logLik | R Documentation |
logLik
extracts the log-likelihood value(s) from stochastic
frontier models estimated with sfacross
, sfalcmcross
,
or sfaselectioncross
.
## S3 method for class 'sfacross'
logLik(object, individual = FALSE, ...)
## S3 method for class 'sfalcmcross'
logLik(object, individual = FALSE, ...)
## S3 method for class 'sfaselectioncross'
logLik(object, individual = FALSE, ...)
object |
A stochastic frontier model returned
by |
individual |
Logical. If |
... |
Currently ignored. |
logLik
returns either an object of class
'logLik'
, which is the log-likelihood value with the total number of
observations (nobs
) and the number of free parameters (df
) as
attributes, when individual = FALSE
, or a list of elements, containing
the log-likelihood of each observation (logLik
), the total number of
observations (Nobs
) and the number of free parameters (df
),
when individual = TRUE
.
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.
## Not run:
## Using data on fossil fuel fired steam electric power generation plants in the U.S.
# Translog SFA (cost function) truncated normal with scaling property
tl_u_ts <- sfacross(formula = log(tc/wf) ~ log(y) + I(1/2 * (log(y))^2) +
log(wl/wf) + log(wk/wf) + I(1/2 * (log(wl/wf))^2) + I(1/2 * (log(wk/wf))^2) +
I(log(wl/wf) * log(wk/wf)) + I(log(y) * log(wl/wf)) + I(log(y) * log(wk/wf)),
udist = 'tnormal', muhet = ~ regu, uhet = ~ regu, data = utility, S = -1,
scaling = TRUE, method = 'mla')
logLik(tl_u_ts)
## Using data on eighty-two countries production (GDP)
# LCM Cobb Douglas (production function) half normal distribution
cb_2c_h <- sfalcmcross(formula = ly ~ lk + ll + yr, udist = 'hnormal',
data = worldprod, S = 1)
logLik(cb_2c_h, individual = TRUE)
## End(Not run)
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