I have just discovered an interesting feature of the gam function. In
fitting complex time series data using quasi-likelihood, I have found that
the final results of the fit depend on the order of the covariates. For
example, the model
log(y) = a + lo(day,span=0.05) + lo(year) + lo(x)
will give slightly different estimates of the dispersion parameter,
residual deviances, residual degrees of freedom, standard errors and the
like than a model, say,
log(y) = a + lo(year) + lo(x) + lo(day,span=0.05).
The differences all occur in the fifth decimal place, so that the effects
are extremely minor, but nevertheless disconcerting.
I'm curious to know whether any one else has run into this and whether
there is a simple explanation.
Mark Goldberg,PhD
E-Mail: mark@polair.epi.mcgill.ca
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