AIC: Akaike Information Criteria for 'VLMCX' objects that compose...

In VLMCX: Variable Length Markov Chain with Exogenous Covariates

Akaike Information Criteria for VLMCX objects that compose Variable Length Markov Chains with Exogenous Covariates

Description

Computes the Akaike Information Criteria for the data using the estimated parameters of the multinomial logistic regression in the VLMCX fit.

Usage

AIC(fit)

Arguments

fit	a betaVLMC object.

Value

a numeric value with the corresponding AIC.

Author(s)

Adriano Zanin Zambom <[e-mail]>

Examples

set.seed(1)
n = 1000
d = 2

X = cbind(rnorm(n), rnorm(n))
p = 1/(1 + exp(0.5 + -2*X[,1] - 3.5*X[,2]))

y = c(sample(1:0,1), rbinom(n,1, p)) 

fit = maximum.context(y[1:n], X, max.depth = 3, n.min = 25)
draw(fit)
AIC(fit)
##[1] 563.5249

fit = VLMCX(y[1:n], X, alpha.level = 0.001, max.depth = 3, n.min = 25)
draw(fit)
AIC(fit)
##[1] 559.4967

VLMCX documentation built on May 29, 2024, 11:04 a.m.