Title: Productivity and Efficiency Analysis using DEA
Version: 0.5.0
Description: Measure productivity and efficiency using Data Envelopment Analysis (DEA). Available methods include DEA under different technology assumptions, bootstrapping of efficiency scores and calculation of the Malmquist productivity index. Analyses can be performed either in the console or with the provided 'shiny' app. See Banker, R.; Charnes, A.; Cooper, W.W. (1984) <doi:10.1287/mnsc.30.9.1078>, Färe, R.; Grosskopf, S. (1996) <doi:10.1007/978-94-009-1816-0>.
License: GPL-3
URL: https://riksrevisjonen.github.io/pioneeR/, https://github.com/Riksrevisjonen/pioneeR
BugReports: https://github.com/Riksrevisjonen/pioneeR/issues
Depends: R (≥ 4.1.0), shiny (≥ 1.7.0)
Imports: bslib (≥ 0.6.0), bsicons, cli (≥ 3.6.0), ggplot2 (≥ 3.3.0), haven (≥ 2.5.0), htmltools, lpSolveAPI (≥ 5.5.2), markdown, reactable (≥ 0.4.0), readxl, rlang (≥ 1.1.0), scales, writexl
Suggests: deaR, rmarkdown, knitr, testthat (≥ 3.0.0), tibble, withr
Language: en-GB
Encoding: UTF-8
RoxygenNote: 7.3.2
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2024-10-22 20:00:20 UTC; ohj
Author: Ove Haugland Jakobsen [aut, cre], Aleksander Eilertsen Valberg [aut], Jan Roar Beckstrøm [ctb], Lars Skaage Engebretsen [ctb], Jonas Månsson [ctb], Joachim Sandnes [ctb], National Audit Office of Norway [cph]
Maintainer: Ove Haugland Jakobsen <ohj@mac.com>
Repository: CRAN
Date/Publication: 2024-10-25 07:40:07 UTC

Bootstrap a DEA model

Description

Run bootstrap on a DEA model to estimate bias corrected efficiency scores and confidence intervals.

Usage

bootstrap_dea(dea, alpha = 0.05, bw_rule = "ucv", iterations = 2000)

Arguments

dea

An object of class 'pioneer_dea' from compute_dea().

alpha

One minus the confidence level required. Default is 0.05.

bw_rule

A string with the type of bandwidth rule to be used, or a number with the bandwidth parameter. See details.

iterations

The number of bootstrap iterations to perform. Default is 2000.

Details

In order to bootstrap a DEA model, you must first create a model object using the compute_dea() function. Note that you currently can only bootstrap models using constant or variable returns to scale (RTS). If you try to bootstrap a model using another RTS, the bootstrap will fail with an error message.

The bandwidth argument can be set to either ucv for unbiased cross validation, silverman for the Silverman rule, or scott for the Scott rule. If you provide a number, this will be used directly as the bandwidth parameter h. This can be useful to replicate results where h is given, such as Simar & Wilson (1998). For most practical applications of the bootstrap, the default value of unbiased cross validation is sensible.

Value

A list of class pioneer_bootstrap.

See Also

compute_dea()

Examples

# Load example data
fare89 <- deaR::Electric_plants
# Estimate efficiency
mod <- compute_dea(
  data = fare89,
  input = c("Labor", "Fuel", "Capital"),
  output = "Output",
  id = "Plant",
)
# Run bootstrap. Reducing the number of iterations to save processing time
boot <- bootstrap_dea(mod, iterations = 100)
# Print results
print(boot)
# Get summary
summary(boot)

Compute DEA

Description

Solve an input or output oriented DEA model under constant (crs), variable (vrs), non-increasing (drs), or non-decreasing (irs) returns to scale.

Usage

compute_dea(
  data,
  input,
  output,
  id = NULL,
  rts = c("crs", "vrs", "drs", "irs"),
  orientation = c("in", "out"),
  super = FALSE,
  slack = FALSE,
  peers = FALSE
)

Arguments

data

Dataset to analyse.

input

A character vector with input variables.

output

A character vector with output variables.

id

Optional. A string with the DMU id or name variable. Defaults to the rownames of the dataset.

rts

Returns to scale.

crs Constant returns to scale, convexity and free disposability.
vrs Variable returns to scale, convexity and free disposability.
drs Decreasing returns to scale, convexity, down-scaling and free disposability.
irs Increasing returns to scale, (up-scaling, but not down-scaling), convexity and free disposability.
orientation

Model orientation.

super

If TRUE super efficiency scores are calculated.

slack

If TRUE slack values are calculated.

peers

If TRUE peers are added to the response.

Value

A list of class pioneer_dea.

Examples

# Load example data
fare89 <- deaR::Electric_plants
# Estimate efficiency
mod <- compute_dea(
  data = fare89,
  input = c("Labor", "Fuel", "Capital"),
  output = "Output",
  id = "Plant",
  rts = "vrs",
  orientation = "in"
)
# Print results
print(mod)
# Get summary
summary(mod)
# Convert to data frame
df <- as.data.frame(mod)

Compute Malmquist

Description

Calculate the Malmquist productivity index and its components using Data Envelopment Analysis.

