U could try the predict function with se.fit=true. I believe this
should give u the predicted score and se and u can calculate CI from
there.
U'll have to create an input matrix with the score u want to predict for.
Chris Howden
Founding Partner
Tricky Solutions
Tricky Solutions 4 Tricky Problems
Dear Jari,
Thanks a lot for your very helpful comments. I still believe that my
results are wrong using the 'standard' RDA code.
Using the following code, I receive 'points' for the Y matrix (height)
and arrows for the X matrix (species) (see pdf). It should be the
opposite way (as seen i
Dear Jari,
Thanks a lot for your very helpful comments. I still believe that my
results are wrong using the 'standard' RDA code.
Using the following code, I receive 'points' for the Y matrix (height)
and arrows for the X matrix (species) (see pdf). It should be the
opposite way (as seen i
Sibylle,
I noticed that you also sent this message to the main R-news mailing list,
but may not have received any answers. I'm not sure that I can answer to
your questions either, since I don't quite follow your explanation. So here
comes a rambling in a parallel universe in hope we go on with sim
On Sun, 2011-09-25 at 22:01 -0700, Laura S wrote:
> Dear all:
>
> I was wondering if you have suggestions on how to analyze the spatial
> autocorrelation of tree abundance in a grid of contiguous quadrats given
> that the data set follows a zero inflated distribution, i.e., in many
> quadrats the
Hi Laura,
You might want to look at the lmmfit package:
http://finzi.psych.upenn.edu/R/library/lmmfit/html/lmmfit-package.html
which has several such measures (well, goodness of fit) for models with
one grouping variable.
You might get more traction on the R Mixed Models SIG.
I often wonder (k
On Tue, 2011-09-27 at 14:40 +0200, Marco Helbich wrote:
> thank you for clarifying.
> so I can remove them all at once.
Given their effects are already removed you could just work with the
model *as is*. If you refit, you might have to be careful to ensure that
the same model (and smooth complexit
Dear list,
I recently submitted a paper in which I analyzed plant growth response to
several environmental factors in several sites. I wanted to account for the
variation attributable to the different sites, so I made "site" a random effect
in a simple LME regression model (e.g.
m<-lme(plantgr
Dear list members,
I have a significant interaction between two continuous variables (it
happens to be a mixed model in lmer, but I imagine the same is applicable to
a glm). The interaction tells me that the relationship between z and y
changes as a function of x, but I want to know whether y has
thank you for clarifying.
so I can remove them all at once.
best
marco
Am 27.09.2011 13:50, schrieb Gavin Simpson:
On Tue, 2011-09-27 at 13:42 +0200, Marco Helbich wrote:
Gavin,
thank you for your reply, I appreciate it!
After consulting the proposed paper, I have tried your suggestion
setti
On Tue, 2011-09-27 at 13:42 +0200, Marco Helbich wrote:
> Gavin,
>
> thank you for your reply, I appreciate it!
>
> After consulting the proposed paper, I have tried your suggestion
> setting "select = T", which results again in another question:
>
> If the p-value is "NA" does this mean that t
Gavin,
thank you for your reply, I appreciate it!
After consulting the proposed paper, I have tried your suggestion
setting "select = T", which results again in another question:
If the p-value is "NA" does this mean that the smoothing term is droped
(or shrank to zero)? Independent of its h
On Tue, 2011-09-27 at 08:54 +0200, Marco Helbich wrote:
> Dear list,
>
> I am studying the influence of several environmental factors (numeric &
> dummies) on species densities (= numeric) using the gam()
> function with a gaussian link function in the mgcv package. As stated in
> Wood (2006) the
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