I want to test whether the proportional odds assumption for an ordered regression is met.
The UCLA website points out that there is no mathematical way to test the proportional odds assumption (http://www.ats.ucla.edu/stat//R/dae/ologit.htm), and use graphical inspection ("We were unable to locate a facility in R to perform any of the tests commonly used to test the parallel slopes assumption."). However, I found a pdf by Agresti suggesting a method using the vglm function, the pdf is called "Examples of Using R for Modeling Ordinal Data". > M1 <- vglm(y ~ x1 + x2 + x3, data=data, family=cumulative(parallel=TRUE)) > M2 <- vglm(y ~ x1 + x2 + x3, data=data, family=cumulative, maxit=100) > pchisq(deviance(Mo.1)-deviance(Mo.2), df=df.residual(Mo.1)-df.residual(Mo.2), lower.tail=FALSE) If the test is significant, the proportional odds assumption is (might be) violated. However, running this procedure in my dataset with 5 predictors (3 dichotomous, 2 z-standardized metric) and an ordinal dependent variable (0,1,2,3) in a sample of N=2500 leads to various problems, maybe you can help me out how to solve these. Warning messages: (1) Error in dotC(name = "tapplymat1", mat = as.double(mat), as.integer(nr), : NA/NaN/Inf in foreign function call (arg 1 (2) In matrix.power(wz, M = M, power = 0.5, fast = TRUE) : Some weight matrices have negative eigenvalues. They will be assigned NAs (3) In Deviance.categorical.data.vgam(mu = mu, y = y, w = w, residuals = residuals, : fitted values close to 0 or 1 (4) In log(prob) : NaNs produced The last two don't seem to be as critical and the first two, seeing that models with that errors at least provide a p-value (the first to errors lead to a p--value of 1). Thank you [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