Usage

compute_malmquist(data, input, output, id, time, orientation = c("in", "out"))

Arguments

data

Dataset to analyse.

input

A character vector with input variables.

output

A character vector with output variables.

id

A string with the DMU id or name variable.

time

A string with the time period variable.

orientation

Model orientation.

Details

Results are returned a la Farrell. This implies that for output-oriented models values above one signify improvements in productivity, while values less than one imply deterioration in productivity. For input-oriented models the interpretation is reversed; values less than one denote improvements and values above one denote deterioration.

Note that compute_malmquist() only works for balanced panel datasets.

Value

A list of class pioneer_mlm

References

Färe, R., Grosskopf, S. (1996). Intertemporal production frontiers: With dynamic DEA. Springer.

Examples

# Load example data
chnEconomy <- deaR::EconomyLong
# Estimate Malmquist
mod <- compute_malmquist(
  data = chnEconomy,
  id = 'DMUs',
  time = 'Period',
  input = c('Labor', 'Capital'),
  output = 'GIOV',
  orientation = 'in')
# Print results
print(mod)
# Get summary
summary(mod)
# Convert to data frame
df <- as.data.frame(mod)

Calculate scale efficiency

Description

Calculate scale efficiency from a set of inputs and outputs and return a data.frame

Usage

compute_scale_efficiency(x, y, orientation = c("in", "out"), digits = NULL)

Arguments

x

A matrix of inputs, created with create_matrix

y

A matrix of outputs, created with create_matrix

orientation

⁠in⁠ for input oriented models or out for output oriented

digits

An integer with the number of digits to round to. If NULL the values are kept unrounded.

Value

A data frame containing the efficiency scores for CRS, VRS, the Scale Efficiency, the VRS to NIRS ratio, and a recommendation on whether to increase or decrease the size of the DMU.

Examples

# Create matrices with random values
inputs <- matrix(runif(10, 1, 10), ncol = 2)
outputs <- matrix(runif(10, 1, 10), ncol = 2)
# Compute scale efficiency
res <- compute_scale_efficiency(
  inputs, outputs, orientation = 'out', digits = 2)

Create a matrix for input or output variables

Description

Create a matrix for input or output variables that can be used in DEA models from a supplied data.frame

Usage

create_matrix(df, columns, id, normalize = FALSE)

Arguments

df

A data.frame containing the data.

columns

A character vector of column names to include in the matrix.

id

A character string specifying the column with DMU IDs.

normalize

A logical indicating whether to normalize the columns by their mean. Defaults to FALSE.

Value

A matrix of inputs or outputs

Examples

df <- data.frame(id = 1:3, a = c(10, 20, 30), b = c(5, 15, 25))
create_matrix(df, columns = c("a", "b"), id = "id", normalize = TRUE)

Run pioneeR

Description

Run the pioneeR app on your local machine.

Usage

run_pioneer(x = NULL, port = NULL, ...)

Arguments

x

A data frame that should be loaded with the app. See details.

port

Integer. The TCP port that the application should listen on.

...

Other arguments to send to shiny::runApp().

Details

Note that pioneeR must be loaded into the namespace with library(pioneeR) before you run the pioneeR app.

You can load a data object in your current environment to the app. You can pass a data frame or a character string with the object name of the data frame you want to be loaded when the app launches. Note that you should only use data frame objects. If you have a tibble (from the tidyverse) or a data table, you can convert to an ordinary data.frame using as.data.frame()

Value

None

Examples

# Only run this example in interactive R sessions
if (interactive()) {
  df = deaR::Electric_plants
  # Load app with data.frame and set port to 8080
  run_pioneer(x = df, port = 8080)
}

Create a summary table for DEA

Description

Create a binned summary table for efficiency scores from a DEA model.

Usage

summary_tbl_dea(x)

Arguments

x

A numeric vector of efficiency scores or an object of class pioneer_dea

Details

The function will return a summary table for efficiency scores from a DEA model. Efficiency scores will be placed in 11 bins, where DMUs with an efficiency score equal to 1 are placed in a separate bin. For output oriented models with range [1, Inf], bins are created with 1/bin. Bin widths will be equal to models with range [0, 1].

Value

A data.frame() with summary statistics

Examples

# Load example data
fare89 <- deaR::Electric_plants
# Estimate efficiency
mod <- compute_dea(
  data = fare89,
  input = c("Labor", "Fuel", "Capital"),
  output = "Output",
  rts = "vrs"
)
# Get a summary table of efficiency scores
summary_tbl_dea(mod)
# You can also create the table from a numeric vector of efficiency scores
res <- as.data.frame(mod)
summary_tbl_dea(res$efficiency)

Unset environment variables

Description

Unsets the environment variables set by pioneeR

Usage

unset_env_vars()

Value

A logical vector, with elements being TRUE if unsetting the variable succeeded

Examples

unset_env_vars()