similar to: GHK Simulator for Ordered Probit

Dear R-community, not to re-invent the wheel I wonder if someone of you has ever written a function to compute the GHK smooth recursive simulator to estimate multivariate normal probabilities. See for instance page 194 of @BOOK{Greene97, author = {William H. Greene}, year = 1997, title = {Econometric Analysis}, edition = {3rd}, publisher = {Prentice-Hall}, address = {New Jersey

Hello. I have yet to receive a response to my previous post, so I may have done a poor job asking the question. So, here is the general question: how can I run a run a multivariate (more than one non-independent, response variables) ordered probit regression model? I've had success doing this in the univariate case using the vglm() function in the VGAM package. For example:

> 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

Hi, I am new in R. I would like to do a ordered probit regression using clm2 (in the ordinal package). My dependent variable y is the way of payment in M&A: y=0 if the deal is financed by stock only, y=1 if the deal is financed by a mix of cash and stock and y=2 if it is by cash only. My independent variables are CollateralB, Cashavailable and Leverage. This is the code I wrote: >

Dear All, Anyone know if there is a package that fits Generalized Linear Models(GAM) to data with ordered dependent variable(response) ? Simon Wood's mgcv has probit, logit,... other links, however, I could not find a way to do GAM *ordered *probit. Yee's VGAM claims to fit ordinal proportional odds model(cumulative logit model) (see: http://www.stat.auckland.ac.nz/~yee/VGAM/) but I

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

Hello, First time poster here so let me know if you need any more information. I am trying to run an ordered probit with maximum likelihood model in R with a very simple model (model <- econ3 ~ partyid). Everything looks ok until i try to run the optim() command and that's when I get " Error in rep(1, nrow(x)) : invalid 'times' argument". I had to adapt the code from a 4

Hi, I have a problem with the ordered probit model -polr function (library MASS). My independent variables are countinuos. I am not able to understand two main points: a) how to calculate marginal effects b) how to calculate the relative importance of each independent variables If required i will attach my model output. Thanks Franco

What is the current state of the R-art for ordered probit models, and more esoterically is there any available R strategy for ordered cauchit models, i.e. ordered multinomial alternatives with a cauchy link function. MCMC is an option, obviously, but for a univariate latent variable model this seems to be overkill... standard mle methods should be preferable. (??) Googling reveals that spss

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

Hello, currently I am estimating an ordered probit model with the function polr (MASS package). Is there a simple way to obtain values for the prediction of the index function ($X*\hat{\beta}$)? (E..g. in the GLM function there is the linear.prediction value for this purpose). If not, is there another function / package where this feature is implemented? Thank you very much for

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

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

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

I have had some weird results using the optim() function. I wrote a probit likelihood and wanted to run it with optim() with simulated data. I did not include a gradient at first and found that optim() would not even iterate using BFGS and would only occasionally work using SANN. I programmed in the gradient and it iterates fine but the estimates it returns are wrong. The simulated data work

Hi, If you plot a cumulative histogram of a gausian distribution, using a log scale on the x-axis and a probit scale on the y-axis, you get a straight line. My question is whether it is possible in R to use a "probit" scale in a "plot". For example on the following webpage you can see an application of how I would like to use a probit scale:

Dear R-community, I am currently replicating a study and obtain mostly the same results as the author. At one point, however, I calculate marginal effects that seem to be unrealistically small. I would greatly appreciate if you could have a look at my reasoning and the code below and see if I am mistaken at one point or another. My sample contains 24535 observations, the dependent variable

Hi, I would appreciate if someone could help me on track with this problem. I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this: sum_i( A+b_i>0 & A+b_i>C+d_i) = x sum_i( C+d_i>0 & C+d_i>A+b_i) = y sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z A, C, E are free variables while the other

Hi, is there a function in R to convert data read with read.shape and which is originally in UTM coordinates into longitude / latitude coordinates? I found the convUL() function from the PBSmapping package but I have no idea how I could apply that to the read.shape object. Many thanks, Werner __________________________________________________ Do sragenden Schutz gegen Massenmails.

Hey everyone, I've been reading an old scientific paper (well, not that old, about 15 years) and I want to verify the authors' statistical results. The paper is fairly unclear about what exactly they did, and none of the relatively simple commands I'm familiar with are producing results similar to theirs. The data is dose-response, recorded as binomial data: structure(list(X1 =