sciplot question

Jarle Bj?rgeengen 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 Bj?rgeengen wrote:

On May 24, 2009, at 4:42 , Frank E Harrell Jr wrote:

Jarle Bj?rgeengen wrote:

On May 24, 2009, at 3:34 , Frank E Harrell Jr wrote:

Jarle Bj?rgeengen 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 Bj?rgeengen
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]

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.

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