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$gam)` should be suffi
Brandon,
Are you asking if you can use betadisper as a substitute for post-anova
pairwise comparisons among levels? After using betadisper to obtain
dispersions, I believe you can plot the centroids for each level. In
addition to telling you if the dispersions differ among levels, you could
see ho
Thanks again Gavin, this works.
gamm() also models the long term trend with a spline s(Time), which is
great. I would still like though, to be able to say whether the factor is
trending up or down over time. Would it be fair to query
summary(mod$lme)$tTable
and to look at the p-value and "Value" c
You mean `betadisper()`? This simply computes a multivariate
dispersion about the kth group centroid for k groups. If you can
express the "levels within main effects" as a factor variable defining
the groups then `betadisper()` could work with that, but I'm not quite
following what you want to do.
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 Gavin,
This seems like a promising approach and a first pass suggests it
works with this data. I can't quite figure out how I would go about
interrogating the fitted spline to deterine when the peak value happens
with respect to DoY. Any suggestions?
-Jacob
On Tue, Mar 25, 2014 at 9:
Thanks for the words of caution on simper.
Am I completely off base in thinking that betadiver function (analgous to
Levene's test) could be used to examine variation between levels within
main effects?
Cheers
On Mon, Mar 24, 2014 at 5:08 PM, Brandon Gerig wrote:
> I am assessing the level of
Dear all,
Please, could someone confirm that vegdist(X, meth="binomial") is the
binomial deviance *scaled* (i.e. the scale-invarient version of
"binomial deviance")
I want to use the dissimilarity "binomial deviance *scaled*", described
by Anderson and Millar (2004) as
where
"scaled" since