similar to: Fitting Data to a Noncentral Chi-Squared Distribution using MLE

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]]

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

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, R guys: I'm using L-BFGS-B method of optim for minimization problem. My function called besselI function which need non-negative parameter and the besselI will overflow if the parameter is too large. So I set the constraint box which is reasonable for my problem. But the point outside the box was test, and I got error. My program and the error follows. This program depends on CircStats

#bug 1: besselI() for nu<0 and expon.scaled=TRUE #tested with R-devel (2007-06-17 r41981) x <- 2.3 nu <- -0.4 print(paste(besselI(x, nu, TRUE), "=", exp(-x)*besselI(x, nu, FALSE))) #fix: #$ diff bessel_i_old.c bessel_i_new.c #57c57 #< bessel_k(x, -alpha, expo) * ((ize == 1)? 2. : 2.*exp(-x))/M_PI #--- #> bessel_k(x, -alpha, expo) * ((ize == 1)? 2. :

I am just trying to teach myself how to use the mle function in R because it is much better than what is provided in MATLAB. I am following tutorial material from the internet, however, it gives the following errors, does anybody know what is happening to cause such errors, or does anybody know any better tutorial material on this particular subject. >

Dear R-Group: Recently, I ran into a problem. I was using a function called "I.1", which evaluates the first-order modified Bessel function of the first kind, in the package "CircStats". This function is not vectorized, since it uses a couple of "if" conditions. However, when I called this function with a vector argument, I got no error/warning messages in

In order to fit a probability distribution proposed by Sichel [Journal of the Royal Statistical Society. Series A (General), Vol. 137, No. 1. (1974), pp. 25-34], I need a modified Bessel function of the 2nd kind. I notice that the base package of "R" only has modified Bessel functions of the 1st and 3rd kind. Does a modified Bessel function of the 2nd kind exist anywhere? Many

Hello Everybody in order to get some needed results out of my function i need to get my besselI function evaluated at some values which normally gave Inf or 0 (expon.scaled NAN) back. So I would like to increase the range in R from approxamittly 1e+320 to aabout 1e+500 or something like that. Is there any possibility or pacckage to do this easily? Thank You Sebastian Kaiser Institut for Statistics

The besselK() function knows about overflows/underflows internally; there is a constant xmax_BESS_K in src/nmath/bessel.h (and referred to only in bessel_k.c), equal to 705.342, which is checked if expon.scaled is FALSE. (The equivalent number for bessel_i.c is 709, defined as exparg_BESS in bessel.h.) However, besselK(x) silently returns +Inf if x>705.342. This behavior is reasonable for

Hi r-users,   I would like to solve for z values using newton iteration method.  I 'm not sure which part of the code is wrong since I'm not very good at programming but would like to learn.  There seem to be some output but what I expected is a vector of z values.  Thank you so much for any help given.   newton.inputsingle <- function(pars,n) {  runi    <- runif(974, min=0, max=1)

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 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 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,

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 R-users,   I wrote a code to evaluate double sum as follows:   ff2 <- function(bb,eta,z,k) { r <- length(z) for (i in 1:r) { sm1 <- sum((z[i]*bb/2)*(psigamma((0:k)+eta+1,deriv=0)/(factorial(0:k)*gamma((0:k)+eta+1))))  sm2 <- sum((besselI(z[i]*bb,eta)*log(z[i]*bb/2) - sm1)/besselI(z[i]*bb,eta))  sm2 } ff2(bb,eta,z,10)     but it gave me the following message:   >

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

Hi, I would like to know if there is a program written in R to get the CDF (cumulative distribution function) of a bivariate non-central chi-square distribution. Hope someone will reply. Thank you, Rossita M Yunus yunus@usq.edu.au This email (including any attached files) is confidentia...{{dropped:19}}

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,