Generally you should do the power analysis before collecting any data.
Since you have results it looks like you already have the data
collected.
But if you want to compute the power for a future study, one option is
to use simulation.
1. decide what the data will look like
2. decide how you will
Hello,
I am running a non parametric repeated measures experiment with
Friedman’s test:
Friedman rank sum test
data: glikozi and week and subject
Friedman chi-squared = 18.538, df = 3, p-value = 0.0003405
How could I run a power analysis for this test in R?
Thank you!
--
George
Hi, this is a statistical question rather than a pure R question. I have got
many help from R mailing list in the past, so would like to try here and
appreciate any input:
I conducted Mantel-Haenszel test to show that the performance of a diagnostic
test did not show heterogeneity among 4
On Nov 12, 2013, at 6:10 PM, array chip wrote:
Hi, this is a statistical question rather than a pure R question. I have got
many help from R mailing list in the past, so would like to try here and
appreciate any input:
I conducted Mantel-Haenszel test to show that the performance of a
thoughts...
Thanks
John
From: Christopher W. Ryan cr...@binghamton.edu
Sent: Tuesday, November 12, 2013 6:53 PM
Subject: Re: [R] power analysis is applicable or not
John--
Well, my simple-minded way of thinking about these issues goes something
like this:
You
.
Please share your thoughts...
Thanks
John
From: Christopher W. Ryan cr...@binghamton.edu
To: array chip arrayprof...@yahoo.com
Sent: Tuesday, November 12, 2013 6:53 PM
Subject: Re: [R] power analysis is applicable or not
John--
Well, my simple-minded way of thinking about these issues
Marc gave the referencer for Schoenfeld's article. It's actually quite
simple.
Sample size for a Cox model has two parts:
1. Easy part: how many deaths to I need
d = (za + zb)^2 / [var(x) * coef^2]
za = cutoff for your alpah, usually 1.96 (.05 two-sided)
zb = cutoff for
Hi Terry, Greg, and Marc,
Thanks for your advice about this. I think I have a pretty good starting point
now for the analysis.
Appreciate your help.
Paul
--- On Wed, 7/18/12, Terry Therneau thern...@mayo.edu wrote:
From: Terry Therneau thern...@mayo.edu
Subject: Re: [R] Power analysis
, Greg Snow 538...@gmail.com* wrote:
From: Greg Snow 538...@gmail.com
Subject: Re: [R] Power analysis for Cox regression with a time-varying
covariate
To: Paul Miller pjmiller...@yahoo.com
Cc: r-help@r-project.org
Received: Friday, July 13, 2012, 3:29 PM
For something like this the best
...@gmail.com* wrote:
From: Greg Snow 538...@gmail.com
Subject: Re: [R] Power analysis for Cox regression with a time-varying
covariate
To: Paul Miller pjmiller...@yahoo.com
Cc: r-help@r-project.org
Received: Friday, July 13, 2012, 3:29 PM
For something like this the best (and possibly only
start out using the steps
you've listed and see where that takes me.
Paul
--- On Fri, 7/13/12, Greg Snow 538...@gmail.com wrote:
From: Greg Snow 538...@gmail.com
Subject: Re: [R] Power analysis for Cox regression with a time-varying covariate
To: Paul Miller pjmiller...@yahoo.com
Cc: r-help
Hello All,
Does anyone know where I can find information about how to do a power analysis
for Cox regression with a time-varying covariate using R or some other readily
available software? I've done some searching online but haven't found anything.
Thanks,
Paul
For something like this the best (and possibly only reasonable) option
is to use simulation. I have posted on the general steps for using
simulation for power studies in this list and elsewhere before, but
probably never with coxph.
The general steps still hold, but the complicated part here will
Is there a library that provides power calculation and sample size
estimation for nonlinear regression?
The task is easy for linear regression with the pwr package, but I
can't find a method for nonlinear regression (estimated with the nls
package).
