similar to: Mixed logit models with a random coefficient

Dear all, I need to maximize the v: v= D' W D D is a column vector ( n , 1) W is a given matrix (n, n) subject to: sum D= 1 (BTW, n is less than 300) I´ve tried to use maxBFGS, as follows: ##################################### objectiveFunction<-function(x) { return(t(D)%*%W%*%D) } Amat<-diag(nrow(D)) Amat<-rbind((rep(-1, nrow(D))), Amat) bvec<-matrix( c(0), nrow(D)+1,

Hi R community, I posted a question on using the R maximum likelihood functions a short while ago and got an email saying that some of the content was "unprocessed". Hence, I am reposting the question just to be sure (sorry for the multiple emails if both reached you). My question is reagarding the way we use constraints in programs like maxBFGS, etc. We need to put two matrices into

Hi all, Sorry to bother you. I'm estimating a discrete choice model in R using the maxBFGS command. Since I wrote the log-likelihood myself, in order to double check, I run the same model in Limdep. It turns out that the coefficient estimates are quite close; however, the standard errors are very different. I also computed the hessian and outer product of the gradients in R using the

Dear R Users, I'm currently working on simultaneous multinomial models and wonder whether R has any package that can estimate such models. I've got a survey dataset that contains 2,000 individuals and one of the survey questions asked the respondents to chose two main reasons to work out of eight options (e.g., pay, pepople, to use abilities, etc.) . Now I'd like to model the

Dear R users, I would like to perform Latent Class Logit Models for the analysis of choice experiments in environmental valuation. This kind of analysis is usually performed with NLogit Software (http://www.limdep.com). I attach the results I usually obtain using NLogit and NLogit model specifications. For Random parameter models and Logit Models I usually perform my analysis with the package

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 R users, I am new to R. I would like to find *maximum likelihood estimators for psi and alpha* based on the following *log likelihood function*, c is consumption data comprising 148 entries: fn<-function(c,psi,alpha) { s1<-sum(for(i in 1:n){(c[i]-(psi^(-1/alpha)*(lag(c[i],-1))))^2* (lag(c[i],-1)^((-2)*(alpha+1)) )}); s2<- sum(for(m in 1:n){log(lag(c[m],-1)^(((2)*alpha)+2))});

Hi all, I am trying to implement a Monte-Carlo simulation for each cell in a spatial matrix (using mcd2 package) . I have figured out how to conduct the simulation using data from a single location (where I manually input distribution parameters into the R code), but am having trouble (a) adjusting the code to pull input variables from my various data sets and then (b) applying the entire

Hello, I'm using optim to program a set of mle regression procedures for non-normal disturbances. This is for teaching and expository purposes only. I've successfully programmed the normal, generalized gamma, gamma, weibull, exponential, and lognormal regression functions. And optim returns reasonable answers for all of these compared with the identical optimization problems in STATA and

>>>>> Steven Yen >>>>> on Fri, 9 Oct 2020 05:39:48 +0800 writes: > Oh Hi Arne, You may recall we visited with this before. I > do not believe the problem is algorithm specific. The > algorithms I use the most often are BFGS and BHHH (or > maxBFGS and maxBHHH). For simple econometric models such > as probit, Tobit, and evening

Hi, I am running glm logit regressions with R and I would like to test a linear combination of coefficients (H0: beta1=beta2 against H1: beta1<>beta2). Is there a package for such a test or how can I perform it otherwise (perhaps with logLik() ???)? Additionally I was wondering if there was no routine to calculate pseudo R2s for logit regressions. Currently I am calculating the pseudo R2

Oh Hi Arne, You may recall we visited with this before. I do not believe the problem is algorithm specific. The algorithms I use the most often are BFGS and BHHH (or maxBFGS and maxBHHH). For simple econometric models such as probit, Tobit, and evening sample selection models, old and new versions of R work equally well (I write my own programs and do not use ones from AER or sampleSekection).

Dear R users, I am happy to announce the initial release of the "mvProbit" package on CRAN (version 0.1-0). This package provides tools for econometric analysis with Multivariate Probit Models. While these models can be estimated also by several other statistical software packages (e.g. LIMDEP/NLOGIT, STATA), "mvProbit" is much more flexible and powerful in calculating

Dear R users, I am happy to announce the initial release of the "mvProbit" package on CRAN (version 0.1-0). This package provides tools for econometric analysis with Multivariate Probit Models. While these models can be estimated also by several other statistical software packages (e.g. LIMDEP/NLOGIT, STATA), "mvProbit" is much more flexible and powerful in calculating

A colleague insalled Wine for me on a 32-bit Red Hat Linux server yesterday. I am not trying to install an application called LIMDEP under Wine. The documentation says to start a script called winecfg. When I invoke that script, all I get is a gray box on my screen with a few tabs. No text appears on the screen. If I click on some of the tabs, there are drop-down menus, which also have no text.

Hi, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform

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 searched everywhere but I didn't find what I want, that is why I as the question here. Threads discussing this issue on this mailing list are already quite old. Does anybody know of a function in R which allows to estimate ordered probit/logit model with random coefficients. The only mixed effect model I found was clmm of the ordinal package but it only provides random intercepts. I

Let's say I have a variable, day, which is saved as a factor with 7 levels, and I use it in a logistic regression model. I ran the model using the car package in R and printed out the results. mod1 = glm(factor(status1) ~ factor(day), data=mydat, family=binomial(link="logit")) print(summary(mod1)) The result I get is: Coefficients: Estimate Std. Error z value

Hello, Could anybody provide me with codes (procedure) how to obtain Conditional Logit (McFadden) and Mixed Logit (say, assuming normal distribution) estimates in R? Thanks, David U. -- View this message in context: http://www.nabble.com/Conditional-Logit-and-Mixed-Logit-tf4489238.html#a12802959 Sent from the R help mailing list archive at Nabble.com.