similar to: Multivariate skew-t cdf

Hello, I have a bigger problem in calculating the Maximum Likelihood Estimator regarding the degree of freedom of a multivariate skew-t copula. First of all I would like to describe what this is all about, so that you can understand my problem: I have 2 time series with more than 3000 entries each. I would like to calculate a multivariate skew-t Copula that fits this time series. Notice:

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, In my simulations, I have to use the values of the cumulative distribution function of a multivariate normal with known mean vector and dispersion matrix. Please, can you tell me if there is a package in R to do that? Thank you very much for your greatly appreciate cooperation. Bernard Colin Colin Bernard Professeur titulaire Département de Mathématiques Faculté des Sciences Université

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,

Dear R-users, I have been using the code below in order to verify how the CDF of a skew-normal distribution was calculated: library(sn) s=seq(-30,30,by=0.1) a<-matrix(nrow=length(s),ncol=5) lambda=1 for(i in 1:length(s)){ a[i,1]=pnorm(s[i],mean=0,sd=1); a[i,2]=T.Owen(s[i],lambda); a[i,3]=a[i,5]-2*a[i,6]; a[i,4]=pnorm(s[i])-2*T.Owen(s[i],lambda);

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Hello, I'm using dmnorm from the package {mnormt} and getting strange results. First, according to the documentation, dmnorm should return a vector of densities, and I'm only getting one value returned (which is what I would expect). I've been interpreting this as the joint density of all values in the x vector (which is what I want). Should a vector of densities be returned, and if

> -----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)

Sir, I want to draw random from any multivariate disrtibution. Is there any function in R to do this? Regards, Suman Dhara [[alternative HTML version deleted]]

Hello, I've problems installing several packages in my R on Fedora 27 64 bit. I found out that it has to do something with a missing compiler (libgfortran.so.3, see below). It works if I downgrade the current version of libgfortran to the specified version by downloading libgfortran-6.2.1-2.fc25.x86_64.rpm and manually installing it. However, I don't want to mess up my system,

Hi, there. Does anyone know the R function for calculating the cdf of bivariate normal distribution function? Thanks. Yulei [[alternative HTML version deleted]]

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

I have drawn a 3D scatter plot : library(mnormt) library(scatterplot3d) dat = cbind(rmnorm(3, rep(0,2), diag(2)), 1:3) scatterplot3d(dat) Now I want to do 2 things : 1 : In the Z-axis (i.e. height), I want to see only numbers 1,2,3, etc NOT, 1,1.5,2,2.5............. 2. I want to add two Horizontal planes at hight z=2 and z=3. Those two planes should look like "bottom" of that 3D plot

Estimados Usuarios-R: Muy buenas tardes. Tengo un listado de precios en pesos por día: Día        Precio en $        01/01/04           0,04 02/01/04           0,11 03/01/04           0,11 04/01/04           0,04 05/01/04           0,10 06/01/04           0,00 07/01/04           0,10 08/01/04           0,10 09/01/04           0,14 10/01/04           0,21 11/01/04           0,21

Dear R-list member, I'm using the function pmnorm() (-->library(mnormt)) within a for-loop. Certain parameter values leads to an error message: "(In sqrt(diag(S)) : NaNs produced, In sqrt(1/diag(V)) : NaNs produced, In cov2cor(S) : diag(.) had 0 or NA entries; non-finite result is doubtful)" obviously because "NaNs" were produced. Is it possible to tell R that it

Hi everybody, I tried to analyze a custom Affymetrix 3'-biased Array. So I wanted to make a cdf package. (My CDF file size is 1.12Go). I tried several methods but the same error occured Method 1 > #Set the working directory > setwd("D:/Analyse R/Cel files") > #library to create cdf env > library("makecdfenv") >#Create cdf environment >pkgpath

Dear all, I have define a discrete distribution P(y_i=x_i)=p_i, which I want to plot a CDF plot. However, I can not find a function in R to draw it for me after searching R and R-archive. I only find the one for the sample CDF instead my theoretical one. I find stepfun can do it for me, however, I want to plot some different CDF with same support x in one plot. I can not manage how to do it with

Good Afternoon, I'm trying to create a cdf plot, with the following code. It works well, but I have little doubt, if you can help solve. When I create the plot, like the graph line would still not appear with point #cdf x<-table(Dataset$Apcode) View(s) hist(s) *plot(ecdf(x))* x<-1 37607 2 26625 3 5856 4 25992 5 30585 6 16064 7 9850 .. ... .. 186 52 -- View this message in

Hi all, I'm working with data to which I'm fitting three-parameter weibull distributions (shape, scale & shift). The data are of low sample sizes (between 10 and 80 observations), so I'm reluctant to check my fits using chi-square (also, I'd like to avoid bin choice issues). I'd use the Kolmogorov-Smirnov test, but of course this is invalid when the distribution

Given f, a pdf over a finite interval, is there any existing R function that can efficiently tabulate the cumulative distribution function for f, or produce all N+1 quantiles of the form i/N? "Efficiently" here means better than doing repeated integrations for each point.