View source: R/ordermscore.boot.r
ordermscore.boot | R Documentation |
Calculates order-m efficiency score (output, input and hyperbolic direction) for a set of assessment points (xeval, yeval) depending on sample points (xobs, yobs), using the initial algorithm of Cazals et al. (2002).
ordermscore.boot(xobs, yobs, xeval=xobs, yeval=yobs, m=30, B=200, m.move=FALSE)
xobs |
a matrix of size |
yobs |
a matrix of size |
xeval |
a matrix of size |
yeval |
a matrix of size |
m |
an integer, the number of selected firms |
B |
an integer, the number of replication |
m.move |
a boolean, to choose different values of m |
This function computes the algorithm initially proposed by Cazals et al. (2002). If m.move=TRUE, different values of m are given as suggested by Daouia et al (2009).
a data.frame
object with the average mean order-m efficiency score and standard deviation associated:
output |
output direction |
output |
output direction |
input |
input direction |
input |
input direction |
hyper |
hyperbolic direction |
hyper |
hyperbolic direction |
Abdelaati Daouia and Thibault Laurent
Cazals et al. (2002), Nonparametric frontier estimation: a robust approach, Journal of Econometrics.
Daouia et al. (2009), Regularization of Nonparametric Frontier Estimators, TSE working paper.
ordermscore
,alphascore
# 1st example
data(spain)
score.orderm.b <- ordermscore.boot(xobs = as.matrix(spain[, c(2, 3, 4)]),
yobs = as.matrix(spain[, 1]))
system.time(
ordermscore.boot(xobs = as.matrix(spain[, c(2, 3, 4)]),
yobs = as.matrix(spain[, 1]))
)
system.time(
ordermscore(xobs = as.matrix(spain[, c(2, 3, 4)]),
yobs = as.matrix(spain[, 1]))
)
# 2nd example
data(burposte)
ind.samp <- sample(nrow(burposte), 500)
xobs <- as.matrix(burposte[ind.samp[1:100], 2])
yobs <- as.matrix(burposte[ind.samp[1:100], 3])
xeval <- as.matrix(burposte[ind.samp[101:500], 2])
yeval <- as.matrix(burposte[ind.samp[101:500], 3])
score.orderm.2.b <- ordermscore.boot(xobs, yobs, xeval, yeval)
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