similar to: looking for a CDF of bivariate noncentral Chisquare

Hi R Help Team I recently updated my R installation to R 3.4.3 and updated to later version of R Studio and I found that the package semPLS will not load even though installed and it seems to be failing on loading package lattice Getting the following error message: library(semPLS) Loading required package: lattice Error: package or namespace load failed for 'lattice': .onLoad failed in

Hi all I have a plot with lines, one specified as (say) lty=1, using standard line types, and another as (say) my own spec: lty="51". I can't get legend to display both. Toy example: > plot(1~1) > legend("topright", lty=c("51",1), legend=c("My own","Standard")) Error in segments(x1, y1, x2, y2, ...) : invalid line type: must be

Hi all I'm using Sweave and LaTeX, and love how they interact. But here's a different interaction I'm not sure how to achieve; I hope someone can help. I use a simple example, of course, to demonstrate. Suppose in my LaTeX document I have this: Here is a linear equation: \begin{equation} y = -1 + 3 x \label{EQ:example} \end{equation} This appears in the final product

Hi all After posting what follows, Duncan Murdoch suggested perhaps a bug in formatC, or an error on documentation. Any comments? In particular, bug, error or not, any ideas about how I can consistently get two significant figures to print? P. ---------- Original Message ---------- Hi all I am not a C programmer, but I am trying to understand formatC to get consistent printing of

Hi all I am not a C programmer, but I am trying to understand formatC to get consistent printing of reals to a given number of significant digits. Can someone please explain this to me? These first three give what I expect on reading ?formatC: > formatC(0.0059999, digits=2,format="fg",flag="#") [1] "0.0060" > formatC(0.59999,

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

From: Martin Maechler <maechler@stat.math.ethz.ch> To: Kurt.Hornik@ci.tuwien.ac.at CC: R-devel@stat.math.ethz.ch Subject: Re: R-alpha: class for chisquare tests; Thoughts on print & summary Kurt, I think we hopefully are coming to an agreement that 1) your ctest collection should make its way into 'R core' ((and you are the one who can make it happen now ..)) By all

Hello all you helpful people out there! I am stil R Beginner using R 2.5.1 on a Apple Power Book G4 with Mac OS X 10.4.10 . I have two tabels like this: MalTabChi X1 X4 X6 X8 X10 X14 X21 X24 X29 X38 X43 X50 X67 X76 X78 X80 X82 X84 Anth_cap 1 1 1 1 6 5 1 45 12 4 12 6 56 5 2 4 1 1 Anth_crin 12 1 1 2 76 5 1

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]]

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

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 ---------------------------------

Full_Name: Long Qu Version: 2.3.1 OS: Windows XP Submission from: (NULL) (64.113.93.235) The QQ-plot of two versions of simulating noncentral F-distributed random numbers has quite different scales: > qqplot(rf(1000,2,15,3),qf(runif(1000),2,15,3)) The rf() function reads: > rf function (n, df1, df2, ncp = 0) { if (ncp == 0) .Internal(rf(n, df1, df2)) else rchisq(n, df1,

In this years biostat teaching I will include Rcommander (it indeed simplifies syntax problems that makes students frequently miss the core statistical problems). But I could not find how to make a simple chisquare comparison between observed frequencies and expected frequencies (eg in genetics where you expect phenotypic frequencies corresponding to 3:1 in standard dominant/recessif

I sort of asked this before, but perhaps not explicitly enough. In my ctest collection, there are several chisquare-based tests. For some of them, it may be useful to also return information on expected (and observed) counts. The question is, how should this be done. Of course, there is no problem adding the corresponding components to the list returned by the functions. However, as these are

For those who are interested, I have made available a R function for noncentral hypergeometric distribution at http://www.geocities.com/jg_liao/software/Hypergeometric/hypergeometric_in_R.txt The paper that describes the algorithm will appear in The American Statistician. The function does not run on S-plus as the R's scoping rule is used. Here is how the function can be used: > n1

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))

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

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

Does anybody have any experience fitting data to the non-central chi-squared or chi-squared distribution? I am trying to fit some data to this distribution but there is error after error. audaces fortuna iuvat --------------------------------- [[alternative HTML version deleted]]

Full_Name: Mike Meredith Version: 2.3.0 OS: WinXP SP2 Submission from: (NULL) (210.195.228.29) Using dt() with a non-centrality parameter and near-zero values for 'x' results in erratic output. Try this: tst <- c(1e-12, 1e-13, 1e-14, 1e-15, 1e-16, 1e-17, 0) dt(tst,16,1) I get: 0.2381019 0.2385462 0.2296557 0.1851817 0.6288373 3.8163916 (!!) 0.2382217 The 0.238 values are okay,