Testing proportional odds assumption in R

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Testing proportional odds assumption in R

Thomas Yee

M1 and M2 are extreme in that all or none of the variables have
parallel lines on the logit scale.  One can try fitting a partial
POM, which remains fraught (but not as much as M2) because if
the lines intersect for a particular variable where the data lie
then there will be numerical problems.

For example, try something like

M3 <- vglm(y ~ x1 + x2 + x3, data = data,
           family = cumulative(parallel = FALSE ~ x3 - 1))

which will only allow nonparallelism wrt the x3 variable.

Another idea is to try transforming each x variable if
it crashes... that might help.

BTW, can use lrtest(), e.g., lrtest(M3, M2)



 >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
 >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


 >However, I found a pdf by Agresti suggesting a method using the
 >function, the pdf is called "Examples of Using R for Modeling
 >> M1 <- vglm(y ~ x1 + x2 + x3, data=data,
 >> M2 <- vglm(y ~ x1 + x2 + x3, data=data, family=cumulative, maxit=100)
 >If the test is significant, the proportional odds assumption is (might be)
 >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
 >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]]

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