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Hello, I want to start testing using the MNP probit function in stead of the lrm function in my current experiment. I have one dependant label and two independent varaibles. The lrm is simple model <- lrm(label ~ val1 + val2) I tried the same thing with the mnp function and got an error that I don't understand model <- mnp(label ~ val1 + val2) I get back an immediate error that

Hello All, I'm trying to run a multinomial probit on a dataset with 28 data points and five levels (0,1,2,3,4) in the latent choice involving response variable. I downloaded the latest mnp package to run the regression. It starts the calculation and then crashes the rpogram. I wish I could give the error message but it literally shuts down R without a warning. I'm using the R

Hi All, Can the MNP package available in R be used to analyze panel data as well? *i.e., *if there are 3 observed discrete choices for three time periods for the same individual , can i estimate a panel multinomial probit model which allows correlated errors across time periods and individual heterogeneity (random coefficients) using the MNP package? In the case that it doesn't work, is

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,

Hi all, Does anybody have a hint on what may be going wrong in this R code? I mimic the sample code from the MNP developers but I seem unable to get the choice specific variables right. Thanks, Joan Serra > rm(list=ls()) > library(foreign) > small<-read.spss("small.sav") Warning message: small.sav: Unrecognized record type 7, subtype 13 encountered in system file. >

I use mnp to run a multinominal probit regression model, but the summary doesn't contain the model statistics, such as the log-likelihood and degree of freedom, for the assessment of the goodness-of-fit of the fitted model. Is there any way that I can generate these statistics for the fitted model in R? Many thanks in advance! SC

Hi, I recently found the MNP package. Out of curiosity, I tried to reproduce results from Greene (Econometric Analysis, fourth edition) on page 874. The signs of the estimates are all opposite those of Greene's table. Might anyone be able to tell me what I am doing wrong? I have attached the function call, the coefficients, and a few rows of the data. The dataset has 210 observations.

Dear All, I have some questions about probit regressions. I saw a nice introduction at http://bit.ly/bU9xL5 and I mainly have two questions. (1) The first is almost about data manipulation. Consider the following snippet ################################################## mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/binary.csv")) names(mydata) <-

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,

I have a data frame in wide format. There are six variables that represent two factors in long format 3x2, Valence and Temperature: > head(dpts) File Subj Time Group PainNeg.hot PainNeg.warm SociNeg.hot SociNeg.warm Positiv.hot Positiv.warm Errors 1 WB101_1_1_dp.txt 101 1 MNP 30.700000 13.75000 16.319048 35.166667 30.18333 14.383333 1 2

Good morning reader, I have encountered a, probably, simple issue with respect to the *formulae* of a *regression model* I want to use in my research. I’m researching alliances as part of my study Business Economics (focus Strategy) at the Vrije Universiteit in Amsterdam. In the research model I use a moderating variable, I’m looking for confirmation or help on the formulation of the model.

Hi, everyone, I haven't found anything similar in the forum, so here's my problem (I'm no expert in R nor statistics): I have a data set of 59.000 cases with 9 variables each (fractional coverage of 9 different plant types, such as deciduous broad-leaved temperate trees or evergreen tropical trees etc.), which was generated by a vegetation model. In order to evaluate the quality of

Hi all, For my research I have to use a Multinomial Probit model. I saw that there are two packages, that include a method to estimate my parameters. The first one is the MNP-package of Imai and van Dyk. The second one is part of the bayesm-package of Rossi. The results for both packages are not the same using the same data. Does anybody know what the difference is between these two approaches?

Hi all, I am running an -MNP- multinomial probit model package using R. It gives me the following objection instead of giving me the results: Erreur : impossible d'allouer un vecteur de taille 137.9 Mo (in english: cannot allocate a 137.9 Mb vector memory).  I have already increased the memory size upto 2047Mb. This problem has been discussed in 2008 (archives) but no profitable answers were

Dear everyone, the following problem: Our group has written a lengthy program in c++, to which we would like to add some additional features. Because we are not sure if those features are actually useful, we would prefer to take a "quick and dirty" approach just to try them out. The additional feature is that in every iteration of the algorithm membership-probabilities should by

Dear everybody! Are there algorithms for multinominal logit/probit available for R? Is it my fault that I cannot find these in CRAN? Has somebody programmed these? with best wishes Ott Toomet Ott.Toomet at mail.ee -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

hi list! i'm plotting a probit plot .On x axis i have value of a statistical variable. on y axis the corresponding normalized representation. I have this code plot(vals,perc,axes=F,col="red",pch=19,cex=0.25) probit.scale.values <- c(0,0.001,0.01,0.05,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95,0.99,0.999,1) probit.scale.at <- qnorm(probit.scale.values)

Not real data. It was gererated randomly. The original codes are the following: par(mfrow=c(2,1)) n <- 500 ######################### #DATA GENERATING PROCESS# ######################### x1 <- rnorm(n,0,1) x2 <- rchisq(n,df=3,ncp=0)-3 sigma <- 1 u1 <- rnorm(n,0,sigma) ylatent1 <-x1+x2+u1 y1 <- (ylatent1 >=0) # create the binary indicator ####################### #THE

Dear all, I have a very simple set of data and I would like to analyze them with probit analysis. dose event trial 0.0 3 15 1.1 4 15 1.3 4 15 2.0 3 15 2.2 5 15 2.8 4 15 3.7 5 15 3.9 9 15 4.4 8 15 4.8 11 15 5.9 12 15 6.8 13 15 The dose should be transformed with log10(). I use glm(y ~ log10(dose), family=binomial(link=probit)) to do probit analysis, however, I have to exclude the