On Nov 9, 2011, at 4:35 PM, Nathan Miller wrote:
Sorry, I didn't realize I was being so obscure.
Within ggplot it is possible to use stat_summary() to generate
confidence intervals about a mean. One method for generating these
CI assumes normality. The other uses bootstrapping to generate the
CI. I am using the second method which requires code like this
stat_summary(fun.data="mean_cl_boot",
geom="errorbar",width=0.1,colour = "red")
I've added some extra flourishes to make them look like errorbars,
alter the width and specify color.
I would like some details regarding how this bootstrapped CI is
calculated. If I type "?mean_cl_boot" at the R command line I get a
minimal help file for "wrap_hmisc {ggplot2}" which is described
"wrap up a selection of Hmisc to make it easy to use with
stat_summary"
I did not mean to suggest that ggplot2 calls Hmisc when I run
stat_summary(),
Actually it does.
but simply that it appears that stat_summary() seems to have been
based upon a selection of Hmisc, hence I went looking in Hmisc to
try to find details regarding stat_summary(). I was unsuccessful in
this attempt.
I don't believe a great deal of debugging is necessary. I am simply
looking for details regarding how "mean_cl_boot" works.
It doesn't. That is not the right name.
If you don't have information regarding how it works (such as the
default number of resamplings) there is no need to respond.
Hadley's help files in ggplot2 are terse (or the links to outside
resources crash my R sessions) to the point of being too frustrating
for me to consider using that package, so I don't know if optional
parameters can be passed to the Hmisc functions. If they are, then
you should set reps=TRUE and then see what happens to the number of
reps from the returned object ... if the wrap_hmisc function does
happen to catch it.
> x <- rnorm(100)
> smean.cl.boot(x)
Mean Lower Upper
-0.0211511 -0.2013623 0.1469728
> smean.cl.boot(x, reps=TRUE)
Mean Lower Upper
-0.03465361 -0.21233213 0.15178655
attr(,"reps")
[1] 0.0283330508 -0.1250784237 0.0744640779 0.1310826601
-0.1373094536
[6] 0.0629291714 0.0145916070 -0.0860141221 0.0549134451
0.0732892908
snipped pages of intervening output.
[991] 0.1029922424 0.0613358597 -0.0645577851 -0.1664905503
-0.1249615180
[996] -0.0751783377 -0.0043747455 -0.1155948060 -0.0750075659
0.1244430930
I don't see where the number of reps is returned, but the B setting
defaults to 1000.
--
david.
Thanks for any assistance,
Nate
On Wed, Nov 9, 2011 at 1:10 PM, David Winsemius <dwinsem...@comcast.net
> wrote:
On Nov 9, 2011, at 2:59 PM, Nathan Miller wrote:
Hello,
This is a pretty simple question, but after spending quite a bit of
time
looking at "Hmisc" and using Google, I can't find the answer.
If I use stat_summary(fun.data="mean_cl_boot") in ggplot to generate
95%
confidence intervals, how many bootstrap iterations are preformed by
default? Can this be changed? I would at least like to be able to
report
the number of boot strap interations used to generate the CIs.
I haven't been able to find "mean_cl_boot" as a function itself or
something ressembling it in the Hmisc documentation, but it seems as
though
Hmisc is wrapped up in stat_summary() and is called to compute
"mean_cl_boot".
You seem really, really confused (and you offer very little in the
way of context to support debugging efforts). You are referring to
ggplot functions. As far as I know there is no connection between
the Hmisc and ggplot (or ggplot2) packages. Al things change, I
know, but Frank just completed switching over to Lattice a couple of
years ago.
--
David Winsemius, MD
West Hartford, CT
David Winsemius, MD
West Hartford, CT
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