graph2_m_BOD {rcDEA}

Graph to select m

Description

This function allows to draw a graph that relates the average efficiency score and the choice of m

Usage

graph2_m_BOD(output, mseries, B, RTS = "crs", ORIENTATION = "in", print = TRUE)

Arguments

output

matrix (or vector) of indicators along which the units are evaluated.

mseries

vector containing the different values of m that needed to be tested.

B

number of bootstrap replicates

RTS

For more details see the dea function in the package Benchmarking. Text string or a number defining the underlying DEA technology / returns to scale assumption. 0 fdh Free disposability hull, no convexity assumption 1 vrs Variable returns to scale, convexity and free disposability 2 drs Decreasing returns to scale, convexity, down-scaling and free disposability 3 crs Constant returns to scale, convexity and free disposability 4 irs Increasing returns to scale, (up-scaling, but not down-scaling), convexity and free disposability 5 irs2 Increasing returns to scale (up-scaling, but not down-scaling), additivity, and free disposability 6 add Additivity (scaling up and down, but only with integers), and free disposability; also known af replicability and free disposability, the free disposability and replicability hull (frh) – no convexity assumption 7 fdh+ A combination of free disposability and restricted or local constant return to scale 10 vrs+ As vrs, but with restrictions on the individual lambdas via param

ORIENTATION

For more details see the dea function in the package Benchmarking. Input efficiency "in" (1), output efficiency "out" (2), and graph efficiency "graph" (3). For use with DIRECT, an additional option is "in-out" (0).

print

If print = TRUE the number of the unit under evaluation is printed. In case of large sample the function could require some time, so it could be useful to control how many units have already been evaluated and which one still have to be evaluated. By default print = FALSE.

Value

This function return a plot representing the average score from the robust analysis for the different values of m chosen.

Examples

 #Example with a very small sample to decrease computational time.
 y1 <-runif(20, 50, 75)
 y2 <-runif(20, 30, 75)
 y <- cbind(y1, y2)

 graph2_m_BOD(output = y, mseries = c(5, 10, 15), B = 50,
              RTS = "crs", ORIENTATION = "in")



 #An example with a larger sample size.
 y1 <-runif(100, 50, 75)
 y2 <-runif(100, 30, 75)
 y <- cbind(y1, y2)

 graph2_m_BOD(output = y,
              mseries = c(20, 30, 40, 50, 60, 70, 80),
              B = 50, RTS = "crs", ORIENTATION = "in")