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