similar to: anova power calculations

I have been attempting some basic power calculations using R and I am not getting the results I expect. I had a homework assignment in SAS, but I want to learn R as well, so I was attempting to reproduce my result. (No one else in the class is doing R, so there's no need to obsfucate the answer, the SAS code is what I get my grade for.) The code I am using is: # You assume that the

Dear List: The square of the noncentral t-statistic with noncentrality parameter \delta is a noncentral F with noncentrality parameter \lambda=\delta^2. So, t^2_{\nu,\delta} = F_{1,\nu,\lambda=\delta^2}. Consequently, it should follow that t^2_{1-\alpha/2,\nu,\delta} = f_{1-alpha,1,\vu,\lambda=\delta^2}. However, this is not what is happening with the following code. The central

Hello Everyone, I've been told that the memory allocation in R and S-Plus is different, with R being better at doing simulations for whatever reason(s). I was wondering if anyone could enlighten me on the differences in the memory usage/allocation (preferably in layman's terms!). The reason I ask this is because of a simulation I am trying to run. In it, I need to use the pt( )

Hi everyone. I have a problem that I have been unable to determine either the best way to proceed and why the methods I'm trying to use sometimes fail. I'm using the pf() function in an optimization function to find a noncentrality parameter that leads to a specific value at a specified quantile. My goal is to have a general function that returns the noncentrality parameter that

Hello all. It seems that the pf() function when used with noncentral parameters can behave badly at times. I've included some examples below, but what is happening is that with some combinations of df and ncp parameters, regardless of how large the quantile gets, the same probability value is returned. Upon first glance noncentral values greater than 200 may seem large, but they are in

Hello I could not find whether there is any R-function that implements the inverse of a noncentral Beta. Could someone out there tell me where I can find it? Or how to implement it? Many thanks Ed [[alternative HTML version deleted]]

Hello! I have a problem with integrate() in my function nctspa(). Integrate produces an error message "evaluation of function gave a result of wrong length". I don't know what that means. Could anyone suggest me what is wrong with my function? These are the examples of function calls that work OK: nctspa(a=1:10,n=5) nctspa(a=1:10, n=5, mu=2, theta=3, renorm=0) This does not work:

I have a data with 236 observations. After plotting the histogram, I found that it looks like non-central t distribution. I would like to get MLE for mu and df. I found an example to find MLE for gamma distribution from "fitting distributions with R": library(stats4) ## loading package stats4 ll<-function(lambda,alfa) {n<-200 x<-x.gam

My grasp of R and statistics are both seriously lacking, so if this question is completely naive, I apologize in advance. I've hunted for a couple hours on the internet and none of the methods I've found have produced the result I'm looking for. I'm currently a student in a Statistics class and we are learning the ANOVA. We had to do one by hand and then reproduce our work in SAS.

Hi there: I'd thought these two versions of noncentral t-distribution are essentially the same: > qqplot(rt(1000,df=20,ncp=3),qt(runif(1000),df=20,ncp=3)) But, the scales of the x-axis and the y-axis are quite different according to the QQ-plot. Did I make any mistakes somewhere? Thanks, Long ---------------------------------

I've been searching for an answer to this for a while but no joy. I have a simple 2-way ANOVA with an interaction. I'd like to determine the power of this test for each factor (factor A, factor B, and the A*B interaction). How can I do this in R? I used to do this with "proc Glmpower" in SAS, but I can find no analogue in R. [[alternative HTML version deleted]]

Hi, I have written out the log-likelihood function to fit some data I have (called ONES20) to the non-central chi-squared distribution. >library(stats4) >ll<-function(lambda,k){x<-ONES20; 25573*0.5*lambda-25573*log(2)-sum(-x/2)-log((x/lambda)^(0.25*k-0.5))-log(besselI(sqrt(lambda*x),0.5*k-1,expon.scaled=FALSE))} > est<-mle(minuslog=ll,start=list(lambda=0.05,k=0.006))

Dear Developers, The pnbeta function has been reviewed recently in the article of A. Baharev, S. Kem?ny, On the computation of the noncentral F and noncentral beta distribution, Statistics and Computing, 2008, in press. Preprint of the paper is available here: http://reliablecomputing.eu/publications.html and the article here http://dx.doi.org/10.1007/s11222-008-9061-3 I suggest increasing

Dear Developers, The pnbeta function has been reviewed recently in the article of A. Baharev, S. Kem=E9ny, On the computation of the noncentral F and noncentral beta distribution, Statistics and Computing, 2008, in press. Preprint of the paper is available here: http://reliablecomputing.eu/publications.html and the article here http://dx.doi.org/10.1007/s11222-008-9061-3 I suggest increasing

hello R-friends, I'm looking for a quantile function for the noncentral f-distribution in the area of equivalence hypotheses testing. Can somebody help me? Many thanks ----------------------------------------------------------------- Dipl. Inform. J. Hedderich Institut f?r Medizinische Informatik Phone : 0431 / 5973182 und Statistik im Klinikum an der CAU

The problem occurs when the sample odds ratio is Inf, such as in the following example. Given the fact that both upper bounds of the two 95% confidence intervals are Inf, I would have expected that the two lower bounds be equal, but they aren't. x <- matrix(c(9,4,0,2),2,2) x # [,1] [,2] #[1,] 9 0 #[2,] 4 2 rbind("two.sided.95CI"=fisher.test(x)$conf.int,

>From basic statistics principle,we know,given several i.i.d Gaussian RVs with zero or nonzero mean,the sum of square of them is a central or noncentral Chi-distributed RV.However if these Gaussian RVs have different variances,what does the sum of square of them obey? Thanks in advance.

Hi, Actually I am facing a similar problem. I would like to fit both an ordinary (symmetric) and a non-central t distribution to my (one-dimensional) data (quite some values.. > 1 mio.). For the symmetric one, fitdistr or funInfoFun (using fitdistr) from the qAnalyst package should do the job, and for the non-central one.. am I right to use gamlss(x ~ 1, family=GT()) ? Anyway, I am a little

I am building a package for various noncentral hypergeometric distributions. I want to include some heavy mathematical formulas. It appears that the build and INSTALL commands produce only .chm files and not .pdf files from my .Rd files under Windows. This means that it cannot show complicated mathematical formulas. The solution might be to include a .pdf file as a vignette. I tried that,

I am building a package for various noncentral hypergeometric distributions. I want to include some heavy mathematical formulas. It appears that the build and INSTALL commands produce only .chm files and not .pdf files from my .Rd files under Windows. This means that it cannot show complicated mathematical formulas. The solution might be to include a .pdf file as a vignette. I tried that,