sicigt {ig} | R Documentation |
The function sicig()
gives the Schwartz information criterion (SIC)
value assuming an IGTD with parameters mu, lambda and a specific kernel.
sicigt(x, nu = 1.0, kernel = "normal")
x |
Vector of observations. |
nu |
Shape parameter corresponding to the degrees of freedom of the t distribution. In the case of the Laplace, logistic, normal kernels, nu can be fixed at the value 1.0 since this parameter is not involved in these kernels. |
kernel |
Kernel of the pdf of the associated symmetrical distribution
by means of which the IGTD is obtained. The kernels:
"laplace" , "logistic" , "normal" and
"t" are available. |
The SIC is a selection model criterion based on information loss. According to this criterion, it is possible to choice a hypothetic model that better describe the data set considering the smaller SIC value. The SIC is defined as SIC = -l(theta)/n+ p log(n)/(2n), where l(theta) is the log-likelihood function associated with the model, n is the sample size and p is the number of involved parameters; for more details see Spieglhaiter, Best, Carlin and van der Linde (2002).
sicigt()
gives the value for the SIC of the IGTD.
Víctor Leiva <victor.leiva@uv.cl; victor.leiva@yahoo.com>,
Hugo Hernández <hugo.hernandez.p@gmail.com> and
Antonio Sanhueza <asanhueza@ufro.cl>.
Spieglhaiter, D. J., Best, N. G., Carlin, B. P., van der Linde, A. (2002). Bayesian measures of complexity and fit. Journal of the Royal Statistical Society Series B 64, 1-34.
Sanhueza, A., Leiva, V., Balakrishnan, N. (2008). A new class of inverse Gaussian type distributions. Metrika (in press).
## Generates a sample from the IGTD with normal kernel x <- rigt(300, mu = 1.0, lambda = 1.0, kernel = "normal") ## Computes the SIC value of the IGTD with normal kernel from the data x sicigt(x, 1.0, kernel = "normal")