Package 'jackstrap'

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

efficiency	is the jackstrap object created by jackstrap function.
model_hist_ks	is the desired graphic model. There are four kinds: 1- Density Histogram of efficiency indicator with complete sample and without outliers by K-S test; 2 - Histogram of efficiency with complete sample and without outliers by K-S test; 3 - Histogram of efficiency without ouliers by K-S test; 4 - Histogram of efficiency with complete sample.

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)

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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

efficiency	is the jackstrap object created by jackstrap function.
model_hist_step	is the desired graphic model. There are four kinds: 1- Density Histogram of efficiency indicators with complete sample and without outliers by heaviside step function; 2 - Histogram of efficiency with complete sample and without outliers by heaviside step function; 3 - Histogram of efficiency without ouliers by heaviside step function; 4 - Histogram of efficiency with complete sample.

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)

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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

# 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)

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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

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.
jackstrap_obj	is the object created by the function jackstrap.
num_cores	is the number of cores available to process.
perc	is the percentage of DMU analyzed by K-S test.

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)

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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

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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

data_plot	is the jackstrap object created by jackstrap function.
model_plot	is the desired model. There are two models: 1 - The graphic shows the amount of removed DMU on x axis and p-value of K-S test on y axis; 2 - The graphic shows DMU code on x axis and p-value of K-S test on y axis.

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)

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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

object_jackstrap	is the jackstrap object created by jackstrap function.
data	is the dataset of research.

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)

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