# R/BIC-SVC_mle.R In varycoef: Modeling Spatially Varying Coefficients

#### Documented in AIC.SVC_mleBIC.SVC_mle

```#' @title Conditional Akaike's and Bayesian Information Criteria
#'
#' @name IC.SVC_mle
#' @aliases AIC.SVC_mle
#'
#' @description Methods to calculate information criteria for
#' \code{\link{SVC_mle}} objects. Currently, two are supported: the conditional
#' Akaike's Information Criteria \eqn{cAIC = -2*log-likelihood + 2*(edof + df)}
#' and the Bayesian Information Criteria \eqn{BIC = -2*log-likelihood + log(n) * npar}.
#' Note that the Akaike's Information Criteria is of the corrected form, that
#' is: \eqn{edof} is the effective degrees of freedom which is derived as the
#' trace of the hat matrices and df is the degree of freedoms with respect to
#' mean parameters.
#'
#' @param ...    further arguments
#'
#' @return numeric, value of information criteria
#'
#' @author Jakob Dambon
#' @importFrom stats BIC
#' @export
BIC.SVC_mle <- function(object, ...) {
as.numeric(
-2*logLik(object) +
log(nobs(object)) *
object\$df\$df
)
}

#' @rdname IC.SVC_mle
#' @param conditional string. If \code{conditional = "BW"}, the
#' conditional AIC is calculated.
#'
#' @importFrom stats AIC
#' @export
AIC.SVC_mle <- function(object, conditional = "BW", ...) {
as.numeric(
-2*logLik(object) +
2*(object\$df\$edof + object\$df\$df)
)
}
```

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varycoef documentation built on Sept. 18, 2022, 1:07 a.m.