similar to: Ordered Probit/Logit with random coefficients

Dear List, I am using both the clm() and clmm() functions from the R package 'ordinal'. I am fitting an ordinal dependent variable with 5 categories to 9 continuous predictors, all of which have been normalised (mean subtracted then divided by standard deviation), using a probit link function. From this global model I am generating a confidence set of 200 models using clm() and the

Dear list, I'd like to create a visual plot of a clmm() I've fitted using the 'ordinal' package in R. It's possible to do this with a glm() by using the 'effects' package. For example: library(effects) data(BEPS) mod <- lm(political.knowledge ~ age + gender + vote, data=BEPS) eff <- effect("age", mod, default.levels=100) plot(eff,

> 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

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

Hi all, I am trying to fit a random effect  model to categorical response variable using package "ordinal" /"clmm". How can I find the correlation between random effects (random intercept and random slope)   Thanks in advance  Ana [[alternative HTML version deleted]]

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",

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

Dear all, I am using glm function in order to estimate a logit model i.e. glm(Y ~ data[,2] + data[,3], family = binomial(link = "logit")). I also created a function that estimates logit model and I would like it to compare it with the glm function. So, does anyone know what optimizer or optimization method glm uses in order to derive the result? Thank you Dimitris -- View this

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 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 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

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 Dirk, I just upgraded to Ubuntu 15.10. It seems that the r-packages are not upgrading although deb files exist on the cran repository. For instance, r-cran-rjags is back to 3-15-1 On the repository, I saw that the 'Sources' and 'Packages' files in wily folder show vivid version of packages although dated to yesterday : https://cran.univ-paris1.fr/bin/linux/ubuntu/wily/Sources

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:

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

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, 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