R help - May 2009 - sciplot question

Hi,

I would like to have lineplot.CI and barplot.CI to actually plot  
confidence intervals , instead of standard error.

I understand I have to use the ci.fun option, but I'm not quite sure  
how.

Like this :

 >  qt(0.975,df=n-1)*s/sqrt(n)

but how can I apply it to visualize the length of the student's T  
confidence intervals rather than the stdandard error of the plotted  
means ?

-- 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Best regards                                       .~.
Jarle Bj?rgeengen                                  /V\
Mob: +47 9155 7978                                // \\
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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))

lineplot.CI(x.factor = dose, response = len, data = ToothGrowth,
    ci.fun=my.ci)

Manuel

On Fri, 2009-05-22 at 18:38 +0200, Jarle Bj?rgeengen
wrote:
> Hi,
> 
> I would like to have lineplot.CI and barplot.CI to actually plot  
> confidence intervals , instead of standard error.
> 
> I understand I have to use the ci.fun option, but I'm not quite sure  
> how.
> 
> Like this :
> 
>  >  qt(0.975,df=n-1)*s/sqrt(n)
> 
> but how can I apply it to visualize the length of the student's T  
> confidence intervals rather than the stdandard error of the plotted  
> means ?
> 
-- 
http://mutualism.williams.edu

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.

Frank
> 
> Best rgds
> Jarle Bj?rgeengen
> 
> 

-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University