AIC.gips {gips}

Akaike's An Information Criterion for gips class

Description

Akaike's An Information Criterion for gips class

Usage

## S3 method for class 'gips'
AIC(object, ..., k = 2)

## S3 method for class 'gips'
BIC(object, ...)

Arguments

object

An object of class gips. Usually, a result of a find_MAP().

...

Further arguments will be ignored.

k

Numeric, the penalty per parameter to be used. The default k = 2 is the classical AIC.

Value

AIC.gips() returns calculated Akaike's An Information Criterion

When the multivariate normal model does not exist (number_of_observations < n0), it returns NULL. When the multivariate normal model cannot be reasonably approximated (output of project_matrix() is singular), it returns Inf.

In both failure situations, shows a warning. More information can be found in the Existence of likelihood section of logLik.gips().

BIC.gips() returns calculated Schwarz's Bayesian Information Criterion.

Functions

• BIC(gips): Schwarz's Bayesian Information Criterion

Calculation details

For more details and used formulas, see the Information Criterion - AIC and BIC section in vignette("Theory", package = "gips") or its pkgdown page.

See Also

• AIC(), BIC() - Generic functions this AIC.gips() and BIC.gips() extend.

• find_MAP() - Usually, the AIC.gips() and BIC.gips() are called on the output of find_MAP().

• logLik.gips() - Calculates the log-likelihood for the gips object. An important part of the Information Criteria.

Examples

S <- matrix(c(
  5.15, 2.05, 3.10, 1.99,
  2.05, 5.09, 2.03, 3.07,
  3.10, 2.03, 5.21, 1.97,
  1.99, 3.07, 1.97, 5.13
), nrow = 4)
g <- gips(S, 14)
g_map <- find_MAP(g, optimizer = "brute_force")

AIC(g) # 238
AIC(g_map) # 224 < 238, so g_map is better than g in AIC
# ================================================================================
BIC(g) # 244
BIC(g_map) # 226 < 244, so g_map is better than g in BIC