sciplot question

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])

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  ?

Can you explain the meaning of B, and how to find a sensible value (if 
not the default is sufficient) ?

Best regards
Jarle Bj?rgeengen