logLik.frontier {frontier} | R Documentation |
Extract the log-likelihood value(s) from stochastic frontier models
returned by frontier
.
## S3 method for class 'frontier': logLik( object, which = "mle", newParam = NULL, ... )
object |
an object of class frontier
(returned by the function frontier ). |
which |
character string. Which log-likelihood value should be returned? 'ols' for the log-likelihood value of the parameters estimated by OLS, 'grid' for the log-likelihood value of the parameters obtained by the grid search (only if no starting values were provided), 'start' for the log-likelihood value of the starting values of the parameters specified by the user (only if starting values were provided), or 'mle' for the log-likelihood values of the parameters estimated by Maximum Likelihood. |
newParam |
optional vector of parameters.
If this argument is provided by the user, the log-likelihood value
of the model object is calculated with these (new) parameters. |
... |
currently unused. |
logLik.frontier
returns an object of class logLik
,
which is a numeric scalar (the log-likelihood value) with 2 attributes:
nobs
(total number of observations in all equations) and
df
(number of free parameters, i.e. length of the coefficient vector).
Arne Henningsen
# example included in FRONTIER 4.1 data( front41Data ) # SFA estimation with starting values obtained from a grid search sfaResult <- sfa( log( output ) ~ log( capital ) + log( labour ), data = front41Data ) logLik( sfaResult, which = "ols" ) logLik( sfaResult, which = "grid" ) logLik( sfaResult ) # SFA estimation with starting values provided by the user sfaResult2 <- sfa( log( output ) ~ log( capital ) + log( labour ), data = front41Data, startVal = 0.9 * coef( sfaResult ) ) logLik( sfaResult2, which = "ols" ) logLik( sfaResult2, which = "start" ) logLik( sfaResult2 ) # evaluate log likelihood function for a user-provided parameter vector logLik( sfaResult, newParam = 0.9 * coef( sfaResult ) ) # equal to logLik( sfaResult2, which = "start" ) # log likelihood function for different values of gamma plot( t( sapply( seq( 0.05, 0.95, 0.05 ), function(x) c( x, logLik( sfaResult, newParam = c( coef( sfaResult )[1:4], x ) ) ) ) ) )