That was embarrassingly easy. Thanks again Patrick! Just correcting a little typo to his reply. this is probably what he meant:
pred = predict(fit,data.frame(x=xx),type="response",se.fit=TRUE) upper = pred$fit + 1.96 * pred$se.fit lower = pred$fit - 1.96 * pred$se.fit # For people who are interested this is how you plot it line by line: plot(xx,pred$fit,type="l",xlab=fd$getFactorName(),ylab=ylab,ylim= c(min(down),max(up))) lines(xx,upper,type="l",lty='dashed') lines(xx,lower,type="l",lty='dashed') In my opinion this is only important if the desired y axis is different than what plot(fit) gives you for a gam fit (i.e fit <- gam(...stuff...)) and you want to plot the confidence intervals. thanks again! Ben On Wed, Mar 14, 2012 at 10:39 AM, Patrick Breheny <patrick.breh...@uky.edu>wrote: > The predict() function has an option 'se.fit' that returns what you are > asking for. If you set this equal to TRUE in your code: > > pred <- predict(fit,data.frame(x=xx),**type="response",se.fit=TRUE) > > will return a list with two elements, 'fit' and 'se.fit'. The pointwise > confidence intervals will then be > > pred$fit + 1.96*se.fit > pred$fit - 1.96*se.fit > > for 95% confidence intervals (replace 1.96 with the appropriate quantile > of the normal distribution for other confidence levels). > > You can then do whatever "stuff" you want to do with them, including plot > them. > > --Patrick > > > On 03/14/2012 10:48 AM, Ben quant wrote: > >> Hello, >> >> How do I plot a gam fit object on probability (Y axis) vs raw values (X >> axis) axis and include the confidence plot lines? >> >> Details... >> >> I'm using the gam function like this: >> l_yx[,2] = log(l_yx[,2] + .0004) >> fit<- gam(y~s(x),data=as.data.frame(**l_yx),family=binomial) >> >> And I want to plot it so that probability is on the Y axis and values are >> on the X axis (i.e. I don't want log likelihood on the Y axis or the log >> of >> my values on my X axis): >> >> xx<- seq(min(l_yx[,2]),max(l_yx[,2]**),len=101) >> plot(xx,predict(fit,data.**frame(x=xx),type="response"),** >> type="l",xaxt="n",xlab="Churn"**,ylab="P(Top >> Performer)") >> at<- c(.001,.01,.1,1,10) #<-------------- I'd also like to generalize >> this rather than hard code the numbers >> axis(1,at=log(at+ .0004),label=at) >> >> So far, using the code above, everything looks the way I want. But that >> does not give me anything information on variability/confidence/** >> certainty. >> How do I get the dash plots from this: >> plot(fit) >> ...on the same scales as above? >> >> Related question: how do get the dashed values out of the fit object so I >> can do 'stuff' with it? >> >> Thanks, >> >> Ben >> >> PS - thank you Patrick for your help previously. >> >> [[alternative HTML version deleted]] >> >> ______________________________**________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help> >> PLEASE do read the posting guide http://www.R-project.org/** >> posting-guide.html <http://www.R-project.org/posting-guide.html> >> and provide commented, minimal, self-contained, reproducible code. >> > > > -- > Patrick Breheny > Assistant Professor > Department of Biostatistics > Department of Statistics > University of Kentucky > [[alternative HTML version deleted]] ______________________________________________ R-help@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.
