similar to: bayesian mixed logit

Dear helpers, I am using the function "constrOptim" to estimate a model with ML with an inequality constraint using the option method='Nelder-Mead'. When I specify the option: hessian = TRUE I obtain the response: Error in f(theta, ...) : unused argument(s) (hessian = TRUE) I guess the function "constrOptim" does not allow this argument which, on the other hand, is

Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times). Any suggestion would be greatly appreciated. Carlo

Hi, I am trying to create a scatterplot, coding each point to one of 5 populations. I was successful when I did this for one set of data, yet when I try plotting other data a blank plot appears (although the axes are labelled and I can fit the regression lines from each population). I have tried a variety of things to fix this but nothing seems to work. I can plot the points if I do not

Dear helpers, I am using R version 2.5.1 to estimate a multinomial logit model using my own maximum likelihood function (I work with share data and the default function of R cannot deal with that). However, the computer (I have an Athlon XP 3200+ with 512 GB ram) takes quite a while to estimate the model. With 3 categories, 5 explanatory variables and roughly 5000 observations it takes 2-3 min.

Hi List. I have some weirdness here which I have no idea how to resolve. I have been runing samba for the las two years on a suse 8.2 box with no problem at all. Last week I had a file system failure and was forced to rebuild. Rebuilt using SuSE 9.1 and SAMBA 2.28 When I started restoring data, I noticed that all files with .eml extensions could not be written to disk.!?!? As an experriment, i

Dear helpers, I am using constrOptim to minimize a function subject to inequality constraint. It works well when the number of parameters to optimize is low (e.g. 4) but when they are more (e.g. 10) it does not produce the expected results. The function is minus the determinant of a binomial logit model with one explanatory variable. Calling ?xeta? the vector of explanatory variables the

Hello: I need to take a species-sample matrix and transpose it to the format used by PC-ORD for analysis. Unfortunately, the number of species is very large (>5000), and so this operation cannot be performed simply in an application like Excel, which has a 255 column limit. So, I wrote relatively simple code in R that I hoped would do this (appended below). But there are glitches. The

I am attempted to examine the temporal independence of my data set and think I need an unordered multinomial logistic regression (or logit model) with repeated measures to do so. The data in question is location of chickens. Chickens could be in any one of 5 locations when a snapshot sample was taken. The locations of chickens (bird) in 8 pens (pen) were scored twice a day (AMPM) for 20 days

Hi all, hope someone can help me out with this. Background Introduction I have a data set consisting of data collected from a questionnaire that I wish to validate. I have chosen to use confirmatory factor analysis to analyse this data set. Instrument The instrument consists of 11 subscales. There is a total of 68 items in the 11 subscales. Each item is scored on an integer scale between 1 to 4.

Dear all, I would like to add mixed effects in a multinomial model and I am trying to use MCMCglmm for that. The main problem I face: my data set consits of a trapping data set, where the observation at eah trap (1 or 0 for each species) have been aggregated per traplines. Therefore we have a proportion of presence/absence for each species per trapline. ex: ID_line mesh habitat Apsy Mygl

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file 'MCMCglmm_2.05/DESCRIPTION'

Hi all, I would like to start to use R's MCMC abilities to compute answers in Bayesian statistics. I don't have any specific problems in mind yet, but I would like to be able to compute/sample posterior probabilities for low-dimensional custom models, as well as handle "standard" Bayesian cases like linear regression and hierarchical models. R clearly has a lot of abilities in

Hi, I am sorry if this is perceived as a C++ question rather than an R question. After uploading an R library to CRAN (MCMCglmm) the C++ code failed to pass the memory checks. The errors come in pairs like: Mismatched free() / delete / delete [] at 0x4A077E6: free (vg_replace_malloc.c:446) by 0x144FA28E: MCMCglmm (MCMCglmm.cc:2184) Address 0x129850c0 is 0 bytes inside a block of size 4

Hi, Thanks in advance for any help on this question. I'm running multinomial models using the MCMCglmm package. The models have 5 outcome variables (each with count data), and an additional two random effects built into the models. The issue is that when I use the following code, the summary only gives me results for four of the outcome variables. Here is the code for my model: m3.random

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

Dear helpers, Does anyone know about a package or a function that allows to estimate Multivariate Markov-Switching Models, like MS-VAR as introduced by Krolzig(1997) with R ? Thanks a lot!! Carlo

Dear all, I am using the "mgcv" package by Simon Wood to estimate an additive mixed model in which I assume normal distribution for the residuals. I would like to test this model vs a standard parametric mixed model, such as the ones which are possible to estimate with "lme". Since the smoothing splines can be written as random effects, is it correct to use an (approximate)

> On Mar 22, 2018, at 1:31 PM, Michelle Kline <michelle.ann.kline at gmail.com> wrote: > > Hi, > > Thanks in advance for any help on this question. I'm running multinomial > models using the MCMCglmm package. The models have 5 outcome variables > (each with count data), and an additional two random effects built into the > models. The issue is that when I use