R help - Mar 2014 - obtaining prediction intervals from lrm() in rms library

Hi,

Apologies if this is a silly question -- I am just now learning how to use
some of the basic functions in
the rms library.

I have been using

     foo.dist <- datadist(foo.frame)
     options(datadist='foo.dist')
     lrm.model <- lrm(binary.outcome ~ rcs(contin.var,5)+categ.var, data
foo.frame, x=TRUE)
     lrm.predict <- predict(lrm.model, type = "fitted")

to obtain the predicted probabilities from a logistic regression model, but
now I need the associated 95%
prediction intervals associated with these predicted probabilities.  I've
read the examples in the ?lrm
help page, and from the information about "predict" from
http://cran.r-project.org/web/packages/rms/rms.pdf
I have tried

     predict(lrm.model, conf.int = 0.95, conf.type = c("individual"))

but I get the error message

     Error in predictrms(object, ..., type = type, se.fit = se.fit) :
        conf.type="individual" requires that fit be from ols
>From the same PDF, I have read the "predict.lrm" pages and was not
able to
figure out how to get
prediction intervals.



Many thanks in advance for some help,

    David

	[[alternative HTML version deleted]]

Depending on how you use the logistic regression this can be a silly
question.  Remember that the prediction interval is where you predict
new observations to be.  If you fit your logistic regression on data
that is 0 or 1 (or FALSE/TRUE, etc.) then predictions for new data
will be predictions of 0 or 1 (the confidence interval gives the
interval for the probability or proportion, so it makes sense).  Since
new values can only be 0 or 1 the only possible prediction intervals
are 0-0, 0-1, and 1-1, so they don't tend to be very interesting.

One case where the question is not silly is if you are modeling a
binomial with multiple possible events in a group, e.g. each subject
took a 100 question quiz and you modeled the proportion correct, now
you want to predict the number correct for a new subject on the 100
point quiz.  Now a prediction interval makes sense because while the
confidence interval is on the proportion correct, the prediction
interval would be on how many questions most subjects would get
correct taking into account all the uncertainty (both in the
proportion and in the binomial around that proportion).  However, this
interval is not often called for and is more complex to calculate than
the simple linear regression prediction interval, the combination of
those 2 facts is probably why it is not implemented for lrm objects.

The simplest way I know to create these prediction intervals is
actually a Bayesian approach (or Bayes like) where you generate a
probability from the posterior, then generate a random value from the
binomial using that probability, repeat a bunch of times, then take
the range of the middle 95% (or whatever percentage) as the prediction
interval.


On Fri, Mar 7, 2014 at 9:45 AM, David Paul <david.alan.paul1 at gmail.com>
wrote:
> Hi,
>
> Apologies if this is a silly question -- I am just now learning how to use
> some of the basic functions in
> the rms library.
>
> I have been using
>
>      foo.dist <- datadist(foo.frame)
>      options(datadist='foo.dist')
>      lrm.model <- lrm(binary.outcome ~ rcs(contin.var,5)+categ.var, data
> foo.frame, x=TRUE)
>      lrm.predict <- predict(lrm.model, type = "fitted")
>
> to obtain the predicted probabilities from a logistic regression model, but
> now I need the associated 95%
> prediction intervals associated with these predicted probabilities. 
I've
> read the examples in the ?lrm
> help page, and from the information about "predict" from
> http://cran.r-project.org/web/packages/rms/rms.pdf
> I have tried
>
>      predict(lrm.model, conf.int = 0.95, conf.type =
c("individual"))
>
> but I get the error message
>
>      Error in predictrms(object, ..., type = type, se.fit = se.fit) :
>         conf.type="individual" requires that fit be from ols
>
> >From the same PDF, I have read the "predict.lrm" pages and
was not able to
> figure out how to get
> prediction intervals.
>
>
>
> Many thanks in advance for some help,
>
>     David
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.


-- 
Gregory (Greg) L. Snow Ph.D.
538280 at gmail.com