outlier.ap {Benchmarking} | R Documentation |
The functions implements the Wilson (1993) outlier detection method using only R functions.
outlier.ap(X, Y, NDEL = 3, NLEN = 25, TRANSPOSE = FALSE) outlier.ap.plot(ratio, NLEN = 25, xlab = "r", ylab = "Log ratio", ..., ylim)
X |
Input as a firms times goods matrix, see |
Y |
Output as a firms times goods matrix, see
|
NDEL |
The maximum number of firms to be considered as a group of outliers, i.e. the maximum number of firms to be deleted. |
NLEN |
The number of ratios to save for each level
or removal, the number of rows in |
TRANSPOSE |
Input and output matrices are treated as firms
times goods matrices for the default value |
ratio |
The |
xlab |
Label for the x-axis. |
ylab |
Label for the y-axis |
ylim |
The y limits |
... |
Usual options for the methods |
An implementation of the method in Wilson (1993) using only R
functions and especially the function det
to calculate
R^{(i)}_{\min}.
An elementary presentation of the method is found in Bogetoft and Otto (2011), Sect. 5.13 on outliers.
ratio |
A |
imat |
A |
r0 |
A |
The function outlier.ap
is extremely slow and for NDEL
larger than 3 or 4 it might be advisable to use the function ap
from the package FEAR.
The name of the returned components are the same as for ap
in
the package FEAR.
Peter Bogetoft and Lars Otto larsot23@gmail.com
Bogetoft and Otto; Benchmarking with DEA, SFA, and R; Springer 2011
Wilson (1993), “Detecing outliers in deterministic nonparametric frontier models with multiple outputs,” Journal of Business and Economic Statistics 11, 319-323.
Wilson (2008), “FEAR 1.0: A Software Package for Frontier Efficiency Analysis with R,” Socio-Economic Planning Sciences 42, 247–254
The function ap
in the package FEAR.
n <- 25 x <- matrix(rnorm(n)) y <- .5 + 2.5*x + 2*rnorm(25) tap <- outlier.ap(x,y, NDEL=2) print(cbind(tap$imat,tap$rmin), na.print="", digit=2) outlier.ap.plot(tap$ratio)