Hello Sir,
I am using mgcv package for my data.
My model is y~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
procedure in R 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?
How can I find variance covariance matrix for that model and confidence
interval for that model ? I tried delta method but I am not getting result.
Hope you understand my question?
Thanks
[[alternative HTML version deleted]]
predict(sm1,type="terms",terms="s(x2)") see ?predict.gam Cheers Joris On Mon, Jun 7, 2010 at 7:25 PM, Dipa Hari <haridipa at yahoo.com> wrote:> > > Hello Sir, > I am using mgcv package for my data. > My model is?y~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 procedure?in?R? 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? > How can I find variance covariance matrix? for that model and confidence interval for that model ? I tried delta method but I am not getting result. > Hope you understand my question? > Thanks > > > > > > ? ? ? ?[[alternative HTML version deleted]] > > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > >-- Joris Meys Statistical consultant Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control tel : +32 9 264 59 87 Joris.Meys at Ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
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 modelThat 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? > ThanksHTH G -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%