Nothing
dpostg <-
function(X,Z,Y,betas,gammas,gpri,Gpri) {
Y=as.matrix(Y)
if(is.null(X)|is.null(Z)|is.null(Y)){
stop("There is no data")
}
if(max(Y)> 1 | min(Y) < 0){
stop("The data is not in the (0,1) interval")
}
if(nrow(X)!=nrow(Z)|nrow(X)!=nrow(Y)|nrow(Z)!=nrow(Y)){
stop("The variables have a diferent size")
}
#if(!is.matrix(betas)|!is.matrix(gammas)){
# stop("The parameters must be a matrix")
#}
if(!is.vector(gpri)){
stop("The mean of the parameters must be a vector")
}
if(ncol(Gpri)!=nrow(Gpri)){
stop("The covariance matriz is not square")
}
# if(ncol(X)!=nrow(betas)| ncol(Z)!=nrow(gammas)|ncol(Z)!=length(g_pri)|ncol(Z)!=ncol(G_pri)){
# stop("The initial values are not conformable to the data")
#}
eta <- X%*%betas
mu <- exp(eta)/(1+exp(eta))
tau <- Z%*%gammas
phi <- exp(tau)
L <- prod(dbeta(Y, phi*mu,phi*(1-mu))) # verosimilitud
P <- dmvnorm(t(gammas),gpri,Gpri) # priori
value=L*P
value
}
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