similar to: Multivariate integration with infinite limits

Hi all, Does anybody know any function that performs gaussian adapative quadrature integration of univariate functions? Thanks in advance, Regards, Caio __________________________________________________ [[alternative HTML version deleted]]

Dear list, [cross-posting from Stack Overflow where this question has remained unanswered for two weeks] I'd like to perform a numerical integration in one dimension, I = int_a^b f(x) dx where the integrand f: x in IR -> f(x) in IR^p is vector-valued. integrate() only allows scalar integrands, thus I would need to call it many (p=200 typically) times, which sounds suboptimal. The

Hi all, I'd like to fit a multivariate regression with the variance of the error term porportional to the predictors, like the WLS in the univariate case. y_1~x_1+x_2 y_2~x_1+x_2 var(y_1)=x_1*sigma_1^2 var(y_2)=x_2*sigma_2^2 cov(y_1,y_2)=sqrt(x_1*x_2)*sigma_12^2 How can I specify this in R? Is there a corresponding function to the univariate specification lm(y~x,weights=x)??

Hi R users, Heard that I can't use multivariate GARCH model in R because R has only univariate GARCH models.... So, how can I run a multivariate GARCH model in R? Also, SPLUS has this utility...any ideas how can I use it in R? Thanks Shubha [[alternative HTML version deleted]]

Dear R users! Does someone know about any algorithms / packages in R, that perform classification / regression / decision trees using multivariate splits? I have done some research, but I found nothing. Packages "tree" and "rpart" seem only to be able to do CART with univariate splits. Thank you for your help! B?lint -- Cz?cz B?lint PhD hallgat? BCE KTK Talajtan ?s

Dear R-users I would like to integrate something like \int_k^\infty (1 - F(x)) dx, where F(.) is a cumulative distribution function. As mentioned in the "integrate" help-page: integrate(dnorm,0,20000) ## fails on many systems. This does not happen for an adaptive Simpson or Lobatto quadrature (cf. Matlab). Even though I am hardly familiar with numerical integration the implementation

Dear R users, I have a data structure as follows: id two res1 res2 c1 c2 inter 1 -0.786093166 1 0 1 2 6 3 -0.308495749 1 0 0 1 2 5 -0.738033048 1 0 0 0 1 7 -0.52176252 1 0

Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general classes of influence measures for multivariate regression models, including analogs of Cook's D, Andrews & Pregibon COVRATIO, etc. As in univariate response models, these are based on leverage and residuals based on omitting one (or more) observations at a time and refitting, although, in the univariate case, the

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

Hello! Recently I got report that my package mar1s doesn't pass checks any more on R 3.0.2. I started to investigate and found the following difference in multivariate time series handling in R 3.0.2 compared to R 2 (I've checked on 2.14.0). Suppose I wish to calculate seasonal component for time series. In case of multivariate time series, I wish to process each column independently.

Dear aDVISOR, Hope I am not disturbing you. Can you tell me how I can perform Multivariate Garch analysis in R. Also please, it is my humble request let me know some resource materials on Multivariate Garch analysis itself. Sincerely yours, -- Arun Kumar Saha, M.Sc.[C.U.] S T A T I S T I C I A N [Analyst] Transgraph Consulting [www.transgraph.com] Hyderabad, INDIA Contact # Home:

Hi, I have a problem with ctree of party package. I have data on distribution of more than one species (about 50 species) and I would like identify the relation of this multivariate object (species distribution) with a number of explanatory variables. rs is the name of my dataframe containing the species (columns from 2 to 51) and the explanatory variables (columns 52 and 53). Rows are my

Hi! I know that there is function in fBasics package for univariate Jarque-Bera test and a funtion for univariate Ljung-Box test in stats package. But I am wondering if there is a function somewhere to do the tests for multivariate time series? Thanks, John [[alternative HTML version deleted]]

Hi, I am trying to perform Maximum Likelihood estimation of a Multivariate model (2 independent variables + intercept) with autocorrelated errors of 1st order (ar(1)). Does R have a function for that? I could only find an univariate option (ar.mle function) and when writing my own I find that it is pretty memory-consuming (and sometimes wrong) so there must be a better way. Thanks, KB

To whom it may concern, I try to simulate a non-normal multivariate distribution. The MASS package allows by mean of "mvrnorm" command to perform a multivariate normal simulation. Is there an equivalent command for an arbitrary multivariate distribution available in the R-language? Thank you in advance. Bernard Colin Colin Bernard Professeur titulaire D?partement de Math?matiques et

Hello all Is there a method/package in R in which I can do regressions for more than 50 independent variables ? Regards The woods are lovely, dark and deep But I have promises to keep And miles before I go to sleep And miles before I go to sleep ----- [[alternative HTML version deleted]]

> -----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)

I'm relatively new to R and I'm stuck. I'm trying to construct a surface to optimize from a multivariate dataset. The dataset contains the response of a system to various stimuli. I am trying to optimize the mix of stimuli to maximize the response. To do so, I've interpolated the various datasets of type response vs. stimuli and I now have an array of functions

Hi, Suppose I have a multivariate response Y (n x k) obtained at a set of predictors X (n x p). I would like to perform a linear regression taking into consideration the covariance structure of Y within each unit - this would be represented by a specified matrix V (k x k), assumed to be the same across units. How do I use "lm" to do this? One approach that I was thinking of

Hi all, I would like to know whether there is any function in R were i can find the cross-correlation of two or more multivariate (time series) data. I tried the function ccf() but it seems like to have two univariate datasets. Please let me know. sincerely, sandeep -- Sandeep Joseph PhD Post Doctoral Associate Center for Tropical & Emerging Global Diseases Paul D. Coverdell Center,