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ggplot stat_summary (mean_cl_boot)

NMiller
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".

Many thanks for clearing this up for me,

Nate

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Re: ggplot stat_summary (mean_cl_boot)

David Winsemius

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

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Re: ggplot stat_summary(mean_cl_boot)

bbolker
David Winsemius <dwinsemius <at> comcast.net> writes:


> 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.

  In defense of the OP, this is a very confusing situation.
 mean_cl_boot is a ggplot2 function that wraps smean.cl.boot
from the Hmisc package: it's almost impossible to figure this
out from looking at the raw code of mean_cl_boot, although the
help page for ?mean_cl_boot does reference smean.cl.boot.
  ?smean.cl.boot (in Hmisc, so you'll need to have that package
loaded) has a B=1000 parameter for bootstrapping.
   I don't know if stat_summary(fun.data="mean_cl_boot",B=10000)
will work or not, but it would be worth a try (try setting B
to a small number and see if your CIs get very noisy, or set
it to a large number and see if your plot starts taking a lot
longer to compute ...)

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Re: ggplot stat_summary (mean_cl_boot)

David Winsemius
In reply to this post by David Winsemius

On Nov 9, 2011, at 4:10 PM, David Winsemius wrote:

>> mean_cl_boot


OK. Things do change. Hadley has written a wrapper for some of the  
Hmisc functions and you appear to be looking for smean.cl.boot()

(Note that Hadley's functions use "_"'s and Harrells use "."'s. And  
this could be found by

??"mean_cl_boot"   # at the R console

And then reading the help page for the wrap_hmisc function, the only  
entry that came up on my machine.


David Winsemius, MD
West Hartford, CT

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Re: ggplot stat_summary (mean_cl_boot)

NMiller
In reply to this post by David Winsemius
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(), 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. If you don't have
information regarding how it works (such as the default number of
resamplings) there is no need to respond.

Thanks for any assistance,
Nate



On Wed, Nov 9, 2011 at 1:10 PM, David Winsemius <[hidden email]>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
>
>

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Re: ggplot stat_summary (mean_cl_boot)

NMiller
Ok, I got it.

smean.cl.boot(x, conf.int=.95, B=1000, na.rm=TRUE, reps=FALSE)

Looks like its 1000.

Cool.
Thanks for the help,
Nate



On Wed, Nov 9, 2011 at 1:35 PM, Nathan Miller <[hidden email]>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(), 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. If you don't have
> information regarding how it works (such as the default number of
> resamplings) there is no need to respond.
>
> Thanks for any assistance,
> Nate
>
>
>
>
> On Wed, Nov 9, 2011 at 1:10 PM, David Winsemius <[hidden email]>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
>>
>>
>

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Re: ggplot stat_summary (mean_cl_boot)

David Winsemius
In reply to this post by NMiller

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 <[hidden email]
> > 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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Re: ggplot stat_summary(mean_cl_boot)

David Winsemius
In reply to this post by bbolker

On Nov 9, 2011, at 4:30 PM, Ben Bolker wrote:

> David Winsemius <dwinsemius <at> comcast.net> writes:
>
>
>> 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.
>
>  In defense of the OP, this is a very confusing situation.
> mean_cl_boot is a ggplot2 function

Another ggplot2 function with no help page, although it does bring up  
a help page with a link to smean.cl.boot

> that wraps smean.cl.boot
> from the Hmisc package: it's almost impossible to figure this
> out from looking at the raw code of mean_cl_boot, although the
> help page for ?mean_cl_boot does reference smean.cl.boot.

Right. And the code for mean_cl_boot threatens to pass any extra  
parameters. But I'm still scratching my head about how smean.cl.boot  
get called because it is never mentioned by name and then there is an  
ignore.dots parameter that apparently renegs on the promise to pass  
the B argument.


>  ?smean.cl.boot (in Hmisc, so you'll need to have that package
> loaded) has a B=1000 parameter for bootstrapping.

As I almost always do.

>   I don't know if stat_summary(fun.data="mean_cl_boot",B=10000)

Might need to be:

stat_summary(fun.data="mean_cl_boot",B=10000, ignore.dots=FALSE)


> will work or not, but it would be worth a try (try setting B
> to a small number and see if your CIs get very noisy, or set
> it to a large number and see if your plot starts taking a lot
> longer to compute ...)
>
> ______________________________________________
> [hidden email] 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.

David Winsemius, MD
West Hartford, CT

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Re: ggplot stat_summary (mean_cl_boot)

NMiller
In reply to this post by David Winsemius
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 <[hidden email]>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 <[hidden email]>
>> 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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