similar to: coefficients' order in polr()?

Hi all, I am trying to do a ordered probit regression using polr(), replicating a result from SAS. >polr(y ~ x, dat, method='probit') suppose the model is y ~ x, where y is a factor with 3 levels and x is a factor with 5 levels, To get coefficients, SAS by default use the last level as reference, R by default use the first level (correct me if I was wrong), The result I got is a

Hi - I am using polr. I can get a result from polr fit by calling result.plr <- polr(formula, data=mydata, method="probit"); However, from the 'result.plr', how can I access standard error of the estimated coefficients as well as the t statistics for each one of them? What I would like to do ultimately is to see which coefficients are not significant and try to refit the

Dear All, I have been struggling to understand why for the housing data in MASS library R and stata give coef. estimates that are really different. I also tried to come up with many many examples myself (see below, of course I did not have the set.seed command included) and all of my `random' examples seem to give verry similar output. For the housing data, I have changed the data into numeric

hello everybody solved the problem with summary, now I have another one eg I estimate > try.op <- polr( > as.ordered(sod.sit.ec.fam) ~ > log(y) + > log(1 + nfiglimin) + > log(1 + nfiglimagg) + > log(ncomp - nfiglitot) + > eta + > I(eta^2) + >

Using ordered probit model, I get errors from dwt and bptest. dwt: Error in durbinWatsonTest.default(...) : requires vector of residuals bptest: Error in storage.mode(y) <- "double" : invalid to change the storage mode of a factor I imagine I have to restate as an individual probit model for each category, but is there an easier way? thanks, bp [[alternative HTML version

I have used plor() to model a rather large 3-category dataset (~1500 data points, ~15 independent variables); from the resulting model (with a deviance slightly below the residual degrees of freedom), the training data are placed in only the two extreme categories. Though the result appears to indicate that there's only a relative 'narrow' bin for the medium group, [and when the

Hi all. I'm just starting to explore ordinal multinomial regression. My dataset is 300,000 rows, with an outcome (ordinal factor from 1 to 9) and five independent variables (all continuous). My first stab at it was this: pomod <- polr(Npf ~ o_stddev + o_skewness + o_kurtosis + o_acl_1e + dispersal, rlc, Hess=TRUE) And that worked; I got a good model fit. However, a variety of other

Hi everyone, If I compute a "Ordered Logistic or Probit Regression" with the polr function from MASS package. the summary give me : coefficients, Standard error and Tvalue.. but not directly the p.value. I can compute "manualy" the Pvalue, but Is there a way to directly obtain the pa.value, and I wonder why the p.valeu is not directly calculated, is there a reason? exemple

I am new to R, ordered logistic regression, and polr. The "Examples" section at the bottom of the help page for polr<http://stat.ethz.ch/R-manual/R-patched/library/MASS/html/polr.html>(that fits a logistic or probit regression model to an ordered factor response) shows options(contrasts = c("contr.treatment", "contr.poly")) house.plr <- polr(Sat ~ Infl +

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

> 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 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

Hi all, I was trying a probit regression using polr() and got this message, Error in model.matrix.default(Terms, m, contrasts) : cannot allocate vector of length 828310236 The data is about 20M (a few days ago I asked a question about large file, thank you for responses, then I use MS Access to select those columns I would use). R is 2.3.1, Windows XP, 512M Ram. I am going to read

Hi, does anyone have a quick answer to the question of how to carry out interval regression in R. I have found "ordered logit" and "ordered probit" as well as multinomial logit etc. The thing is, though, that I want to apply logit/probit to interval-coded data and I know the cell limits which are used to turn the quantitative response into an ordered factor. Hence, it does

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

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 am running a model with four categories and want predicted probabilities in each category. Now for this example I wont give a counterfactual just the training data is fine but is there anyway to get a confidence interval around the predicted probabilities in each group? I have tried but it gives me probabilities and I have used interval="confidence", level=.095 and then interval

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

In polr.R the (several) functions gmin and fmin contain the code > theta <- beta[pc + 1L:q] > gamm <- c(-100, cumsum(c(theta[1L], exp(theta[-1L]))), 100) That's bad. There's no reason to suppose beta[pc+1L] is larger than -100 or that the cumulative sum is smaller than 100. For practical datasets those assumptions are frequently violated, causing the

I want to fit a multinomial model with logit link. For example let this matrix to be analyzed: male female aborted factor 10 12 1 1.2 14 14 4 1.3 15 12 3 1.4 (this is an example, not the true data which are far more complex...) I suppose the correct function to analyze these data is polr from MASS library. The data have been