- About the first question, I was not sure about what was the proper model (
a) or b) ) because I saw this at the end of the help for te ---> ?te :
n <- 500
v <- runif(n);w<-runif(n);u<-runif(n)
f <- test2(u,v,w)
y <- f + rnorm(n)*0.2
# tensor product of 2D thin plate regression spline and 1D c
- About the visualization, my question is more about interpretation. In the
case of :
model_name <- gam ( bm ~ t + te (t_year, temp_W, temp_sept, k = 5, bs = c(
“cc”,”cr”,”cr”)), data = data)
* a)* vis.gam (model_name , view= c(“t_year”, “temp_W”))
*b)* vis.gam (model_name , view= c(“t_year
On 02/08/12 18:02, Bert Gunter wrote:
Well, geez! Without trying to upstage SImon, why not google it yourself?!
http://en.wikipedia.org/wiki/Akaike_information_criterion
-- one or two slightly surprising statements in there though :-)
I quite like the coverage in Davison (2003) "Statistical Mod
Bert, thanks for the very useful link. I am duly chastised for not
having searched thoroughly enough.
Will
On 8/2/2012 10:02 AM, Bert Gunter wrote:
Well, geez! Without trying to upstage SImon, why not google it yourself?!
http://en.wikipedia.org/wiki/Akaike_information_criterion
(seemed pret
Well, geez! Without trying to upstage SImon, why not google it yourself?!
http://en.wikipedia.org/wiki/Akaike_information_criterion
(seemed pretty clear to me...)
-- Bert
On Thu, Aug 2, 2012 at 9:54 AM, Will Shadish wrote:
> Simon, could you clarify this paragraph. We are submitting to a psych
Simon, could you clarify this paragraph. We are submitting to a
psychology journal and I am certain they will be asking us about why it
is still ok to use AIC for non-nested models. Thanks. Will Shadish
On 8/2/2012 9:49 AM, Simon Wood wrote:
For AIC model comparison, the usual advice applies th
Ricardo,
Your second construction (b) is the correct one (in a you are asking for
one marginal to be a 2 dimensional cubic regression spline, which
doesn't exist in mgcv).
For visualization, would the example at the end of ?te be the thing to
do? In 3d I find looking at a series of 2d slices
Hello R users,
I'm working with a time-series of several years and to analyze it, I’m using
GAM smoothers from the package mgcv. I’m constructing models where
zooplankton biomass (bm) is the dependent variable and the continuous
explanatory variables are:
-time in Julian days (t), to creat a long-
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