Thank you. The binomial()$linkinv() is good to know.
Ben
On Wed, Mar 14, 2012 at 12:23 PM, Patrick Breheny
wrote:
> Actually, I responded a bit too quickly last time, without really reading
> through your example carefully. You're fitting a logistic regression model
> and plotting the results on the probability scale. The better way to do
> what you propose is to obtain the confidence interval on the scale of the
> linear predictor and then transform to the probability scale, as in:
>
> x <- seq(0,1,by=.01)
> y <- rbinom(length(x),size=1,p=x)
> require(gam)
> fit <- gam(y~s(x),family=binomial)
> pred <- predict(fit,se.fit=TRUE)
> yy <- binomial()$linkinv(pred$fit)
> l <- binomial()$linkinv(pred$fit-1.**96*pred$se.fit)
> u <- binomial()$linkinv(pred$fit+1.**96*pred$se.fit)
> plot(x,yy,type="l")
> lines(x,l,lty=2)
> lines(x,u,lty=2)
>
>
> --
> Patrick Breheny
> Assistant Professor
> Department of Biostatistics
> Department of Statistics
> University of Kentucky
>
>
>
>
> On 03/14/2012 01:49 PM, Ben quant wrote:
>
>> That was embarrassingly easy. Thanks again Patrick! Just correcting a
>> little typo to his reply. this is probably what he meant:
>>
>> pred = predict(fit,data.frame(x=xx),**type="response",se.fit=TRUE)
>> upper = pred$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$getFactorName(),ylab=**ylab,ylim=
>> c(min(down),max(up)))
>> lines(xx,upper,type="l",lty='**dashed')
>> lines(xx,lower,type="l",lty='**dashed')
>>
>> In my opinion this is only important if the desired y axis is different
>> than what plot(fit) gives you for a gam fit (i.e fit <-
>> gam(...stuff...)) and you want to plot the confidence intervals.
>>
>> thanks again!
>>
>> Ben
>>
>> On Wed, Mar 14, 2012 at 10:39 AM, Patrick Breheny
>> >>
>> wrote:
>>
>> The predict() function has an option 'se.fit' that returns what you
>> are asking for. If you set this equal to TRUE in your code:
>>
>> pred <- predict(fit,data.frame(x=xx),_**_type="response",se.fit=TRUE)
>>
>>
>> will return a list with two elements, 'fit' and 'se.fit'. The
>> pointwise confidence intervals will then be
>>
>> pred$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.
>>
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>>
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