-- -- -- -- -- -- -- -- -- -- -- -- --
May I suggest you consult your local statistician. For reasons that (s)he
can answer, your request makes little sense.
Hint: Nonlinear regression is much different than linear regression: The
design matrix -- and hence the variance of estimators -- is a function of
the parameters being estimated.
Verzonden: maandag 5 september 2011 16:17
Aan: r-help@r-project.org
Onderwerp: [R] Power analysis in hierarchical models
Dear All
I am attempting some power analyses, based on simulated data.
My experimental set up is thus:
Bleach: main effect, three levels (control, med, high), Fixed.
Temp
Dear All
I am attempting some power analyses, based on simulated data.
My experimental set up is thus:
Bleach: main effect, three levels (control, med, high), Fixed.
Temp: main effect, two levels (cold, hot), Fixed.
Main effect interactions, six levels (fixed)
For each main-effect combination I
Inter ocular data
Quite amusing :)
Thank you for the help. For some reason I was thinking that I could get the
n values for the combined test, but that doesn't make sense as there could
be an infinite number of combinations of n values.
Thanks again for the replies.
--
View this message in
On Apr 19, 2011, at 8:43 AM, Schatzi wrote:
Inter ocular data
Quite amusing :)
Thank you for the help. For some reason I was thinking that I could get the
n values for the combined test, but that doesn't make sense as there could
be an infinite number of combinations of n values.
Thanks
I am trying to do a power analysis to get the number of replicas per
treatment.
If I try to get the power it works just fine:
setn=c(2,3)
sdx=c(1.19,4.35)
power.t.test(n = setn, delta = 13.5, sd = sdx, sig.level = 0.05,power =
NULL)
If I go the other way to obtain the n I have problems.
First, note that you are doing two separate power calculations,
one with n=2 and sd = 1.19, the other with n=3 and sd = 4.35.
I will assume this was on purpose. Now...
power.t.test(n = 2, delta = 13.5, sd = 1.19, sig.level = 0.05)
Two-sample t test power calculation
n = 2
It seems to me, with deltas this large (relative to the SD), that a
significance test is a moot point!
David Cross
d.cr...@tcu.edu
www.davidcross.us
On Apr 18, 2011, at 5:14 PM, Albyn Jones wrote:
First, note that you are doing two separate power calculations,
one with n=2 and sd = 1.19,
Yes, Richard Savage used to call this inter ocular data;
the answer should leap up and strike you right between the eyes...
albyn
On Mon, Apr 18, 2011 at 05:23:05PM -0500, David Cross wrote:
It seems to me, with deltas this large (relative to the SD), that a
significance test is a moot point!
Hi:
Just to add to the discussion, see the following article by Russell Lenth on
the subject:
http://www.stat.uiowa.edu/techrep/tr378.pdf
Dennis
On Thu, Sep 2, 2010 at 3:59 PM, C Peng peng.cheng...@hotmail.com wrote:
Agree with Greg's point. In fact it does not make logical sense in many
Lewis G. Dean wrote:
post-hoc power analysis on a Wilcoxon test.
There is a (somewhat dated) list of why-not papers in
http://www.childrens-mercy.org/stats/size/posthoc.asp
Dieter
--
View this message in context:
http://r.789695.n4.nabble.com/Power-analysis-tp2524729p2525333.html
I am aware this is fairly simple, but is currently driving me mad! Could
someone help me out with conducting a post-hoc power analysis on a Wilcoxon
test. I am being driven slightly mad by some conflicting advice!
Thanks in advance,
Lewis
[[alternative HTML version deleted]]
-project.org
Subject: [R] Power analysis
I am aware this is fairly simple, but is currently driving me mad!
Could
someone help me out with conducting a post-hoc power analysis on a
Wilcoxon
test. I am being driven slightly mad by some conflicting advice!
