similar to: Welcome to the "R-help" mailing list

Hi, I want to calculate the Value at Risk with using some distirbutions and a volatility model. I use the following data(http://uploadeasy.net/upload/cdm3n.rar) which are losses (negative returns) of a company of approx. the last 10 years. So I want to calculated the Value at Risk, this is nothing else than the quantile. Since I have losses I consider the right tail of the distribution. Consider

Dear All I want to generate multivariate normal data in R for a given covariance matrix, i.e. my generated data must have the given covariance matrix. I know the rmvnorm command is to be used but may be I am failing to properly assign the covariance matrix. Any help will be greatly appreciated thanks. M. R. Ahmad

Hi, I'm working on the codes below however every time I run them when they get to OpenBUGS I keep getting the error message: array index is greater than array upper bound for hab. Any help would be greatly appreciated, Suzie Codes: ungulate <- read.csv(file.choose ()) #ungulate ungulate <- as.matrix(ungulate);colnames(ungulate)<-NULL;rownames(ungulate)<-NULL

Dear All, Is there any function in R for computing "multivariate chi-square distribution"? How about "multivariate gamma distribution"? I appreciate any comment on this subject. Thank you, Amin Zollanvari PhD student Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX

I am trying to replicate part of the EuropeanOptionExample using my date. I have a data.frame containing all my inputs atm.vols<-subset(data.vols,moneyness==min); #Some days have the abs(moneyness) exactly between two strike prices, #Traders will alway price an option at the higher vol when selling it, so we will too.

I'm trying to use garchFit from fSeries, with Student or Skewed Student conditionnal distribution. Let's say that eps (vector) is my series of daily log-returns: data(EuStockMarkets) eps = diff(log(EuStockMarkets[,"CAC"])) library(fSeries) g = garchFit(series = eps, formula.var = ~garch(2,2), cond.dist = "dstd") s = g at fit$series All the coefficients are ok

Hi the manpage says that crossprod(x,y) is formally equivalent to, but faster than, the call 't(x) %*% y'. I have a vector 'a' and a matrix 'A', and need to evaluate 't(a) %*% A %*% a' many many times, and performance is becoming crucial. With f1 <- function(a,X){ ignore <- t(a) %*% X %*% a } f2 <- function(a,X){ ignore <-

Dear R People: Here is some output from AR and ARIMA functions: > xb <- arima.sim(n=120,model=list(ar=0.85)) > xb.ar <- ar(xb) > xb.ar Call: ar(x = xb) Coefficients: 1 0.6642 Order selected 1 sigma^2 estimated as 1.094 > xb.arima <- arima(xb,order=c(1,0,0),include.mean=FALSE) > xb.arima Call: arima(x = xb, order = c(1, 0, 0), include.mean = FALSE)

Ok, I try it again with plain text, with a simple R code example and just sending it to the r list and you move it to sig finance if it is necessary. I try to be as detailed as possible. I want to fit a distribution to my financial data using a volatility model to estimate the VaR. So in case of a normal distribution, this would be very easy, I assume the returns to follow a normal distribution

Hello there, I would like to simulate X --Normal (20, 5) Y-- Normal (40, 10) and the correlation between X and Y is 0.6. How do I do it in R? Thank you very much Lisa Wang Msc. Princess Margaret Hospital Toronto, Ca

I'm proposing to add something like this to the stats package : ---------------------------------------------------------- ### "The" sigma in lm/nls - "like" models: sigma <- function(object, ...) UseMethod("sigma") ## works whenever deviance(), nobs() and coef() do fine: sigma.default <- function (object, use.fallback=TRUE, ...)

Dear addresses, I need perform a batch of 10 000 simulations for each of 4 options considered. (The idea is to obtain the parameter estimates in a heteroskedastic linear regression model - with additive or mixed heteroskedasticity - via the Kenward-Roger small-sample adjusted covariance matrix of disturbances). For this purpose I wrote an R program which would capture all possible options (true

Greetings: I would like comments on this example and after fixing it up, I need help from someone who has access to insert this in R's help page for plotmath. I uploaded a drawing http://pj.freefaculty.org/R/Normal-2009.pdf that is created by the following code http://pj.freefaculty.org/R/Normal1_2009_plotmathExample.R This will be a good addition to the plotmath help page/example.

Hi Folks! Just started using the gamlss package and I tried a simple code example (see below). Why the negative sigma? John > y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, : possible convergence problem: optim gave code=1 false convergence (8)2: In MLE(ll4, start = list(eta.mu =

Here's another example of my plotmath whipping boy, the Normal distribution. A colleague asks for a Normal plotted above a series of axes that represent various other distributions (T, etc). I want to use vectors of equations in plotmath to do this, but have run into trouble. Now I've isolated the problem down to a relatively small piece of working example code (below). If you would

Dear friends - I'm on winXP, R 2.71 - I have with some help dveloped this multivariate normal model, which gives very plausible results in WinBUGS even without any initial values specified. However, when I then try to run the same model via the bugs function in R2WinBUGS with inits specified as inits=NULL the program stops in a dead end. So I have tried to make inits for the bugs function

Dear all, I am experiencing problems using the glmmPQL function in the MASS package (Venables & Ripley 2002) to model binomial data with spatial autocorrelation. My question - is the presence of birds affected by various hydrological parameters? Presence/absence data were collected from 83 sites and coupled against hydrological data from the same site. The bird survey sampling effort

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

Hi , R is a new language for me so sorry in advance if this error is to basic for posting. I have tried the R manual and search online for quite a few, if anyone could help i would be very thankful. Here is my code. kappa = 1.1 theta = 0.1 sigma = 0.4 rho = -0.6 v0 = 0.2 r = 0.05 T = 0.5 s0 = 1 K = 0.5 type = 1 Hestoncall = function(kappa,theta,sigma,rho,v0,r,T,s0,K,type) { u = 0.5 b

Hello! I was wondering if it's possible to see the actual code of checkTmvArgs function that is part of the code for rtmvnorm (which is below - I just typed "rtmvnorm" on the prompt). I get an error: Error in checkTmvArgs(mean, sigma, lower, upper) : sigma must be a symmetric matrix At the same time I am pretty sure that the matrix I am passing as sigma is a var-covar matrix