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]
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
Best regards
Jarle Bjørgeengen
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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