jackstrap (version 0.1.0)

jackstrap: Jackstrap Method: Tool identifies outliers in Nonparametric Frontier. This function applies the developed technique by Sousa and Stosic (2005) Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers.

Description

Jackstrap Method: Tool identifies outliers in Nonparametric Frontier. This function applies the developed technique by Sousa and Stosic (2005) Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers.

Usage

jackstrap(
  data,
  ycolumn,
  xcolumn,
  bootstrap = 1000,
  perc_sample_bubble = 0.1,
  dea_method = "vrs",
  orientation_dea = "in",
  n_seed = NULL,
  repos = FALSE,
  num_cores = 1
)

Arguments

data

is the dataset with input and output used to measure efficiency; Dataset need to have this form: 1th column: name of DMU (string); 2th column: code of DMU (integer); n columns of output variables; n columns of input variables.

ycolumn

is the quantity of y columns of dataset.

xcolumn

is the quantity of x columns of dataset.

bootstrap

is the quantity of applied resampling.

perc_sample_bubble

is the percentage of sample in each bubble.

dea_method

is the dea method: "crs" is DEA with constant returns to scale (CCR); "vrs" is DEA with variable returns to scale; and "fdh" is Free Disposal Hull (FDH) with variable returns to scale.

orientation_dea

is the direction of the DEA: "in" for focus on inputs; and "out" for focus on outputs.

n_seed

is the code as seed used to get new random samples.

repos

identify if the resampling method is with reposition TRUE or not FALSE.

num_cores

is the number of cores available to process.

Value

Return the jackstrap object with information as follows: "mean_leverage" is leverage average for each DMU; "mean_geral_leverage" is general average of leverage and step function threshold; "sum_leverage" is accrued leverage on all resampling for each DMU; "count_dmu" is amount of each DMU was selected by bootstrap. "count_dmu_zero" is amount of each DMU was selected by bootstrap but it did not influence in others. "ycolumn" is the number of output variables; "xcolumn" is the number of input variables; "perc_sample_bubble" is the percentage of sample used in each bubble;"dea_method" is the model used in DEA analysis; "orientation_dea" is the orientation of DEA; ""bootstrap" is the amount of bubble used by jackstrap method; "type_obj" is type of object; "size_bubble" is the amount of DMU used in each bubble.

Examples

# NOT RUN {
 
# }
# NOT RUN {
 
# }
# NOT RUN {
    # Examples with the municipalities data.
    #Load package jackstrap
    library(jackstrap)

    #Load data example
    municipalities <- jackstrap::municipalities

    #Command measures efficiency with jackstrap method and heaviside criterion
    efficiency <- jackstrap (data=municipalities, ycolumn=2, xcolumn=1, bootstrap=1000,
                      perc_sample_bubble=0.20, dea_method="vrs", orientation_dea="in",
                      n_seed = 2000, repos=FALSE, num_cores=4)
 
# }