[R] monotonic factors

[Greg Snow]

One approach would be to code dummy variables for your factor levels, have d1 equal to 0 for 'low' and 1 for 'med' and 'high', then have d2 equal to 1 for 'high' and 0 otherwise.  For linear regression there are functions that will fit a model with all non-negative coefficients, but I don't know of anything like that for glms, so one option is to fit with all the dummy variables, then if any of the estimated coefficients are negative remove that variable (force it to 0) and refit.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Jeffrey Pollock
> Sent: Wednesday, October 12, 2011 6:29 AM
> To: r-help at r-project.org
> Subject: [R] monotonic factors
> 
> Hello all,
> 
> 
> 
> I have an ordered factor that I would like to include in the linear
> predictor of a binomial glm, where the estimated coefficients are
> constrained to be monotonic. Does anyone know how to do this? I've
> tried
> using an ordered factor but this does not have the desired effect, an
> (artificial) example of this follows;
> 
> 
> 
> n <- 100
> 
> strings <- sample(c("low", "med", "high"), n, TRUE)
> 
> 
> 
> x.ordered <- ordered(strings, c("low", "med", "high"))
> 
> x.unordered <- factor(strings)
> 
> 
> 
> pr <- ifelse(strings == "low", 0.4, ifelse(strings == "med", 0.3, 0.2))
> 
> 
> 
> y <- rbinom(n, 1, pr)
> 
> 
> 
> mod.ordered <- glm(y ~ x.ordered, binomial)
> 
> mod.unordered <- glm(y ~ x.unordered, binomial)
> 
> 
> 
> summary(mod.ordered)
> 
> summary(mod.unordered)
> 
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