On Mon, 2010-06-07 at 10:25 -0700, Dipa Hari wrote:
>
> Hello Sir,
> I am using mgcv package for my data.
> My model isy~x1+f(x2),I want to find out the function f(x2).
> Following is the code.
>
> sm1=gam(y~x1+s(x2),family=binomial, f)
> summary(sm1)
> plot(sm1,residuals=TRUE, xlab="AGE",pch=20)
>
> In this plot I am getting S(x2,1.93) on y axixs
> How should I get the function for x2 from this plot.or Is there
> anyother procedureinR to get this function or value for that
> particular function.for example f(x2)= log(x2) so from that plot how
> can I get this kind of formula for x2 variable?
## evaluate smooth at 200 equally spaced points across range of x2
n <- 200
ndat <- with(f, data.frame("x2" = seq(min(x2), max(x2), length = n))
pred <- predict(sm1, newdata = ndat, type = "terms", terms = "s(x2)")
## or for the observed data
pred <- predict(sm1, type = "terms", terms = "s(x2)")
> How can I find variance covariance matrix for that model
That can be done with the standard vcov() extractor method for "gam"
objects:
vcov(sm1)
> and confidence interval for that model ?
Model or smooth s(x2)? If the latter, add se.fit = TRUE to the predict
calls above. If the former;
pred <- predict(sm1, se.fit = TRUE)
or for predictions on scale of your response:
pred <- predict(sm1, type = "response", se.fit = TRUE)
And you can add newdata = ndat to either call if you want predictions
for the newdata points we generated earlier.
> I tried delta method but I am not getting result.
> Hope you understand my question?
> Thanks
HTH
G
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Dr. Gavin Simpson [t] +44 (0)20 7679 0522
ECRC, UCL Geography, [f] +44 (0)20 7679 0565
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