Benchmarking (version 0.10)

outlier.ap: Detection of outliers in benchmark models

Description

The functions implements the Wilson (1993) outlier detection method using only R functions.

Usage

outlier.ap(X, Y, NDEL = 3, NLEN = 25, TRANSPOSE = FALSE)

outlier.ap.plot(ratio, NLEN = 25, xlab = "r", ylab = "Log ratio", 
                ..., ylim)

Arguments

X

Input as a firms times goods matrix, see TRANSPOSE.

Y

Output as a firms times goods matrix, see TRANSPOSE.

NDEL

The maximum number of firms to be considered as a group of outliers.

NLEN

The number of ratios to calculate/plot for each level or removel, the number of rows in ratio used.

TRANSPOSE

Input and output matrices are treated as goods times firms as defaults. If TRANSPOSE=FALSE then marices are firms times goods as is standard in R for statistical models

ratio

The ratio component from the list as output from outlier.ap.

xlab

Label for the x-axis.

ylab

Label for the y-axis

ylim

The y limits (y1, y2) of the plot, an array/vector of length 2.

...

Usual options for the methods plot and lines.

Value

• ratio
• imat
• r0

Details

Uses the function det to calculate $R^{(i)}_{\min}$.

References

Wilson, P.W. (1993), Detecing outliers in deterministic nonparametric frontier models with multiple outputs, Journal of Business and Economic Statistics 11, 319-323. Wilson, P.W. (2008), "FEAR 1.0: A Software Package for Frontier Efficiency Analysis with R," Socio-Economic Planning Sciences 42, 247--254

See Also

The function ap in the package FEAR help.

Examples

n <- 25
x <- matrix(rnorm(n))
y <- .5 + 2.5*x + 2*rnorm(25)
tap <- outlier.ap(x,y,NDEL=3)
print(cbind(tap$imat,tap$rmin),na.print="",digit=2)
outlier.ap.plot(tap$ratio)