| Pn_logtDensity-class | R Documentation |
(\prod_{i=1}^n x_i^{-1})\frac{\Gamma\left[(\nu+n)/2\right]}{\Gamma(\nu/2)\nu^{n/2}\pi^{n/2}\left|{\Sigma}\right|^{1/2}}\left[1+\frac{1}{\nu}({\log(\vec{x})}-{\vec{\delta}})^{T}{\Sigma}^{-1}({\log(\vec{x})}-{\vec{\delta}})\right]^{-(\nu+n)/2} on [0,\infty)^nImplementation of the function
f \colon [0,\infty)^n \to (0,\infty),\, \vec{x} \mapsto f(\vec{x}) = (\prod_{i=1}^n x_i^{-1})\frac{\Gamma\left[(\nu+n)/2\right]}{\Gamma(\nu/2)\nu^{n/2}\pi^{n/2}\left|{\Sigma}\right|^{1/2}}\left[1+\frac{1}{\nu}({\log(\vec{x})}-{\vec{\delta}})^{T}{\Sigma}^{-1}({\log(\vec{x})}-{\vec{\delta}})\right]^{-(\nu+n)/2},
where n \in \{1,2,3,\ldots\} is the dimension of the integration domain [0,\infty)^n = \times_{i=1}^n [0,\infty).
In this case the integral is know to be
\int_{[0,\infty)^n} f(\vec{x}) d\vec{x} = 1.
The instance needs to be created with four parameters representing the dimension n, the location vector \vec{\delta}, the variance-covariance matrix \Sigma which needs to be symmetric positive definite and the degrees of freedom parameter \nu.
dimAn integer that captures the dimension
deltaA vector of size dim with real entries.
sigmaA matrix of size dim x dim that is symmetric positive definite.
dfA positive numerical value representing the degrees of freedom.
Klaus Herrmann
n <- as.integer(3)
f <- new("Pn_logtDensity",dim=n,delta=rep(0,n),sigma=diag(n),df=3)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.