Thanks in advance,
Lewis
Agree with Greg's point. In fact it does not make logical sense in many
cases. Similar to the use of the statistically unreliable reliability
measure Cronbach's alpha in some non-statistical fields.
--
View this message in context:
Dear R-help list,
Does anyone have a function that I could use to determine power for 2 way
Anova??
an A x B repeated measures study,power is 0.95, I'd like to draw separate lines
for three different combinations of A and B:
(2,2), (2,5), (2,8).
Thanks a lot.
Tammy
Hi all,
Is there any way we can to power analysis for prop trend test? Many thanks!
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and
Breslow Day has a nice three page discussion in volume 2 of their
Statistical Methods in Cancer Research. See pages 285-7. Most of the
gain in power comes from the decrease in degrees of freedom and only
if the trend is approximately linear. Alternatives that are quadratic
are not well
Hi Rick,
I understand the authors' point and also agree that post-hoc power
analysis is basically not telling me anything more than the p-value and
initial statistic for the test I am interested in computing power for.
Beta is a simple function of alpha, p, and the statistic.
On Wed, 2009-01-28 at 21:21 +0100, Stephan Kolassa wrote:
Hi Adam,
first: I really don't know much about MANOVA, so I sadly can't help you
without learning about it an Pillai's V... which I would be glad to do,
but I really don't have the time right now. Sorry!
Second: you seem to be
Thanks for the response, Stephan.
Really, I am trying to say, My result is insignificant, my effect sizes are
tiny, you may want to consider the possibility that there really are no
meaningful differences. Computing post-hoc power makes a bit stronger of a
claim in this setting.
My real goal in
Hi Adam,
first: I really don't know much about MANOVA, so I sadly can't help you
without learning about it an Pillai's V... which I would be glad to do,
but I really don't have the time right now. Sorry!
Second: you seem to be doing a kind of post-hoc power analysis, my
result isn't
Hello,
I have searched and failed for a program or script or method to
conduct a power analysis for a MANOVA. My interest is a fairly simple case
of 5 dependent variables and a single two-level categorical predictor
(though the categories aren't balanced).
If anybody happens to
http://www.amazon.com/Statistical-Power-Analysis-Behavioral-Sciences/dp/0805802835
Cohen's book was in fact the basis for the pwr package at CRAN.
And it does have a MANOVA power analysis, which was left out of the
pwr package.
On Mon, Jan 26, 2009 at 4:12 PM, Adam D. I. Kramer
Hi Adam,
My (and, judging from previous traffic on R-help about power analyses,
also some other people's) preferred approach is to simply simulate an
effect size you would like to detect a couple of thousand times, run
your proposed analysis and look how often you get significance. In your
On Mon, 26 Jan 2009, Stephan Kolassa wrote:
My (and, judging from previous traffic on R-help about power analyses,
also some other people's) preferred approach is to simply simulate an
effect size you would like to detect a couple of thousand times, run your
proposed analysis and look how
If you know what a 'general linear hypothesis test' is see
http://cran.r-project.org/src/contrib/Archive/hpower/hpower_0.1-0.tar.gz
HTH,
Chuck
On Mon, 26 Jan 2009, Adam D. I. Kramer wrote:
On Mon, 26 Jan 2009, Stephan Kolassa wrote:
My (and, judging from previous traffic on
On Mon, 26 Jan 2009, Charles C. Berry wrote:
If you know what a 'general linear hypothesis test' is see
http://cran.r-project.org/src/contrib/Archive/hpower/hpower_0.1-0.tar.gz
I do, and am quite interested, however this package will not install on R
2.8.1: First, it said that
On Mon, 26 Jan 2009, Adam D. I. Kramer wrote:
On Mon, 26 Jan 2009, Charles C. Berry wrote:
If you know what a 'general linear hypothesis test' is see
http://cran.r-project.org/src/contrib/Archive/hpower/hpower_0.1-0.tar.gz
I do, and am quite interested, however this package will not
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