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pi.mix=<-list(formula=~mixture)
This works because the default design data has a mixture column with C-1 values (thanks Jeff).
I am certain this has probably been spelled out before somewhere or is common knowledge to others but I thought it might deserve it's own topic for this forum. I noticed it when i saw my model (assuming a mixture of 3 classes) fitting 3 estimates for p but only one estimate for pi (rather than 2). (As an aside, I am not sure how to interpret the single pi estimate, but my guess is that the single estimate suggest equal probability of assignment to the first two classes and the remainder (1-2*pi) is the probability of assignment for the third class?)
The mixture column in the default design data for pi would also allow for models to test for heterogeneity within groups (such as sex) using a mixture*group pi model. Pledger et al. 2003 discuss this briefly but don't illustrate an example and I would be very keen to see an example of this being implement if anyone knows of one.