similar to: Modelling a skew-normal distribution using glm/ mgcv

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Caio Lucidius > Naberezny Azevedo > Sent: 01 September 2005 12:09 > To: Help mailing list - R > Subject: [R] Multivariate Skew Normal distribution > > > Hi all, > > Could anyone tell me if there is any package (or function)

AA> In 2008, I have spotted some errors in a package, one which is AA> likely to have many users (I am not one myself). The more serious AA> errors are in the documentation, since they lead to a completely AA> distorted interpretation of the outcome; in addition, there is (at AA> least) one programming error which produces some wrong AA> computations. A few weeks later, the

Dear R-help list members, I have created a package 'mnormt' with facilities for the multivariate normal and t distributions. The core part is simply an interface to Fortran routines by Alan Genz for computing the integral of two densities over rectangular regions, using an adaptive integration method. Other R functions compute densities and generate random numbers. The starting

Hi R-users,  I have a data from multi skew t and would like to transform each of the data to uniform data.  I tried using 'pmst' but only got one output:   > rr1 <- as.vector(r1);rr1  [1]  0.7207582  5.2250906  1.7422237  0.5677233  0.7473555 -0.6020626 -2.1947872 -1.1128313 -0.6587316 -1.1409261     > pmst(rr1, xi=rep(0,10), Omega=diag(10), alpha=rep(1,10), df=5) [1] 3.676525e-09

Hello everybody, I need your help about the package SN and the skew student distribution. Il will be very grateful if I have the solution. I construct a stochastic model with a white noise not gaussian but following a skew student distribution. I fit the noise on monthly data to obtain the four parameters. The question is : how to annualize the parameters to use my model for simulate daily data

Dear all, I am calculating the bivariate skew normal cdf in "sn" package using "pmsn" function. Although it is quite convenient ( thanks to prof. Azzalini) but it seems to be slow. For example, it takes about 1 minute in calculation of 100k of such cdf values. I am thinking to write a c++ code for this although not very familiar with it. Any other idea?    Thanks in advance,

hello users of the SN package, i thought i post here some useful help on R code on the 4 moments for the skew t sampling gives seldom good results for skewness and kurtosis, so one really needs the analytical results, it took me some time to get it from the article Azzalini, A. & Capitanio, A. (2003), Distributions generated by perturbation of symmetry with emphasis on a multivariate

Hello please note that the code at https://stat.ethz.ch/pipermail/r-help/2006-August/110892.html to compute indices of skewness and kurtosis for the skew-t distribution is not correct. It has been kindly pointed out that i made some error in this code, which was a bit too quickly copied from the paper. The 'sn' package already contains a facility for computing the cumulants, namely

Hello, Is there an R function that tells me the packages that come with R e.g. c("base","boot","methods","mgcv",...) Thank you Saptarshi [[alternative HTML version deleted]]

In the document "R Data Import/Export", section "Output to connections", there is the following portion of code: ## convert decimal point to comma in output, using a pipe (Unix) zz <- pipe(paste("sed s/\\./,/ >", "outfile"), "w") cat(format(round(rnorm(100), 4)), sep = "\n", file = zz) close(zz) ## now look at the output

Dear List-members, to download a file from the net, the function download.file(..) does the job. However, before embarking on the download, I would like to find out how large the file is. Is there a way to know it? Most easily, this question has been asked before, but I am new to the list. Regards, with thanks in advance, Adelchi Azzalini ---- Adelchi Azzalini <azzalini at

There seems to exist peculiar cases where optim does not take care of constraints on the parameters to be optimized over. The call to optim is of the form opt <- optim(cp, fn=sn.dev, gr=sn.dev.gh, method="L-BFGS-B", lower=c(-Inf, 1e-10, -0.99527), upper=c( Inf, Inf, 0.99527), control=control, X=X, y=y, hessian=FALSE) The code has worked fine

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### Today I have installed R 1.02.1 on my MSW-95 laptop; it essentially worked, but thre are two

If I define this function R> ask <- function (message = "Type in datum") eval(parse(prompt = paste(message, ": ", sep = ""))) the following is produced as expected on a Linux/debian machine R> ask("input") input: 3 [1] 3 R> ask("input") input: 3:6 [1] 3 4 5 6 R> ask("input") input: c(3,6) [1] 3 6 If I

I have summary statistics from many sets (10,000's) of near-normal continuous data. From previously generated QQplots of these data I can visually see that most of them are normal with a few which are not normal. I have the raw data for a few (700) of these sets. I have applied several tests of normality, skew, and kurtosis to these sets to see which test might yield a parameter which

Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me. Ruposh --- r-help-request at stat.math.ethz.ch wrote:

The package "sm" was obtained twice, one using R's built-in updating of packages, the second directly. In both cases the USA-NC CRAN mirror was used. In both cases, loading the package under R 2.10.1 for Windows resulted in a 'package obsolete' kind of message. Switching the mirror to USA-CA-1 (Berkeley) got a good package that loaded without complaint. R version 2.10.1

Hi, Is there a function in R that I can use to fit the data with skew t distribution? Speaking in detail, I first used the kernel density estimation to fit my data, then I drew the skew t using my specified location, scale, shape, and df to make it close to the kernel density. Now I want to get the parameter estimations of the skew t which give me the closet density to the kernel density.

I am learning glm function, but how do you fit data using exponential distribution with glm? In the help file, under "Family Objects for Models", no ready made option seems available for the distribution as well as for other distributions satisfying GLM requirements not listed there.

Hi, I need to create a plot (type = "h") and then overlay a skewed-normal curve on this distribution, but I'm not finding a procedure to accomplish this. I want to use the plot function here in order to control the bin distributions. I have explored the sn library and found the dsn function. dsn uses known location, scaling and shape parameters associated with a given input