g
> Subject: [R] statistical question
>
> Dear List!
>
> I want to compare medians of non normal distributed data. Is it
> possible and usefull to calculate 95% confidence intervals for
> medians? And if so - how can this be achieved in R?
>
> Thanks a lot!
> Anna
&g
The default rank test in the quantreg package would look like this
summary(rq(y ~ d, tau = .5))
where d is a factor variable indicating which sample the elements of
y belonged to. Summary returns a confidence interval for the coef
of the factor variable -- if this interval excludes zero at the c
Dear List!
I want to compare medians of non normal distributed data. Is it
possible and usefull to calculate 95% confidence intervals for
medians? And if so - how can this be achieved in R?
Thanks a lot!
Anna
--
Der Inhalt dieser E-Mail ist vertraulich. Sollte Ihnen die E-Mail
irrtümlich zu
On Dec 10, 2009, at 10:59 AM, Santosh wrote:
Dear R/Statistics-gurus!
I tried to find answer to my hypothetical question and in vain.
Sorry, I
don't have a dataset that fits into this hypothetical question and
pardon me
if my explanations/use of statistical terms are not accurate.
It doe
Dear R/Statistics-gurus!
I tried to find answer to my hypothetical question and in vain. Sorry, I
don't have a dataset that fits into this hypothetical question and pardon me
if my explanations/use of statistical terms are not accurate.
It does sound a weird question, but I want to rule out that
[mailto:r-help-boun...@r-project.org] On
Behalf Of Denis Aydin
Sent: Wednesday, August 26, 2009 10:18 AM
To: r-help@r-project.org
Subject: [R] Statistical question about logistic regression simulation
Hi R help list
I'm simulating logistic regression data with a specified odds ratio
(beta) and
On 26-Aug-09 14:17:40, Denis Aydin wrote:
> Hi R help list
> I'm simulating logistic regression data with a specified odds ratio
> (beta) and have a problem/unexpected behaviour that occurs.
>
> The datasets includes a lognormal exposure and diseased and healthy
> subjects.
>
> Here is my loop:
Hi R help list
I'm simulating logistic regression data with a specified odds ratio
(beta) and have a problem/unexpected behaviour that occurs.
The datasets includes a lognormal exposure and diseased and healthy
subjects.
Here is my loop:
ors <- vector()
for(i in 1:200){
# First, I creat
hi,
at first; thanks for the help on getting confidence intervals in R.
now I have a pure statistical question.
I hope you don't mind if I ask ...
I have an expectation of how large my beta-weight in a regression
should be - so I have an "ideal" or expected regression line.
Now the real
The bootstrap that Greg Snow suggested is probably the best approach, but
it is possible to estimate the variance of the proportion.
The total T number of yes reponses is the sum of twenty totals for blocks,
and these are independent, so the variance of Y is 20 times the variance
of these tw
Message-
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
> project.org] On Behalf Of Matthias Gondan
> Sent: Tuesday, November 25, 2008 7:53 AM
> To: [EMAIL PROTECTED]
> Subject: [R] Statistical question: one-sample binomial test for
> clustered data
>
> Dear list,
>
Dear list,
I hope the topic is of sufficient interest, because it is not
R-related. I have N=100 yes/no-responses from a psychophysics
paradigm (say Y Yes and 100-Y No-Responses). I want to see
whether these yes-no-responses are in line with a model
predicting a certain amount p of yes-responses.
On Tue, 23 Sep 2008, Ted Byers wrote:
Thanks Timur
While assessing whether or not the best option would be a normal
distribution (it won't be, the data in this case LOOKS more poisson, or if I
explude the first week of results, a negative exponential; and in my other
case, cauchy is more likel
Thanks Timur
While assessing whether or not the best option would be a normal
distribution (it won't be, the data in this case LOOKS more poisson, or if I
explude the first week of results, a negative exponential; and in my other
case, cauchy is more likely), I really need a test that can be appl
If one of the goals is the normality test, then there may be better
alternatives to the Kolmogorov-Smirnov test.
See an explanation on:
http://graphpad.com/FAQ/viewfaq.cfm?faq=959
The R implementation:
?shapiro.test
A casual search also turned this up:
http://tolstoy.newcastle.edu.au/R/help/04/09
I am in a situation where I have to fit a distrution, such as cauchy or
normal, to an empirical dataset. Well and good, that is easy.
But I wanted to assess just how good the fit is, using ks.test.
I am concerned about the following note in the docs (about the example
provided): "Note that the
16 matches
Mail list logo
