanks, Ted. -- Dr E.A. Catchpole Visiting Fellow Univ of New South Wales at ADFA, Canberra, Australia and University of Kent, Canterbury, England - e.catchpole at adfa.edu.au - www.ma.adfa.edu.au/~eac - fax: +61 2 6268 8687 - ph: +61 2 6268 8895

We would like to announce the availability on CRAN of a new package multinomRob. It does robust estimation of overdispersed multinomial regression models. The package is also able to estimate overdispersed grouped multinomial logistic and multivariate-t logistic models. The code is relatively general; for example, it allows for equality constraints across parameters and it can handle datasets in

We would like to announce the availability on CRAN of a new package multinomRob. It does robust estimation of overdispersed multinomial regression models. The package is also able to estimate overdispersed grouped multinomial logistic and multivariate-t logistic models. The code is relatively general; for example, it allows for equality constraints across parameters and it can handle datasets in

We would like to announce the release of our software, which is now available through CRAN. MNP: R Package for Fitting the Multinomial Probit Models Abstract: MNP is a publicly available R package that fits the Bayesian multinomial probit models via Markov chain Monte Carlo. Along with the standard multinomial probit model, it can also fit models with different choice sets for each observation,

We would like to announce the release of our software, which is now available through CRAN. MNP: R Package for Fitting the Multinomial Probit Models Abstract: MNP is a publicly available R package that fits the Bayesian multinomial probit models via Markov chain Monte Carlo. Along with the standard multinomial probit model, it can also fit models with different choice sets for each observation,

Hello, it might be a very simple question but I cannot find the solution (I tried a || b, any(a,b)... but none works). My problem is: I have two vectors, a <- c(TRUE,FALSE,FALSE) b <- c(TRUE,FALSE,TRUE) and I would like to obtain a vector that indicates if it is TRUE in any of the two vectors. Hence, the desired output would be: TRUE, FALSE, TRUE Thank you in advance, Federico

Dear R-Help, I'm hoping to find a Multinomial Nested Logit package in R. It would be great to find something analogous to "PROC MDC" in SAS: > The MDC (Multinomial Discrete Choice) procedure analyzes models > where the > choice set consists of multiple alternatives. This procedure > supports conditional logit, > mixed logit, heteroscedastic extreme value,