ic {sfaR} | R Documentation |
Extract information criteria of stochastic frontier models
Description
ic
returns information criterion from stochastic
frontier models estimated with sfacross
, sfalcmcross
,
or sfaselectioncross
.
Usage
## S3 method for class 'sfacross'
ic(object, IC = "AIC", ...)
## S3 method for class 'sfalcmcross'
ic(object, IC = "AIC", ...)
## S3 method for class 'sfaselectioncross'
ic(object, IC = "AIC", ...)
Arguments
object |
A stochastic frontier model returned
by |
IC |
Character string. Information criterion measure. Three criteria are available:
. |
... |
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{\left[\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.
See Also
sfacross
, for the stochastic frontier analysis model
fitting function using cross-sectional or pooled data.
sfalcmcross
, for the latent class stochastic frontier analysis
model fitting function using cross-sectional or pooled data.
sfaselectioncross
for sample selection in stochastic frontier
model fitting function using cross-sectional or pooled data.
Examples
## Not run:
## Using data on Swiss railway
# LCM (cost function) half normal distribution
cb_2c_u <- sfalcmcross(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')
## End(Not run)