similar to: Package for sample size calculation

Craig, Thanks for your follow-up note on using the asypow package. My problem was not only constructing the "constraints" vector but, for my particular situation (Poisson regression, two groups, sample sizes of (1081,3180), I get very different results using asypow package compared to my other (home grown) approaches. library(asypow) pois.mean<-c(0.0065,0.0003) info.pois <-

Hi all, I would like to estimate power and necessary sample size for a GLM with a response variable that has a poisson distribution. Do you have any suggestions for how I can do this in R? Thank you for your help. Sincerely, Craig -- Craig A. Faulhaber Department of Forest, Range, and Wildlife Sciences Utah State University 5230 Old Main Hill Logan, UT 84322 (435)797-3892

Dear R users, I am not asking questions specifically on R, but I know there are many statistical experts here in the R community, so here it goes my questions: Freedman (1982) propose an approximation of sample size/power calculation based on log-rank test using the formula below (This is what nQuery does): (Z(1-?/side)+Z(power))^2*(hazard.ratio+1)^2 N =

we have some initial data from our field sampling. From the means and variances, we see your sampling field is much heterogeneous (not uniform). We are looking for a "R" program for sampling size determination. Right now, we are not good enough to write a whole "R" program, so please help. [[alternative HTML version deleted]]

Hi, I'm try to compute the minimum sample size needed to have at least an 80% of power, with alpha=0.05. The problem is that empirical proportions are really small: 0.00154 in one case and 0.00234. These are the estimated failure proportion of two medical treatments. Thomas and Conlon (1992) suggested Fisher's exact test and proposed a computational method, which according to their table

Hi everyone. I need to do a sample size calculation for a one sample based on a CI interval of +- 5% of the proportion, we expect to have a rate of 0.07 based on similar studies. I have looked at the pwr-package as well as the power.prop.test function, but I can't see how to make either do what I need. Could anyone point me to a function, or explain how to do this will the functions in pwr?

Hi, I am trying to estimate the sample size needed for the comparison of two groups on a certain measurement, given some previous data at hand. I find that the data collected does not follow a normal distribution, so I would like to use a non-parametric option for sample size calculation. I found the pwr package but I don't think it has this option and on the internet found that

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). -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- Dr. Johannes W. Dietrich, M.D. --

I've got a 20 slide Powerpoint set up against the expected day someone says "Mitch, could you talk for a short while to the residents on how to use statistics?" It looks good at this end, but I'd love to get at least one more pair of eyes on it. Any volunteers? -- Due to the recession, requests for instant gratification will be deferred until arrears in scheduled

Dear R-user Suppose I have the following data y=c(2,1,5,8,11,3,1,7,50,21,33,7,60) x=data.frame(y) for(i in 4:nrow(x)) x[i,] =var(x[i-3:i-1,]) I'm trying to get a new variable with the variance of the 3 previous values (just an example) and with NA in the three first positions. I know that my for() is wrong but I'm not able to find my error. Any idea? Thanks, Marlene.

I'm getting a MacBook and I'd like to stick with OS X rather than convert it to Linux just yet. However, my main concern is having decent performance. What's my best option: *use the existing binary for R? *compile R fresh under OS X? * install Linux and run R under that? Does anyone have any recent experience they can share? Thanks! -- I can answer any question. "I

Dear R folks, Additional problem... bug.report() failed, so am sending it from another mailer. The file it generated is appended. Thanks Russ -- Russell V. Lenth -- Department of Statistics & Actuarial Science The University of Iowa -- Iowa City, IA 52242 USA Tel (319)335-0814 -- FAX (319)335-3017 mailto:Russell-Lenth@uiowa.edu - http://www.stat.uiowa.edu/~rlenth/ ===== R.bug.report

Dear Tomas,Thank you.Regarding the "unnumbered" arguments, i.e. sprintf('%f %f', 1, 2, 3). This was the case I wanted to report, here a warning can be very useful.Regarding the "numbered" arguments, that is, sprintf('%$1f %$3f', 1, 2, 3). Here, omission of an argument might be intended, for example, in an application with support for multiple languages.

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. Standard procedure would be a one-sample binomial test for the observed proportion, chi?(1 df) =

Dear R experts, I have conducted a power calculation in order to estimate the number of subjects needed to detect an effect size of d=0.28 (cohen's d) for a difference between two independent groups (alpha level should be 0.05 and the effect should be detected with 80% probability). The results from the code below indicates that I would need n=400 subjects (200 in each group). This is seems

Another way to avoid the problem is to not redefine variables that are arguments. E.g., > Su3 <- function(u=100, l=u, mu=0.53, sigma2=4.3^2, verbose) { if (verbose) { print(c(u, l, mu)) } uNormalized <- u/sqrt(sigma2) lNormalized <- l/sqrt(sigma2) muNormalized <- mu/sqrt(sigma2) c(uNormalized, lNormalized, muNormalized) } > Su3(verbose=TRUE)

Dear R developers, sessionInfo() below Please have a look at the following two versions of the same function: 1. Intended behavior: > Su1 = function(u=100, l=u, mu=0.53, sigma2=4.3^2) + { + print(c(u, l, mu)) # here, l is set to u?s value + u = u/sqrt(sigma2) + l = l/sqrt(sigma2) + mu = mu/sqrt(sigma2) + print(c(u, l, mu)) + } > > Su1() [1] 100.00 100.00 0.53 [1]

Hello, Is there any R function which does power calculation for unbalanced groups (n1 neq n2)? Since power.t.test has n Number of observations (per group). Many thanks, Samuel [[alternative HTML version deleted]]

Dear R developers, I am wondering if this should raise an error or a warning. > sprintf('%.f, %.f', 1, 2, 3) [1] "1, 2" I am aware that R has ?numbered? sprintf arguments (sprintf('%1$.f', ?), and in that case, omissing of specific arguments may be intended. But in the usual syntax, omission of an argument is probably a mistake. Thank you for your consideration.

I am trying to do power calculations for the proportional odds model using the asypow library. The code noncenta90b10<-asypow.noncent(theta.ha=a9010,info.mat=infomatrixa90b10,constraints=constrt) returns Error in max(..., na.rm = na.rm) : invalid "mode" of argument. the various arguments I've used are: a9010 [,1] [1,] -1.7357568 [2,] -0.1928619 specifying the