Prof Brian Ripley <ripley@stats.ox.ac.uk> writes:
I believe you are right, but can you please explain why anyone would want=
=20
to fit this model?
When analysing data from mixture designs, the variables add up to a constant, and it is often preferrable to fit models without intercept term. We often have experiments where a mixture design (typically raw materials) is combined with a factorial design (typically process settings), and these are often modelled as the product of a polynomial in the mixture components (without intercept) and a polynomial in the factorial components.(*) More often than not, either the raw materials or the process settings cannot be completely randomised, leading to models with error terms. It is also quite common that the experiment has to be run over several days, leading to an error term for day.
There is a much simpler fix: in the line
if(intercept) nmstrata <- c("(Intercept)", nmstrata)
remove the condition (and drop the empty stratum later if you want).
You are right. I didn't see that one. --- (*) In these models, we have to manually code the factorial terms into continuous variables, in order to circumvent the coding logic in model.matrix() (when there is no intercept term, the first factor is coded as dummy variables, one variable per level of the factor, which makes these models overparametrised), but that is perhaps a different story. :-) --=20 Bj=F8rn-Helge Mevik, dr.scient.