similar to: glm (probit/logit) optimizer

> I make a study in health econometrics and have a categorical > dependent variable (take value 1-5). I would like to fit an ordered > probit or ordered logit but i didn't find a command or package who > make that. Does anyone know if it's exists ? R is very fancy. You won't get mundane things like ordered probit off the shelf. (I will be very happy if someone will show

Hello, I'm working on the multiple categorical data (5-points scale) using linear mixed effect model and wondering if anyone knows about or works on the linear mixed effect model with ordered logit or probit link. I found that the "lmer" function in R is very flexible and supports various models, but not ordered logit/probit models. I may conduct my analysis by turning my DVs

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",

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

Does anybody know how to do multiple logit/probit analysis with R? Thanks in advance!

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

Hello everyone I am trying to fit an ordered probit/logit model for bank rating prediction. Besides polr() in MASS package which is not written especially for this as far as I know, do you know how else I can do this? I already found the modified polr () version on the Valentin STANESCU Enrst and Young Tel. 402 4000 ---------------------------------------------------------- The information

Hello all, I am wondering if there is a way to do a spatial error probit/logit model in R? I can't seem to find it in any of the packages. I can do it in MATLAB with Gibbs sampling, but would like to confirm the results. Ideally I would like to use this model to predict probability of parcel conversion in a future time period. This seems especially difficult in a binary outcome model

Dear all, I would like to use the apply or a similar function belonging to this family, but applying for each column (or row) but let say for each q columns. For example I would like to apply a function FUN for the first q columns of matrix X then for q+1:2*q and so on. If I do apply (X, 2, FUN) it applies for each column and not for every q columns. Is that possible with any similar function?

Dear List I am trying to fit a simutaneous equation logit model. i.e., the response variables of the structured equations are binomial, I am not sure if systemfit can do this job. A google search doesn't yield too much helpful information. Your knowledge on any other packages or codes are appreciated. Thanks will

Dear all, I would like to create a code for semiparametric Klein and Spady's estimator. For that I created a function that provides the log-likelihood function for each observation (so it is a function of betas and i, where i denotes the observation). Now, in order to maximize the log-likelihood function, I have to sum these log-likelihood functions for each i and so to get another function

Dear R users/experts, I've heard it's possible to estimate the above kinds of models in R. However, after (an admittedly brief) survey of the packages, I haven't found an obvious candidate. Can you offer any help? (Yes I'm a newby.) Yours sincerely Christoph Hanck Studentische Hilkskraft Lehrstuhl f?r Empirische Wirtschaftsforschung, Prof. Dr. Wilfling

Dear all, I am trying to use the integrate function in R but it seems that it does not work in my example and I cannot figure out why. I create a function Mu1 (which works fine) and try to integrate by the code: n <- 100 Ctrl <- as.matrix(cbind(runif(n, -30, 30))) W <- Ctrl + as.matrix(rnorm(n)) Rsp <- (W>as.matrix(sample(10, n, T)))*1 Mu1 <- function(x, Y=Rsp, Xc=Ctrl){ x

Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

Dear R users: I am using lmer fo fit binomial data with a probit link function: > fer_lmer_PQL<-lmer(fer ~ gae + ctipo + (1|perm) -1, + family = binomial(link="probit"), + method = 'PQL', + data = FERTILIDAD, + msVerbose= True) The output look like this: > fer_lmer_PQL Generalized linear mixed model fit

Dear R helpers, I need to estimate a probit model with box constraints placed on several of the model parameters. I have the following two questions: 1) How are the standard errors calclulated in glm (family=binomial(link="probit")? I ran a typical probit model using the glm probit link and the nlminb function with my own coding of the loglikehood, separately. As nlminb does not

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

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) <-

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,

If I wanted to fit a logit model and account for clustering of observations, I would do something like: library(Design) f <- lrm(Y1 ~ X1 + X2, x=TRUE, y=TRUE, data=d) g <- robcov(f, d$st.year) What would I do if I wanted to do the same thing with a probit model? ?robcov says the input model must come from the Design package, but the Design package appears not to do probit? Thanks very