similar to: cannot update polr model if I specify "start" parameters

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

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

Hi, I have a dataset that I am trying to analyze and plot as an ordered logistic regression (y = ordinal categories 1-3, x = continuous variable with values 3-9). First is a problem with cdplot: Produces a beautiful plot, with the "right" trend, but my independent factor values are transformed. The factor has values from 3-9, but the plot produces an x-axis with values from 20-140.

Hi, I'm trying to fit a ordered logistic regression. The response variable (y) has three levels (0,1,2). The command I've used is: /ordlog<-polr(y~x1+x2+x3+x4, data=finalbase, subset=heard, weight=wt, na.action=na.omit) / (There are no NA's in y but there are NA's in X's) The error I'm getting is: /Warning messages: 1: non-integer #successes in a binomial glm! in:

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 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 community, I am currently trying to fit an ordinal logistic regression model with the polr function. I often get the same error message : "attempt to find suitable starting values failed", for example with : require(MASS) data(iris) polr(Species~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width,iris) (I know the response variable Species should be nominal but I do as levels

Dear all, I've replicated the cheese tasting example on p175 of GLM's by McCullagh and Nelder. This is a 4 treatment (rows) by 9 ordinal response (cols) table. Here's my simple code: #### cheese library(MASS) options(contrasts = c("contr.treatment", "contr.poly")) y = c(0,0, 1, 7, 8,8,19, 8,1, 6,9,12,11, 7,6, 1, 0,0, 1,1, 6, 8,23,7,

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

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 +

Dear Sir, I get an error message when I use polr() in MASS package. My data is "ord.dat". I made "y" a factor. y y1 y2 x lx 1 0 0 0 3.2e-02 -1.49485 2 0 0 0 3.2e-02 -1.49485 3 0 0 0 1.0e-01 -1.00000 4 0 0 0 1.0e-01 -1.00000 5 0 0 0 3.2e-01 -0.49485 6 0 0 0 3.2e-01 -0.49485 7 1 1 0 1.0e+00 0.00000 8 0 0 0 1.0e+00 0.00000 9 1 1 0

Hi (1)In polr(), is there any way to calculate a pseudo analogue to the R^2. Just for use as a purely descriptive statistic of the goodness of fit? (2) And: what are the assumptions which must be fulfilled, so that the results of polr() (t-values, etc.) are valid? How can I test these prerequisites most easily: I have a three-level (ordered factor) response and four metric variables. many

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))

Hi all, I have a problem to estimate Std. Error and t-value by ?polr? in library Mass. They result from the summary of a polr object. I can obtain them working in the R environment with the following statements: temp <- polr(formula = formula1, data = data1) coeff <- summary(temp), but when the above statements are enclosed in a function, summary reports the following error:

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

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

Hello, I am trying to run an ordinal logistic regression (polr) using the package 'MASS'. I have successfully run other regression classes (glm, multinom) without much problem, but with the 'polr' class I get the following error: " Error in svd(X) : infinite or missing values in 'x' " which appears when I run the "summary" command. The data file is

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 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

For the following model, library(vcd) arth.polr <- polr(Improved ~ Sex + Treatment + Age, data=Arthritis) summary(arth.polr) where Improved is an ordered, 3-level response I'm looking for a *simple* way to test the validity of the proportional odds assumption, typically done via a score test for equal slopes/effects over the predictors. I do find a po.test= option in the repolr package