VLMCX (version 1.0)

AIC: 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)

Value

a numeric value with the corresponding AIC.

Arguments

fit

a betaVLMC object.

Author

Adriano Zanin Zambom <[e-mail]>

Examples

# \donttest{
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
# }