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:
BIC:
HQIC:
where
is the maximum likelihood value,
the number of parameters
estimated and
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)