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]]

