ic: Extract information criteria of classic or latent class...

In sfaR: Stochastic Frontier Analysis using R

Extract information criteria of classic or latent class stochastic models

Description

ic returns information criterion from classic or latent class stochastic frontier models estimated with sfacross or lcmcross.

Usage

## S3 method for class 'sfacross'
ic(object, IC = "AIC", ...)

## S3 method for class 'lcmcross'
ic(object, IC = "AIC", ...)

Arguments

object	A classic or latent class stochastic frontier model returned by sfacross or lcmcross.
IC	Character string. Information criterion measure. Three criteria are available: "AIC" for Akaike information criterion (default) "BIC" for Bayesian information criterion "HQIC" for Hannan-Quinn information criterion
...	Currently ignored.

Details

The different information criteria are computed as follows:

• AIC: -2 \log{LL} + 2 * K

• BIC: -2 \log{LL} + \log{N} * K

• HQIC: -2 \log{LL} + 2 \log{≤ft[\log{N}\right]} * K

where LL is the maximum likelihood value, K the number of parameters estimated and N the number of observations.

Value

ic returns the value of the information criterion (AIC, BIC or HQIC) of the maximum likelihood coefficients.

Author(s)

K Hervé Dakpo, Yann Desjeux and Laure Latruffe

See Also

sfacross, for the stochastic frontier analysis model fitting function.

lcmcross, for the latent class stochastic frontier analysis model fitting function.

Examples

## Using data on Swiss railway
# LCM (cost function) half normal distribution
cb_2c_u <- lcmcross(formula = LNCT ~ LNQ2 + LNQ3 + LNNET + LNPK + LNPL, 
    udist = "hnormal", uhet = ~ 1, data = swissrailways, S = -1, method="ucminf")
  ic(cb_2c_u)
  ic(cb_2c_u, IC = "BIC")
  ic(cb_2c_u, IC = "HQIC")

sfaR documentation built on May 3, 2022, 3 p.m.