similar to: MLE for noncentral t distribution

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

Dear Ingmar & Dave, Thanks a lot for your help and sorry for the late reply. Finally, I've found a way to separate the mixture of distributions (empirically). But the gamlss package looks great, I'm sure it will help me during my further studies. Kind regards, Susanne On 15 Jun 2009, at 20:09, Ingmar Visser wrote: > Dear Susanne & Dave, > > The gamlss package family

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 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

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,

Dear all, I am attempting to model some one-dimensional data using a mixture model of non-central Student's t distributions. However, I haven't been able to find any R package that provides this functionality. Could there be a way to "manipulate" the EM algorithms from the mixdist or mixtools package to fit the model, or do you have any other suggestions? If anyone could help

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]]

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

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

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

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

I sent a message a couple days ago about doing calculations for power of the ANOVA. Several people got back to me very quickly which I really appreciated. I'm working now on a similar problem, but instead of a balanced ANOVA, I have an unbalanced one. The first part of the question was: You assume that the within-population standard deviations all equal 9. You set the Type 1 error rate at á

Dear all, I am trying to read in big amounts of data with scan. It's only one variable, numeric values, separated by tabs,.. and it's many of them. So I was thinking that I could use the skip option and read in 100000 values at a time - but skip doesn't work, probably because I don't have line breaks in the txt file. So any value specified for skip makes the scan function jump to

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]]

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,

Dear all, I tried help.search("karber") and RSiteSearch("karber") and RSiteSearch("*karber*") to find whether the (trimmed) Spearman-Karber method for LD50 evaluation in toxicity data (e.g. according to Hamilton 1977) has been implemented in R. Or does this method feature under a different name? Of course logit and probit are doable, but Spearman-Karber seems to

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( )

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. >