For all those that are interested.
To adjust the number of reps in the stat_summary() "mean_cl_boot" function
simply specify "B" to the number of bootstrap resamples. I set B to 2000
resamplings below.
stat_summary(fun.data="mean_cl_boot", geom="errorbar",width=0.1,colour =
"red", B=2000 )
If you run "mean_cl_boot" within stat_summary() and ggplot setting "reps=T"
does not appear to return a vector of the resampled means as an attribute
that I could locate anywhere.
However, you can run "smean.cl.boot" code outside of ggplot.
x<-smean.cl.boot(OsmData$Mean, B=2000, reps=T)
attr(x,"reps")
Thus, outside of ggplot you can use reps=T to check the resampling is
proceeding as you expect, before adding it to the ggplot code. I did some
checks setting B=1 and B=5 as well as large numbers both inside and
outside of the ggplot code to assure myself that my adjustments to B within
stat_summary() within ggplot were actually doing what I thought.
Finally, despite the fact that the Hmisc function is called
"smean.cl.boot", as David points out, within ggplot and stat_summary you
must use "mean_cl_boot" without the "s" before "mean".
Within ggplot "mean_cl_boot" is the correct notation and it does work.
I really like ggplot, but can agree that it isn't always clear how to get
from point A to point B. My hope in writing this out is that someone else
might start their own exploration of these issues a little further down the
road than I found myself when I started looking into this.
Thanks,
Nate
On Wed, Nov 9, 2011 at 1:46 PM, David Winsemius <dwinsem...@comcast.net>wrote:
>
> 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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