Nothing
dmvESN = function(x,mu=rep(0,length(lambda)),Sigma=diag(length(lambda)),lambda,tau=0){
#Validating Lambda
if(is.null(lambda)){
#not provided by user
stop("Skewness parameter 'lambda' must be provided (zero vector fot the symmetric case).")
}else{
#validate input
if(length(c(lambda)) != length(c(mu)) | !is.numeric(lambda)){
stop("Lambda must be numeric and have same dimension than mu.")
}
if(all(lambda==0)){
warning("Lambda = 0, Normal case is considered.",immediate. = TRUE)
return(dmvnorm(x = x,mean = c(mu),sigma = as.matrix(Sigma)))
}
}
if(is.null(tau)){
#not provided by user
stop("Extension parameter 'tau' must be provided for the ESN case (zero for the Skew-normal case).")
}else{
#validate input
if(!is.numeric(tau) | length(tau)>1){
stop("Tau must be numeric real number.")
}
tautil = tau/sqrt(1+sum(lambda^2))
if(tautil< -37){
#print("normal aproximation")
Delta = sqrtm(Sigma)%*%lambda/sqrt(1+sum(lambda^2))
return(dmvnorm(x = x,mean = c(mu - tautil*Delta),sigma = as.matrix(Sigma - Delta%*%t(Delta))))
}
return(dmvESN0(x,mu,Sigma,lambda,tau))
}
}
dmvESN0 <- function(y, mu, Sigma, lambda,tau){
#y: deve ser uma matrix onde cada linha tem um vetor de dados multivariados de dimens?o ncol(y) = p. nrow(y) = tamanho da amostra
#mu, lambda: devem ser do tipo vetor de mesma dimens?o igual a ncol(y) = p
#Sigma: Matrix p x p
if(length(c(mu)) == 1){return(dmvESN1(c(y),mu,Sigma,lambda,tau))}
if(!is.matrix(y)){
y = matrix(y,ncol = nrow(Sigma),byrow = TRUE)
}
n <- nrow(y)
p <- ncol(y)
tautil<-tau/sqrt(1+sum(lambda^2))
dens <- dmvnorm(y, c(mu),Sigma)*exp(
pnorm(apply(matrix(rep(t(lambda)%*%solve(sqrtm(Sigma)),n), n, p, byrow = TRUE)*
(y - matrix(rep(mu, n), n, p, byrow = TRUE)), 1,sum)+tau,log.p = TRUE) - pnorm(tautil,log.p = TRUE))
return(dens)
}
#######################################################################################
#######################################################################################
dmvESN1 <- function(y, mu=0, Sigma=1, lambda,tau){
#y: deve ser uma matrix onde cada linha tem um vetor de dados multivariados de dimens?o ncol(y) = p. nrow(y) = tamanho da amostra
#mu, lambda: devem ser do tipo vetor de mesma dimens?o igual a ncol(y) = p
#Sigma: Matrix p x p
n <- length(c(y))
p <- 1
s = sqrt(Sigma)
tautil = tau/sqrt(1+sum(lambda^2))
if(tautil< -35){
#print("normal aproximation")
Gamma = Sigma/(1+lambda^2)
mub = lambda*tau*Gamma/s
return(dnorm(y,mu-mub,sqrt(Gamma)))
}
dens <- dnorm(x = c(y),mean = c(mu),sd = sqrt(Sigma))*exp(
pnorm(apply(matrix(rep(t(lambda)%*%solve(sqrtm(Sigma)),n), n, p, byrow = TRUE)*
(y - matrix(rep(mu, n), n, p, byrow = TRUE)), 1,sum)+tau,log.p = TRUE) - pnorm(tautil,log.p = TRUE))
return(dens)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.