See ?predict.gam
You create a data frame (e.g. `dat2011') containing the 2011 values for
RH, solar, windspeed and transport, then
predict(cod,dat2011,type=response)
there are various options for type -- see ?predict.gam
On 09/28/2011 10:10 AM, pigpigmeow wrote:
For example:
GAMs and
On Wed, 2011-09-28 at 02:10 -0700, pigpigmeow wrote:
For example:
GAMs and after stepwise regression:
You probably don't want to be doing stepwise model/feature selection in
any regression model. Marra Wood (2011, Computational Statistics and
Data Analysis 55; 2372-2387) show results that
For example:
GAMs and after stepwise regression:
cod-gam(newCO~RH+s(solar,bs=cr)+windspeed+s(transport,bs=cr),family=gaussian
(link=log),groupD,methods=REML)
I used 10 year meterorology data (2000-2010) to form equation of
concentration of carbon monoxide.
NOW, I have 2011 meteorology data, I
I have 5 GAMs ( model1, model2, model3, model4 and model5)
Before I use some data X(predictor -January to June data) to form a equation
and calculate the expected value of Y (predictand -January to June). After
variable selection, GAMs (Model 1)were bulit up! R-square :0.40
NOW, I want to use
Your questions is pretty opaque. Please adhere to the posting guide. Provide
a self-contained (!) example (i.e., code) that reproduces your problem.
Generally, you would predict like this:
x-rnorm(100)
e-rnorm(100)
y-x+x^2+e
reg-gam(y~s(x))
plot(reg)
predict(reg,newdata=data.frame(x=2))
where
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