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
<patrick.breh...@uky.edu <mailto: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 <mailto: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.
______________________________________________
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.
