Histogram with Jackstrap Efficiency Indicators: This function builds graphics with distributions of efficiency indicators without outliers and complete sample. The outliers are defined by K-S Test.

Description

Histogram with Jackstrap Efficiency Indicators: This function builds graphics with distributions of efficiency indicators without outliers and complete sample. The outliers are defined by K-S Test.

Usage

hist_jack_ks(efficiency, model_hist_ks)

Arguments

Value

Return the plot with efficiency indicators with complete sample and/or without outliers by combination leverage level and K-S test;

Examples

 
 
    #Build charts with efficiency indicators with jackstrap method and K-S test criterion
    hist_jack_ks(efficiency_ks, 1)
    hist_jack_ks(efficiency_ks, 2)
    hist_jack_ks(efficiency_ks, 3)
    hist_jack_ks(efficiency_ks, 4)
 

Histogram with Jackstrap Efficiency Indicators: This function builds a graphic with indicator distributions without outliers and complete sample. The outliers are defined by heaviside step function method.

Description

Histogram with Jackstrap Efficiency Indicators: This function builds a graphic with indicator distributions without outliers and complete sample. The outliers are defined by heaviside step function method.

Usage

hist_jack_step(efficiency, model_hist_step)

Arguments

Value

Return the plot with efficiency indicators with complete sample and/or without outliers by heaviside step function;

Examples

 
 
    #Build charts with efficiency indicators with jackstrap method and heaviside criterion
    hist_jack_step(efficiency, 1)
    hist_jack_step(efficiency, 2)
    hist_jack_step(efficiency, 3)
    hist_jack_step(efficiency, 4)
 

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

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

 
 
    # 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)
 

Jackstrap KS 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 Meansurements for Outliers and to use the K-S test with criterion to define outliers.

Description

Jackstrap KS 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 Meansurements for Outliers and to use the K-S test with criterion to define outliers.

Usage

jackstrap_ks(data, jackstrap_obj, num_cores = 1, perc = 0.9)

Arguments

Value

Return the jackstrap object increased with informations as follows: "result_kstest_method" is p-values of K-S test obtained by removing sequencially one by one the high leverage DMU; "efficiency_ks_method" is efficiency indicators obtained by K-S test criterion.

Examples

 
 
    #Command measures efficiency with jackstrap method and K-S test criterion
    efficiency_ks <- jackstrap_ks (data=municipalities, jackstrap_obj=efficiency,
                                   num_cores = 4)
 

Dataset of Municipalities of Bahia state in Brazil

Description

Dataset of Municipalities of Bahia state in Brazil

Usage

municipalities

Format

A data frame with 489 rows (DMUs) and 3 variables (2 outputs and 1 inputs):

municipio

string variable with descriptions of the each local governments

cod

integer variable identifies each DMU for integer code

total_atend_amb_hosp_ab

float variable with public health services in local governments (output)

total_diversid

float variable with diversity of public services provide in local governments (output)

desp_saude

float variable with public service expeditures in local governments (input)

Examples

 
    #Load data exemple
    municipalities <- jackstrap::municipalities
 

Plot Jackstrap KS: This function plots p-value of Kolmogorov-Smirnov Test in decreasing order of leverage.

Description

Plot Jackstrap KS: This function plots p-value of Kolmogorov-Smirnov Test in decreasing order of leverage.

Usage

plot_jackstrap_ks(data_plot, model_plot)

Arguments

Value

Return the plot with p-value of K-S test and removed DMU or DMU code.

Examples

 
 
   ##Plot the dispersion chart with p value of K-S test and amount of DMU removed.
   plot_jackstrap_ks(effic_ks, 1)
 

Summary Jackstrap: This function shows the main outcomes with outlier technique developed by Sousa and Stosic(2005).

Description

Summary Jackstrap: This function shows the main outcomes with outlier technique developed by Sousa and Stosic(2005).

Usage

summary_jackstrap(object_jackstrap, data)

Arguments

Value

Return the data frame with information as follows: "outliers_by_step_func" are the outliers by heaviside step function criterion; "outliers_by_ks" are the outliers by K-S test; "dmu_efficiency_by_step_func" are DMUs evaluated as efficient by heaviside step function criterion; "dmu_inefficiency_by_step_func" are the DMUs evaluated as maximum inefficient by heaviside step function criterion; "dmu_efficiency_ks" are DMUs evaluated as efficient by K-S test criterion; "dmu_inefficiency_by_ks" are the DMUs evaluated as maximum inefficient by K-S test criterion.

Examples

 
 
    #Create object with the resume of efficiency measurement.
    summary_efficiency <- summary_jackstrap(efficiency_ks, municipalities)