similar to: negative multinomial regression models

I have seen several queries about parameterizing the negative binomial scale parameter. This is called the heterogeneous negative binomial. I have written a function called "nbinomial" which is in the msme package on CRAN. Type ?nbinomial to see the help file. The default model is a negative binomial for which the dispersion parameter is directly related to mu, which is how Stata,

Hello, I''ve spent a lot of time during the past month trying to get negative multinomial regression models for clustered event counts as described in (Guang Guo. 1996. "Negative Multinomial Regression Models For Clustered Event Counts." Sociological Methodology 26: 113-132., abstract at http://depts.washington.edu/socmeth2/4abst96.htm) implemented in R. A FORTRAN version of the

R-listers: I have searched the help files and everything I have related to R graphics. I cannot find how to graph y against several distributions on a single graph. Here is code for creating 4 Poisson distributions with different mean values, although I would prefer having it in a loop: The top of the y axis for the first distribution, with count of 0, is .6, which is the highest point for

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

Hi, Consider the need for a regression model which can handle an ordered multinomial response variable. There are, for example, proportional odds / cumulative logit models, but actually the regression should include random effects (a mixed model), and I would not be aware of multinomial regression model as part of lme4 (am I wrong here ?). Further, the constraint of proportional odd models

Hi, I would like to apply the L-BFGS optimization algorithm to compute the MLE of a multilevel multinomial Logistic Regression. The likelihood formula for this model has as one of the summands the formula for computing the likelihood of an ordinary (single-level) multinomial logit regression. So I would basically need the R implementation for this formula. The L-BFGS algorithm also requires

Hi, I am trying to carry out a multinomial regression using the cumlogit link function. I have tried using the VGAM package, and have gotten some results... fit1 <- vgam(Y ~ X1 + X2 + X3 + X4, cumulative(link=logit,intercept.apply=FALSE,parallel=TRUE), data = data1 ) The problem arrises when I try to get the information out of the fitted object. I can

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,

Dear all, I am trying to run a mixed multinomial logit model in R since my response variable has 4 non-ordinal categories. I am using the package mlogit that estimates the parameters by maximum likelihood methods. First of all, I prepared my data using the mlogit.data command. In the mlogit command, one can introduce alternative-specific (fixed factors??) and individual-specific (random

Hi, I am inexperienced with ML and R so please be tolerant :) I am trying to replicate the method I have seen in a paper without success. If my understanding is correct (a big 'IF') it seems to use Multinomial Logit on multiple groups of various sizes, with 'nature' selecting the choice (the winner) - then uses Maximum Likelihood to optimise the parameters to produce a model for

I want to analyse data with an unordered, multi-level outcome variable, y. I am asking for the appropriate method (or R procedure) to use for this analysis. > N <- 500 > set.seed(1234) > data0 <- data.frame(y = as.factor(sample(LETTERS[1:3], N, repl = T, + prob = c(10, 12, 14))), x1 = sample(1:7, N, repl = T, prob = c(8, + 8, 9, 15, 9, 9, 8)), x2 = sample(1:7, N, repl =

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

Hello, after calculating a multinomial logit regression on my data, I compared the output to an output retrieved with SPSS 18 (Mac). The coefficients appear to be the same, but the logLik (and therefore fit) values differ widely. Why? The regression in R: set.seed(1234) df <- data.frame( "y"=factor(sample(LETTERS[1:3], 143, repl=T, prob=c(4, 1, 10))), "a"=sample(1:5,

Is there any library for fitting multinomial probit using either likelihood or the "method of simulated moments" or both. I presume it would be possible to write a family function in VGAM for the multinomial probit, but was hoping that someone has done it already. Thanking you as always. Vumani Dlamini CSO-Swaziland

Hello I am having some problems for extracting the residuals from models working with ordinal multinomial data. Either working with the polr() function or the plsRglm () function, the residuals are "NULL". I guess this is because the data is multinomial but I do not know how to solve it. I have read the following in internet: "can you tell us how residuals would be defined in

Hello, I have a log likelihood function that I was able to optimize using mle2. I have two years of the data used to fit the function and I would like to fit both years simultaneously to test if the model parameter estimates differ between years, using likelihood ratio tests and AIC. Can anyone give advice on how to do this? My likelihood functions are long so I'll use the tadpole

I am estimating a multinomial model with two quantitative predictors, X1 and X2, and 3 responses. The responses are called neutral, positive and negative with neutral being the baseline. There are actually many models being estimated because I estimate the model over time and also for various parameter sets but that's not important. When I estimate a model, since neutral is the baseline

Dear all, I have found some difference of the results between multinom() function in R and multinomial logistic regression in SPSS software. The input data, model and parameters are below: choles <- c(94, 158, 133, 164, 162, 182, 140, 157, 146, 182); sbp <- c(105, 121, 128, 149, 132, 103, 97, 128, 114, 129); case <- c(1, 3, 3, 2, 1, 2, 3, 1, 2, 2); result <- multinom(case ~ choles

Dear list, I would like to know: 1. After I have used the R code (http://pscl.stanford.edu/zeroinfl.r) to fit a zero-inflated negative binomial model, what criteria I should follow to compare and select the best model (models with different predictors)? 2. How can I compare the model I get from question 1 (zero-inflated negative binomial) to other models like glm family models or a logistic

Dear all, I am trying to model variation of distribution of species assemblages according to environmental variables. For that I use a log linear multinomial regression. In order to select variables that mostly discriminate the assemblages, I tried to apply a hierarchical partitioning protocol to my data set. For that I have adapted the all.regs() for multinomial model. The problem is that I