dT {bbricks} | R Documentation |
Get the density of a set of samples from a t distribution. For a random vector x, the density function is defined as:
Gamma((df + p)/2) / (Gamma(df/2)df^{p/2} pi ^{p/2} |Sigma|^{1/2}) [1+1/df (x-df)^T Sigma^{-1} (x-df)]^{-(df +p)/2}
Where p is the dimension of x.
dT(x, mu, Sigma = NULL, A = NULL, df = 1, LOG = TRUE)
x |
matrix, when x is a numeric vector, it will be converted to a matrix with 1 column! |
mu |
numeric, mean vector. |
Sigma |
matrix, Sigma is proportional to the covariance matrix of x, one of Sigma and A should be non-NULL. |
A |
matrix, the Cholesky decomposition of Sigma, an upper triangular matrix, one of Sigma and A should be non-NULL. |
df |
numeric, degrees of freedom. |
LOG |
logical, return log density of LOG=TRUE, default TRUE. |
A numeric vector, the probability densities.
plot( dT(x=seq(-5,5,length.out = 1000),mu = 0,Sigma = 1,LOG = FALSE) ,type = "l" )