Thanks Gavin,
Your suggestion seems promising. I don't think I'll do the derivatives
analysis at this time so no hurry on those codes.
New question. I am wondering if there are additional considerations when
making multiple comparasons with a gamm model.
I have been testing a large number of
1) Visually - unless it actually matters exactly on which day in the
year the peak is observed? If visually is OK, just do `plot(mod, pages
= 1)` to see the fitted splines on a single page. See `?plot.gam` for
more details on the plot method.
2) You could generate some new data to predict upon as
Thanks again Gavin, this works.
gamm() also models the long term trend with a spline s(Time), which is
great. I would still like though, to be able to say whether the factor is
trending up or down over time. Would it be fair to query
summary(mod$lme)$tTable
and to look at the p-value and Value
Sorry about the errors (typos, not syntax errors) - I was forgetting
that you'd need to use `gamm()` and hence access the `$gam` component
I don't follow the point about a factor trending up or down. You
shouldn't try to use the `$lme` part of the model for this.
`summary(mod$gam)` should be
Hello all,
I am thinking about applying season::cosinor() analysis to some
irregularely spaced time series data. The data are unevenly spaced, so
usual time series methods, as well as the nscosinor() function are out. My
data do however trend over time and I am wondering if I can feed date as
I would probably attack this using a GAM modified to model the
residuals as a stochastic time series process.
For example
require(mgcv)
mod - gamm(y ~ s(DoY, bs = cc) + s(time), data = foo,
correlation = corCAR1(form = ~ time))
where `foo` is your data frame, `DoY` is a
