similar to: ordinal logistic model with pre-defined coefficients

R-Experts: I am using the vglm function of the VGAM library to perform proportional odds ordinal logistic regression. The issue that I would like help with concerns the format in which the response variable must be provided for this function to work correctly. Consider the following example: ------ library(VGAM) library(MASS) attach(pneumo) pneumo # Inspect the format of the original dataset

Hi, ?? Are there any packages which prints out p-values for OLR's (like `ologit' from Stata)? I want to run a bunch of OLRs and print the p-value for the first coefficient from each of them. ? I checked polr() under MASS and it doesn't. ?There's a lrm() function under Design which does print out p-values but I couldn't extract p-values from the output. ? Thanks, ? Debs

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

A student brought this question to me and I can't find any articles or examples that are directly on point. Suppose there are 2 ordinal logistic regression models, and one wants to set them into a simultaneous equation framework. Y1 might be a 4 category scale about how much the respondent likes the American Flag and Y2 might be how much the respondent likes the Republican Party in America.

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 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.

Hello everybody, I am hoping you can help me. I just downloaded and installed the "precompiled binary distribution" of R to my Windows machine (running Windows Millennium). (I did not download or install anything else - no packages). I need to run a logistic regression and understand the function I need is polr() or maybe SSlogis(). However, when I try to invoke either function,

Hello list, Is there any mean to fit models for ordinal response other than multinomial polytomous ("multinom" from nnet ) and cumulative logit ("polr" from MASS)? I am particularly interested in continuation-ratio model and adjacent-category logit model. It is for the sake of epidemiology in wild-living populations! Many thanks, Emmanuelle Fromont

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:

Dear All, I am new to ordinal logistic regression. Using ordinal regression within the R Commander GUI, I have generated an independent variable that is significant, but whose 95% confidence intervals slightly crosses "1". Is this possible? Here is the syntax and output: polr(formula = CDIcat ~ Employment, data = CDIallvariables, Hess = TRUE, method = "logistic")

Hi, how can I do multinomial logistic regression in R? I think glm() can only handle binary response variable, and polr() can only handle ordinal response variable. how to do logistic regression with multinomial response variable? Thanks __________________________________

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

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 =

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,

What function and package I use to conduct ordinal regression?? My data is composed 2colums and 180rows. The first colum indicate level of mass and second colum is intensity. So, I want to calculate how much intensity are related mass. [[alternative HTML version deleted]]

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

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'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 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