similar to: changing coefficient names in a model

With the incorporation of the useful hints, my user function now looks like this: userfn <- function() { ... ow <- options("warn") options(warn = 2); ... reg<-try(polr(act~.,data=mm,Hess=TRUE)) ... sumreg<-try(summary(reg)) print(length(sumreg)) print(sumreg) ... options(ow) # reset } The routine userfn() is called multiple times, two of which I happen to know to

Dear R experts: I am running some calculations using polr() in MASS library, and found some differences in results obtained on two different machines (IRIX 6.5, and Linux RH 7.1). It is not clear to me whether this is due to some error in my programming the calculation and how to resolve the differences, if possible. The polr() call is the following:

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

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

I'm running polr() and getting warning messages from glm.fit(). It seems reasonable to use glm.control() to turn on the trace and follow what glm.fit() does when called by polr(); or is it? glm.control(maxit=10, trace=TRUE) polr(act~., data=mm) The glm.control() sets the trace TRUE, but there's no change in the output from polr(). Many thanks in advance for any help/pointers.

Dear friends. Do Polr in Mass change the sign of the coefficients ? Example (McCullagh 1980) options(contrasts=c("contr.treatment","contr.poly")) library(Mass) freq <- c(19,29,24,497,560,269) yy <- ordered(gl(3,1,6)) z4 <- polr(yy~x,weights=freq) > z4 Call: polr(formula = yy ~ x, weights = freq) Coefficients: x2 -0.6026492 Intercepts: 1|2

Hi, I am trying to fit a ordinal GEE model using ordgee {geepack}. In order to check the validity of the function, I specified the correlation structure as independence (i.e. constr = "independence") and compared the result with that using polr {MASS}. Because a GEE model with an independent working correlation structure is equivalent to an ordinary GLM model, we would expect the same

Hi, I am trying to build an ordinal regression model using polr (from the MASS package). In order to construct an initial model (without an error aborting it) in my setting, I must pass in a "start" parameter. I would then like to use the "step" function to remove unnecessary variables from the model. However, this fails with the error message: > mod1 <-

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Hi, I want to do a global likelihood ratio test for the proportional odds logistic regression model and am unsure how to go about it. I am using the polr() function in library(MASS). 1. Is the p-value from the likelihood ratio test obtained by anova(fit1,fit2), where fit1 is the polr model with only the intercept and fit2 is the full polr model (refer to example below)? So in the case of the

Hi, I'm getting an error message using polr(): Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : initial value in 'vmmin' is not finite The outcome variable is ordinal and factored, and the independant variable is continuous. I've checked the source code for both polr() and optim() and can't find any variable called

Hello, I am using polr(...) to generate a model. The summary shows the coefficients and the intercepts. For example: coefficient for x1 = c1 coefficient for x2 = c2 intercept A|B = i1 intercept B|C = i2 I can then run predict(..., type="p") with the model and see the odds for each factor. For example: A B C 1 0.3 0.5 0.2 2 0.4

Dear members of the list, I'm fitting ordinal regressions using polr, and in some models I get the error copied below. Dependent variable is an ordered factor of bird abundance categories, and predictors are continuous habitat variables. > ro6 <- polr(formula = abun ~ InOmbrot + Oliva.OC + ToCultAr + DivCulArb + AltitMax + COORXY) > summary(ro6) Re-fitting to get Hessian

Hi all, The problem is that when you try to use the function summary of a polr object in a function, it does not work. The problem is not related to the formula or the structure of data involved. It is probably related to the use of the function "vcov" in the code of summary for polr, and the iterative procedure to estimate the Hessian. Anyway, here there is an example extracted from

Greetings: I'm running R-1.9.1 on Fedora Core 2 Linux. I tested a proportional odds logistic regression with MASS's polr and Design's lrm. Parameter estimates between the 2 are consistent, but the standard errors are quite different, and the conclusions from the t and Wald tests are dramatically different. I cranked the "abstol" argument up quite a bit in the polr

Hi, I'm trying to fit a proportional ordinal logistic model using function polr() (package MASS). Is there a way to fix certain betas in the regression (e.g. function arima() allows this by defining fixed ) Maybe there is another function than polr() which allows that? Thanks Kazys [[alternative HTML version deleted]]

Dear r-help listers, I am using effects to produce an effect plot after the proportional odds logistic regression model. There is no problem for me to estimate the model, but when it comes to the graphing, I was stuck. see the codes below: ############################################################################## myologit <- polr(factor(warm) ~ yr89 + male + white + age + ed + prst, +

Dear all, I have used polr in MASS but I am uncertain about the summary(polr.object) interpretation and would be happy for help on that. This is my summary: > summary(shade.polr) Re-fitting to get Hessian Call: polr(formula = as.ordered(shade) ~ as.factor(objekt), data = sof, weights = as.numeric(frek)) Coefficients: Value Std. Error t value 2.1699520 0.3681840 5.8936612

Dear R helpers, I would like to ask why polr occasionally generates results that look very odd. I have been trying to compare the power of proportional odds logistic regression with the Wilcoxon test. I generated random samples, applied both tests and extracted and compared the p-values, thus:- library(MASS) c1=rep(NA,100); c2=c1 for (run in 1:100) { dat=c(rbinom(20,12,0.65),rbinom(20,12,0.35))