[R] Partial proportional odds logistic regression

From: Inman, Brant A. M.D. <Inman.Brant_at_mayo.edu>
Date: Tue 09 Jan 2007 - 03:10:03 GMT

Just a follow-up note on my last posting. I still have not had any replies from the R-experts our there that use partial proportional odds regression (and I have to hope that there are some of you!) but I do think that I have figured out how to perform the unconstrained partial proportional odds model using vglm. I show this code below for the benefit of others that may want to try it (or point out my errors) using one of the datasets in Petersen and Harrell's paper (Appl Stat 1990). However, I remain open for suggestions on how to implement the unconstrained partial proportional odds model.


library(VGAM)
library(MASS)
library(Design)

#######################################################################

# Nausea dataset
# Peterson and Harrell. Applied Statistics 1990, 39(2): 205-217
nausea.short <- data.frame(matrix(nrow=12, ncol=3))	#Table 2
	colnames(nausea.short) <- c('nausea', 'cisplatin', 'freq')
	nausea.short[,1] <- ordered(rep(seq(0,5,1),2),
labels=seq(0,5,1))
	nausea.short[,2] <- c(rep(0,6), rep(1,6))
	nausea.short[,3] <- c(43,39,13,22,15,29,7,7,3,12,15,14)

# Proportional odds ordinal logistic regression: 3 options
polr(nausea ~ cisplatin, weights=freq, data=nausea.short, method='logistic')
lrm(nausea ~ cisplatin, weights=freq, data=nausea.short) vglm(nausea ~ cisplatin, weights=freq, data=nausea.short, family=cumulative(parallel=T, reverse=T))

# Unconstrained partial proportional odds ordinal logistic regression
vglm(nausea ~ cisplatin, weights=freq, data=nausea.short, family=cumulative(parallel=F, reverse=T))


The results obtained with this approach appear consistent with those presented in Table 3 of the paper. However, the code for the unconstrained partial proportional odds model is so simple (just one letter is different than in the proportional odds model!) that I wonder if there is not room for error here that I am too inexperienced to identify.

Again, help with the constrained model would be greatly appreciated.

Brant Inman



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