search for: polres

Hello I am running R 1.8.1 on a Windows platform I am attempting to fit an ordinal logistic regression model, using the polr function, as described in Venables and Ripley. But when I try model4 <- polr(ypsxcat~committed + as.factor(sex) + as.factor(drugusey) + anycsw + as.factor(sex)*committed + as.factor(sex)*as.factor(drugusey)+as.factor(sex)*anycsw, data = duhray) I get a message

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:

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,

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.

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

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

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 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 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

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 +

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

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 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

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 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

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 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))

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

Dear all, To modelize the abundance of fish (4 classes) with a set of environmental variables, I used the polr and predict.polr functions. I would like to know how to bring the cumulated probabilities back to a discrete ordinal scale. For the moment I used the predict.polr function with the argument "class". Is there an other way? polrf <- polrf <- polr_mod(formula =

Hi, I obtained all the coefficients that I need from polr. However, I'm wondering how I can obtain the standard error of each estimated coefficient? I saved the Hessian and do something like summary(polrObj), I don't see any standard error like when doing regression using lm. Any help would be really appreciated. Thank you! - adschai