[R] sciplot question
Jarle Bjørgeengen
jarle at bjorgeengen.net
Tue May 26 15:34:13 CEST 2009
On May 26, 2009, at 3:02 , Frank E Harrell Jr wrote:
> Jarle Bjørgeengen wrote:
>> On May 26, 2009, at 4:37 , Frank E Harrell Jr wrote:
>>> Manuel Morales wrote:
>>>> On Mon, 2009-05-25 at 06:22 -0500, Frank E Harrell Jr wrote:
>>>>> Jarle Bjørgeengen wrote:
>>>>>> On May 24, 2009, at 4:42 , Frank E Harrell Jr wrote:
>>>>>>
>>>>>>> Jarle Bjørgeengen wrote:
>>>>>>>> On May 24, 2009, at 3:34 , Frank E Harrell Jr wrote:
>>>>>>>>> Jarle Bjørgeengen wrote:
>>>>>>>>>> Great,
>>>>>>>>>> thanks Manuel.
>>>>>>>>>> Just for curiosity, any particular reason you chose
>>>>>>>>>> standard error , and not confidence interval as the default
>>>>>>>>>> (the naming of the plotting functions associates closer to
>>>>>>>>>> the confidence interval .... ) error indication .
>>>>>>>>>> - Jarle Bjørgeengen
>>>>>>>>>> On May 24, 2009, at 3:02 , Manuel Morales wrote:
>>>>>>>>>>> You define your own function for the confidence intervals.
>>>>>>>>>>> The function
>>>>>>>>>>> needs to return the two values representing the upper and
>>>>>>>>>>> lower CI
>>>>>>>>>>> values. So:
>>>>>>>>>>>
>>>>>>>>>>> qt.fun <- function(x) qt(p=.975,df=length(x)-1)*sd(x)/
>>>>>>>>>>> sqrt(length(x))
>>>>>>>>>>> my.ci <- function(x) c(mean(x)-qt.fun(x), mean(x)+qt.fun(x))
>>>>>>>>> Minor improvement: mean(x) + qt.fun(x)*c(-1,1) but in
>>>>>>>>> general confidence limits should be asymmetric (a la
>>>>>>>>> bootstrap).
>>>>>>>> Thanks,
>>>>>>>> if the date is normally distributed , symmetric confidence
>>>>>>>> interval should be ok , right ?
>>>>>>> Yes; I do see a normal distribution about once every 10 years.
>>>>>> Is it not true that the students-T (qt(... and so on)
>>>>>> confidence intervals is quite robust against non-normality too ?
>>>>>>
>>>>>> A teacher told me that, the students-T symmetric confidence
>>>>>> intervals will give a adequate picture of the variability of
>>>>>> the data in this particular case.
>>>>> Incorrect. Try running some simulations on highly skewed data.
>>>>> You will find situations where the confidence coverage is not
>>>>> very close of the stated level (e.g., 0.95) and more situations
>>>>> where the overall coverage is 0.95 because one tail area is near
>>>>> 0 and the other is near 0.05.
>>>>>
>>>>> The larger the sample size, the more skewness has to be present
>>>>> to cause this problem.
>>>> OK - I'm convinced. It turns out that the first change I made to
>>>> sciplot
>>>> was to allow for asymmetric error bars. Is there an easy way (i.e.,
>>>> existing package) to bootstrap confidence intervals in R. If so,
>>>> I'll
>>>> try to incorporate this as an option in sciplot.
>>>
>>> library(Hmisc)
>>> ?smean.cl.boot
>> H(arrel)misc :-)
>> Thanks for valuable input Frank.
>> This seems to work fine. (slightly more time consuming , but what
>> do we have CPU power for )
>> library(Hmisc)
>> library(sciplot)
>> my.ci <- function(x) c(smean.cl.boot(x)[2],smean.cl.boot(x)[3])
>
> Don't double the executing time by running it twice! And this way
> you might possibly get an upper confidence interval that is lower
> than the lower one. Do function(x) smean.cl.boot(x)[-1]
>
D'oh
>> lineplot
>> .CI
>> (V1
>> ,V2
>> ,data
>> =
>> d
>> ,col
>> =
>> c
>> (4
>> ),err
>> .col
>> =
>> c
>> (1
>> ),err
>> .width
>> =
>> 0.02
>> ,legend
>> =FALSE,xlab="Timeofday",ylab="IOPS",ci.fun=my.ci,cex=0.5,lwd=0.7)
>> Have I understood you correct in that this is a more accurate way
>> of visualizing variability in any dataset , than the students T
>> confidence intervals, because it does not assume normality ?
>
> Yes but instead of saying variability (which quantiles are good at)
> we are talking about the precision of the mean.
>
Right.
>> Can you explain the meaning of B, and how to find a sensible value
>> (if not the default is sufficient) ?
>
> For most purposes the default is sufficient. There are great books
> and papers on the bootstrap for more info, including improved
> variations on the simple bootstrap percentile confidence interval
> used here.
>
> Frank
Once again, thanks.
Best regards
- Jarle Bjørgeengen
More information about the R-help
mailing list