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.breheny@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
>

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