From 8ea62d2efbe7ea29ee23ddd2e60b203950e05450 Mon Sep 17 00:00:00 2001 From: Aleksander Eilertsen Date: Mon, 15 Apr 2024 14:58:06 +0200 Subject: [PATCH] Add custom functions for DEA calculations (#82) Add custom functions for DEA calculations Add custom functions for efficiency, super efficiency, slack and peer calculations. This removes Benchmarking as a hard dependency. The commit implies some minor changes in the slack table shown in the Analyse tab, but should otherwise not effect the UI. Includes: * New exported function compute_dea() * New internal function compute_efficiency() * New internal function compute_super_efficiency() * New internal function compute_slack() * New internal function get_peers() * New test datasets and extensive unit tests for these functions * Bug fix for rounding error in the UI Closes #80 Closes #84 --------- Co-authored-by: Ove Haugland Jakobsen --- .Rbuildignore | 1 + DESCRIPTION | 18 +- NAMESPACE | 2 + NEWS.md | 11 +- R/compute_dea.R | 91 +++++ R/dea-methods.R | 182 +++++++++ R/dea-utils.R | 101 ++++- R/lp-utils.R | 330 ++++++++++++++++ R/summary-dea.R | 7 + ...fct_dea-boostrap.R => fct_dea-bootstrap.R} | 4 +- inst/app/dea_analysis.Rmd | 41 +- inst/app/global.R | 1 + inst/app/server.R | 77 ++-- man/compute_dea.Rd | 49 +++ tests/script/create-testdata-benchmarking.R | 89 +++++ tests/testdata/benchmarking-results.RDS | Bin 0 -> 1268203 bytes tests/testdata/electricPlants.RDS | Bin 0 -> 652 bytes tests/testdata/frontier41.RDS | Bin 0 -> 1208 bytes tests/testdata/hospitals.RDS | Bin 0 -> 18268 bytes tests/testdata/norCourts2018.RDS | Bin 0 -> 2136 bytes tests/testthat/test-compute_efficiency.R | 343 +++++++++++++++++ .../testthat/test-compute_scale_efficiency.R | 27 ++ tests/testthat/test-compute_slack.R | 357 ++++++++++++++++++ .../testthat/test-compute_super_efficiency.R | 291 ++++++++++++++ tests/testthat/test-dea-utils.R | 46 ++- tests/testthat/test-get_peers.r | 68 ++++ tests/testthat/tests-lp-utils.R | 81 ++++ 27 files changed, 2118 insertions(+), 99 deletions(-) create mode 100644 R/compute_dea.R create mode 100644 R/dea-methods.R create mode 100644 R/lp-utils.R rename inst/app/R/{fct_dea-boostrap.R => fct_dea-bootstrap.R} (89%) create mode 100644 man/compute_dea.Rd create mode 100644 tests/script/create-testdata-benchmarking.R create mode 100644 tests/testdata/benchmarking-results.RDS create mode 100644 tests/testdata/electricPlants.RDS create mode 100644 tests/testdata/frontier41.RDS create mode 100644 tests/testdata/hospitals.RDS create mode 100644 tests/testdata/norCourts2018.RDS create mode 100644 tests/testthat/test-compute_efficiency.R create mode 100644 tests/testthat/test-compute_scale_efficiency.R create mode 100644 tests/testthat/test-compute_slack.R create mode 100644 tests/testthat/test-compute_super_efficiency.R create mode 100644 tests/testthat/test-get_peers.r create mode 100644 tests/testthat/tests-lp-utils.R diff --git a/.Rbuildignore b/.Rbuildignore index 823e4f0..8247b0a 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -11,3 +11,4 @@ dev ^CONTRIBUTING.md$ ^DOCKERFILE$ ^README.Rmd$ +^tests/script diff --git a/DESCRIPTION b/DESCRIPTION index bbdf720..8fc6d1c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -3,13 +3,13 @@ Title: Perform productivity analyses in a web browser Version: 0.3.0.9000 Authors@R: c( person("Ove Haugland", "Jakobsen", role = c("aut", "cre"), email = "ohj@mac.com"), + person("Aleksander", "Eilertsen", role = "aut", email = "ale@riksrevisjonen.no"), person("Jan Roar", "Beckstrøm", role = "ctb", email = "jrb@riksrevisjonen.no"), person("Lars Skaage", "Engebretsen", role = "ctb", email = "lse@riksrevisjonen.no"), person("Jonas", "Månsson", role = "ctb", email = "jonas.mansson@riksrevisjonen.no"), - person("Aleksander", "Eilertsen", role = "ctb", email = "ale@riksrevisjonen.no"), person("Joachim", "Sandnes", role = "ctb", email = "jsn@riksrevisjonen.no"), person(family = "Riksrevisjonen", role = "cph")) -Description: What the package does (one paragraph). +Description: Application to conduct productivity and efficiency (DEA) analysis. License: GPL-3 | file LICENSE URL: https://riksrevisjonen.github.io/pioneeR/, https://github.com/Riksrevisjonen/pioneeR BugReports: https://github.com/Riksrevisjonen/pioneeR/issues @@ -17,7 +17,7 @@ Depends: R (>= 4.1.0), shiny (>= 1.7.0) Imports: - Benchmarking (>= 0.30), + lpSolveAPI (>= 5.5.2), haven (>= 2.5.0), markdown, productivity (>= 1.1.0), @@ -25,22 +25,22 @@ Imports: writexl, bslib (>= 0.5.1), ggplot2 (>= 3.3.0), - reactable (>= 0.4.0) + reactable (>= 0.4.0), + cli (>= 3.6.0) Suggests: - cli, + Benchmarking (>= 0.30), data.table, - lpSolve, - lpSolveAPI, rmarkdown, knitr, scales, rlang, testthat (>= 3.0.0), - tibble + tibble, + withr Language: en-GB Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.1 VignetteBuilder: knitr Config/testthat/edition: 3 diff --git a/NAMESPACE b/NAMESPACE index e702b5c..a0a84f4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,6 +2,8 @@ S3method(summary_tbl_dea,Farrell) S3method(summary_tbl_dea,numeric) +S3method(summary_tbl_dea,pioneer_dea) +export(compute_dea) export(compute_scale_efficiency) export(create_matrix) export(runPioneeR) diff --git a/NEWS.md b/NEWS.md index 33f40ca..d26c1d2 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,11 +1,18 @@ # pioneeR 0.3.0.9000 -- Fixed bug where checkbox for timeseries would be available before any data is uploaded (#74) +## Changes & improvements + - Updated documentation for `run_pioneer()` +- Added custom functions for DEA calculations in order to remove `{Benchmarking}` as a dependency (#80) + +## Bug fixes + +- Fixed bug where checkbox for timeseries would be available before any data is uploaded (#74) +- Fixed a few bugs where the UI did not correctly reflect the user supplied input for rounding (#84) # pioneeR 0.3.0 -## Breaking changes: +## Breaking changes - pioneeR now depends on bslib 0.5.1 or higher. Version 0.6.0 or higher is recommended, and support for version 0.5.1 will be deprecated in the next release diff --git a/R/compute_dea.R b/R/compute_dea.R new file mode 100644 index 0000000..26814ef --- /dev/null +++ b/R/compute_dea.R @@ -0,0 +1,91 @@ +#' Compute DEA +#' +#' DEA analysis. +#' +#' @param data Dataset to analyse. +#' @param id A string with the DMU id or name variable. +#' @param input A character vector with input variables. +#' @param output A character vector with output variables. +#' @param rts Returns to scale. +#' \tabular{rlll}{ +#' \tab crs \tab\tab Constant returns to scale, convexity and free disposability. \cr +#' \tab vrs \tab\tab Variable returns to scale, convexity and free disposability. \cr +#' \tab drs \tab\tab Decreasing returns to scale, convexity, down-scaling and free disposability. \cr +#' \tab irs \tab\tab Increasing returns to scale, (up-scaling, but not down-scaling), convexity and free disposability. +#' } +#' @param orientation Model orientation. +#' @param super If `TRUE` super efficiency scores are calculated. +#' @param slack If `TRUE` slack values are calculated. +#' @param peers If `TRUE` peers are added to the response. +#' @return list +#' @export +compute_dea <- function( + data, + id, + input, + output, + rts = c('crs', 'vrs', 'drs', 'irs'), + orientation = c('in', 'out'), + super = FALSE, + slack = FALSE, + peers = FALSE) { + + rts <- match.arg(rts) + orientation <- match.arg(orientation) + + # Prepare and validate input + x <- as.matrix(data[input]) + y <- as.matrix(data[output]) + ids <- data[[id]] + check_data(x, y) + + # Calculate DEA metrics + res <- compute_dea_( + x, y, ids, + rts = rts, + orientation = orientation, + super = super, + slack = slack, + peers = peers + ) + + res + +} + +#' compute_dea_ (internal helper) +#' @inheritParams compute_dea +#' @inheritParams compute_efficiency +#' @return list +#' @noRd +compute_dea_ <- function(x, y, ids, rts, orientation, super, slack, peers) { + + # Set initial values + super_res <- NULL + slack_res <- NULL + peers_res <- NULL + + eff_res <- compute_efficiency(x, y, rts = rts, orientation = orientation) + if (super) super_res <- compute_super_efficiency(x, y, rts = rts, orientation = orientation) + if (slack) slack_res <- compute_slack(x, y, eff_res) + if (peers) { + peers_res <- get_peers(eff_res, ids) + peers_res <- peers_res[2:ncol(peers_res)] + } + + res <- list( + efficiency = eff_res$values, + super_efficiency = super_res$values, + slack = slack_res$values + ) + res <- do.call('cbind', res) + if (peers) res <- cbind(res, peers_res) + + out <- structure( + list( + results = data.frame(dmu = ids, res), + info = eff_res$info + ), class = 'pioneer_dea' + ) + +} diff --git a/R/dea-methods.R b/R/dea-methods.R new file mode 100644 index 0000000..90fd5c3 --- /dev/null +++ b/R/dea-methods.R @@ -0,0 +1,182 @@ +#' Compute efficiency +#' +#' Compute DEA efficiency scores. +#' +#' @inheritParams compute_dea +#' @param x A matrix with input values. +#' @param y A matrix with output values. +#' @param xref A matrix with input values for the reference technology. Defaults to `NULL`. +#' @param yref A matrix with output values for the reference technology. Defaults to `NULL`. +#' @param values_only Only return the calculated values. +#' @return list +#' @noRd +compute_efficiency <- function( + x, y, + xref = NULL, + yref = NULL, + rts, + orientation, + values_only = FALSE) { + # Get dims + dims <- get_dims(x, y, xref, yref, rts = rts, slack = FALSE) + # Create linear program model + lp <- create_lp( + x = x, y = y, + xref = xref, yref = yref, + rts = rts, + orientation = orientation, + m = dims$n_inputs, + n = dims$n_outputs, + n_units = dims$n_units, + n_constraints = dims$n_constraints, + n_vars = dims$n_vars, + slack = FALSE) + # Solve model + res <- solve_lp( + lp, x, y, + orientation = orientation, + m = dims$n_inputs, + n = dims$n_outputs) + # Adjust values + res <- adjust_values(res$values, res$lambda, res$eps) + # Return + res <- create_dea_output( + res, + rts = rts, + orientation = orientation, + dims = dims, + values_only = values_only) + res +} + +#' Compute super efficiency +#' +#' Compute DEA super efficiency scores. +#' +#' @inheritParams compute_efficiency +#' @noRd +compute_super_efficiency <- function( + x, y, + rts, + orientation) { + dims <- get_dims(x, y, rts = rts, slack = FALSE) + lambda <- matrix(0, dims$n_units, dims$n_units) + values <- rep(NA_real_, dims$n_units) + for (i in seq_len(dims$n_units)) { + # Create linear program model + lp <- create_lp( + x[-i,,drop=FALSE], + y[-i,,drop=FALSE], + rts = rts, + orientation = orientation, + m = dims$n_inputs, + n = dims$n_outputs, + n_units = dims$n_units - 1, + n_constraints = dims$n_constraints, + n_vars = dims$n_vars - 1, + slack = FALSE) + # Solve model + tmp <- solve_lp_single( + x[i,,drop=FALSE], + y[i,,drop=FALSE], + lp = lp, + orientation = orientation, + m = dims$n_inputs, + n = dims$n_outputs)[[1]] + values[i] <- tmp$value + lambda[i,-i] <- tmp$solution + } + eps <- lpSolveAPI::lp.control(lp)$epsilon['epsint'] + res <- adjust_values(values, lambda, eps) + # Return + res <- create_dea_output( + res, + rts = rts, + orientation = orientation, + dims = dims, + values_only = FALSE) + res +} + +#' Compute slack +#' +#' Compute slack for a DEA efficiency analysis. +#' +#' @inheritParams compute_efficiency +#' @param model Output of `compute_efficiency()`. +#' @return list +#' @noRd +compute_slack <- function(x, y, model) { + # Get dims + dims <- get_dims(x, y, rts = model$info$rts, slack = TRUE) + # Scale inputs and outputs if needed + scale <- scale_vars( + x, y, + m = dims$n_inputs, + n = dims$n_outputs, + n_units = dims$n_units) + # Create linear program model + lp <- create_lp( + scale$x, scale$y, + rts = model$info$rts, + orientation = model$info$orientation, + m = dims$n_inputs, + n = dims$n_outputs, + n_units = dims$n_units, + n_constraints = dims$n_constraints, + n_vars = dims$n_vars, + n_lambda = dims$n_lambda, + slack = TRUE) + # Solve model + res <- solve_lp_slack( + lp, + scale$x, scale$y, + values = model$unadj_values, # slack is calculated based on unadjusted eff.scores + orientation = model$info$orientation, + m = dims$n_inputs, + n = dims$n_outputs, + n_units = dims$n_units, + n_vars = dims$n_vars) + # Adjust values + res <- adjust_slack( + lp, + values = res$values, + sx = res$sx, + sy = res$sy, + lambda = res$lambda, + scaling = scale, + m = dims$n_inputs, + n = dims$n_outputs, + n_units = dims$n_units) + # Return + res <- create_dea_output( + res, + rts = model$info$rts, + orientation = model$info$orientation, + dims = dims, + values_only = FALSE) + res +} + +#' Get peers +#' +#' Get peers for each DMU. +#' +#' @param model Output of `compute_efficiency()`. +#' @param ids A vector with DMU names or ids. +#' @param threshold Minimum weight for extracted peers. Defaults to 0. +#' @return data.frame +#' @noRd +get_peers <- function(model, ids, threshold = 0) { + lambda <- model$lambda + pt_ <- apply(lambda, 1, function(x) {which(x > threshold)}) + if (dim(lambda)[1] == 1) pt_ <- list(c(pt_)) # Only 1 DMU + bench <- t(mapply(function(x) x[1:max(sapply(pt_, length))], pt_)) + maxp <- max(sapply(pt_, length)) + if (maxp == 0 | is.na(maxp)) maxp <- 1 + dim(bench) <- c(dim(lambda)[1], maxp) + bench <- matrix(ids[bench], nrow=nrow(bench)) + colnames(bench) <- paste0('peer', 1:ncol(bench)) + bench <- data.frame(cbind(dmu = ids, bench)) + bench +} diff --git a/R/dea-utils.R b/R/dea-utils.R index 9dd30a9..36142fa 100644 --- a/R/dea-utils.R +++ b/R/dea-utils.R @@ -42,8 +42,7 @@ compute_scale_efficiency <- function( x, y, orientation = c('in', 'out'), - digits = NULL) -{ + digits = NULL) { if (!is.matrix(x)) { cli::cli_abort('inputs must be a matrix of input values') @@ -71,26 +70,26 @@ compute_scale_efficiency <- function( orientation <- match.arg(orientation) # Run DEA models - crs_mod <- Benchmarking::dea(x, y, RTS = 'crs', ORIENTATION = orientation) - vrs_mod <- Benchmarking::dea(x, y, RTS = 'vrs', ORIENTATION = orientation) - nirs_mod <- Benchmarking::dea(x, y, RTS = 'drs', ORIENTATION = orientation) + crs_mod <- compute_efficiency(x, y, rts = 'crs', orientation = orientation, values_only = TRUE)$values + vrs_mod <- compute_efficiency(x, y, rts = 'vrs', orientation = orientation, values_only = TRUE)$values + nirs_mod <- compute_efficiency(x, y, rts = 'drs', orientation = orientation, values_only = TRUE)$values # If efficiency scores for a unit differs in the CRS and VRS models and the ratio # of the NIRS and VRS models is equal to 1, the unit should decrease its size. When # the CRS and VRS models differ, and the ratio of the NIRS and VRS models is *not* # equal to 1, the unit should increase its size. - equal_crs_vrs <- round(crs_mod$eff, 6L) == round(vrs_mod$eff, 6L) + equal_crs_vrs <- round(crs_mod, 6L) == round(vrs_mod, 6L) optimal_scale <- ifelse( !equal_crs_vrs, - ifelse(vrs_mod$eff / nirs_mod$eff == 1, 'Decrease', 'Increase'), + ifelse(vrs_mod / nirs_mod == 1, 'Decrease', 'Increase'), '-' ) out_mod <- data.frame( - 'CRS' = crs_mod$eff, - 'VRS' = vrs_mod$eff, - 'Scale.eff.' = crs_mod$eff / vrs_mod$eff, - 'VRS.NIRS.ratio' = vrs_mod$eff / nirs_mod$eff + 'CRS' = crs_mod, + 'VRS' = vrs_mod, + 'Scale.eff.' = crs_mod / vrs_mod, + 'VRS.NIRS.ratio' = vrs_mod / nirs_mod ) if (!is.null(digits) || is.numeric(digits)) { @@ -107,3 +106,83 @@ compute_scale_efficiency <- function( out_mod } + +#' check_data +#' @noRd +check_data <- function(x, y, xref = NULL, yref = NULL) { + check_numeric(x, y) + check_nunits(x, y) + if (!is.null(xref) || !is.null(yref)) { + if (ncol(xref) != ncol(x)) { + msg <- c('Inconsistent number of input variables:' , + 'x' = "You've supplied are {ncol(x)} column(s) for `x` and {ncol(xref)} column(s) for `xref`.") + cli::cli_abort(msg) + } + if (ncol(yref) != ncol(y)) { + msg <- c('Inconsistent number of output variables:' , + 'x' = "You've supplied are {ncol(y)} column(s) for `y` and {ncol(yref)} column(s) for `yref`.") + cli::cli_abort(msg) + } + check_numeric(xref, yref) + check_nunits(xref, yref) + } +} + +#' check_data +#' @noRd +check_numeric <- function(x, y) { + chk <- apply(cbind(x, y), 2, is.numeric) + if (any(!chk)) { + cli::cli_abort('All variables must be numeric.') + } +} + +#' check_data +#' @noRd +check_nunits <- function(x, y, ref = FALSE) { + nx <- nrow(x) + ny <- nrow(y) + if (nx != ny) { + if (ref) { + msg <- "You've supplied {nrows(x)} rows for `xref` and {nrow(y)} column(s) for `yref`." + } else { + msg <- "You've supplied {nrows(x)} rows for `x` and {nrow(y)} column(s) for `y`." + } + cli::cli_abort(c('Inconsistent number of units: ', 'x' = msg)) + } +} + +#' create_dea_output +#' @noRd +create_dea_output <- function(res, rts, orientation, dims, values_only) { + if (values_only) { + res <- list(values = res$values) + } else { + res$info <- create_model_info(rts, orientation, dims) + } + res +} + +#' create_model_info +#' @noRd +create_model_info <- function(rts, orientation, dims) { + list( + rts = rts, + orientation = orientation, + dims = dims + ) +} + +#' round_numeric +#' @noRd +round_numeric <- function(df, digits = 4L) { + df[] <- lapply(df, function(x) if(is.numeric(x)) round(x, digits) else x) + df +} + +#' pioneer_dea print method +#' @noRd +print.pioneer_dea <- function(x, ...) { + cat("DEA result:\n") + utils::str(x) +} diff --git a/R/lp-utils.R b/R/lp-utils.R new file mode 100644 index 0000000..366fdd3 --- /dev/null +++ b/R/lp-utils.R @@ -0,0 +1,330 @@ +#' Create linear program +#' @inheritParams compute_efficiency +#' @param m Number of inputs. +#' @param n Number of outputs. +#' @param n_units Number of units (DMUs). +#' @param n_constraints Number of constraints. +#' @param n_vars Number of decision variables. +#' @param n_lambda Number of lambda variables. +#' @param slack If `TRUE` creates a model for slack analysis. +#' @noRd +create_lp <- function(x, y, + xref = NULL, + yref = NULL, + rts, + orientation, + m, n, + n_units, + n_constraints, + n_vars, + n_lambda = NULL, + slack = FALSE) { + if (is.null(xref) && is.null(yref)) { + lp <- create_lp_( + x, y, + rts = rts, + orientation = orientation, + m = m, + n = n, + n_units = n_units, + n_constraints = n_constraints, + n_vars = n_vars, + n_lambda = n_lambda, + slack = slack) + } else { + lp <- create_lp_( + xref, yref, + rts = rts, + orientation = orientation, + m = m, + n = n, + n_units = n_units, + n_constraints = n_constraints, + n_vars = n_vars, + n_lambda = n_lambda, + slack = slack) + } + lp +} + +#' Create linear program +#' @inheritParams create_lp +#' @noRd +create_lp_ <- function(x, y, rts, orientation, m, n, n_units, n_constraints, + n_vars, n_lambda, slack) { + lp <- lpSolveAPI::make.lp(n_constraints, n_vars) + if (slack) { + lpSolveAPI::set.objfn(lp, rep(1, m + n), 1:(m + n)) + lpSolveAPI::set.constr.type(lp, c(rep('=', m + n), rep('<=', n_lambda))) + lpSolveAPI::lp.control(lp, sense='max') + } else { + lpSolveAPI::set.objfn(lp, 1, 1) + lpSolveAPI::set.constr.type(lp, rep('>=', n_constraints)) + if (orientation == 'in') lpSolveAPI::lp.control(lp, sense = 'min') + if (orientation == 'out') lpSolveAPI::lp.control(lp, sense = 'max') + } + lpSolveAPI::lp.control(lp, scaling = c('range', 'equilibrate', 'integers')) + lp <- set_lp_constraints( + lp, x, y, rts, m = m, n = n, n_units = n_units, + n_vars = n_vars, slack = slack) + lp +} + +#' set_lp_constraints +#' @param lp An lpSolve linear program model object. +#' @inheritParams create_lp +#' @noRd +set_lp_constraints <- function(lp, x, y, rts, m, n, n_units, n_vars, slack) { + if (slack) { + l1 <- m+n+1 + # Set restrictions on matrix + for (h in seq_len(m)) lpSolveAPI::set.row(lp, h, x[,h], l1:n_vars) # (m+n+1):(m+n+n_units) + for (h in seq_len(n)) lpSolveAPI::set.row(lp, m+h, -y[,h], l1:n_vars) # (m+n+1):(m+n+n_units) + for (h in seq_len(m+n)) lpSolveAPI::set.mat(lp, h, h, 1) + } else { + # Set restrictions on input/output + for (h in seq_len(m)) lpSolveAPI::set.row(lp, h, c(0, -x[,h])) # inputs + for (h in seq_len(n)) lpSolveAPI::set.row(lp, m+h, c(0, y[,h])) # outputs + } + lp <- set_lambda_constraints( + lp, rts = rts, m = m, n = n, + n_units = n_units, slack = slack) + lp +} + +#' set_lambda_constraints +#' @noRd +set_lambda_constraints <- function(lp, rts, m, n, n_units, slack){ + if (slack) q <- rep(0, m+n) else q <- 0 # Set number of starting zeros + if (rts != 'crs') { + l1 <- m+n+1; l2 <- m+n+2 + if (rts == 'vrs') { + lpSolveAPI::set.row(lp, l1, c(q, rep(-1, n_units))) + lpSolveAPI::set.row(lp, l2, c(q, rep(1, n_units))) + lpSolveAPI::set.rhs(lp, c(-1, 1), l1:l2) + } else if (rts == 'irs') { + lpSolveAPI::set.row(lp, l1, c(q, rep(1, n_units))) + lpSolveAPI::set.rhs(lp, 1, l1) + if (slack) lpSolveAPI::set.constr.type(lp, '>=', l1) + } else if (rts == 'drs') { + lpSolveAPI::set.row(lp, l1, c(q, rep(-1, n_units))) + lpSolveAPI::set.rhs(lp, -1, l1) + if (slack) lpSolveAPI::set.constr.type(lp, '>=', l1) + } + } + lp +} + +#' solve_lp +#' @inheritParams create_lp +#' @noRd +solve_lp <- function(lp, x, y, orientation, m, n) { + # Solve LP for each row + dl <- mapply(\(x, y) { + solve_lp_single(x, y, lp, orientation, m = m, n = n) + }, x = asplit(x, 1), y = asplit(y, 1)) + values <- vapply(dl, \(x) x$value, FUN.VALUE = numeric(1)) + lambda <- do.call('rbind', lapply(dl, \(x) x$solution)) |> as.matrix() + eps <- lpSolveAPI::lp.control(lp)$epsilon['epsint'] + list( + values = values, + lambda = lambda, + eps = eps + ) +} + +#' solve_lp_single +#' @noRd +solve_lp_single <- function(x, y, lp, orientation, m, n){ + if (orientation == 'in') { + lpSolveAPI::set.column(lp, 1, c(1, x), 0:m) + lpSolveAPI::set.rhs(lp, y, (m + 1):(m + n)) + } else { + lpSolveAPI::set.column(lp, 1, c(1, -y), c(0, (m + 1):(m + n))) + lpSolveAPI::set.rhs(lp, -x, 1:m) + } + lpSolveAPI::set.basis(lp, default = TRUE) + status <- lpSolveAPI::solve.lpExtPtr(lp) + value <- get_lp_objective(lp, status, orientation) + solution <- get_lp_solution(lp, status)[-1] + list(list(value = value, solution = solution)) +} + +#' solve_lp_slack +#' @inheritParams create_lp +#' @noRd +solve_lp_slack <- function(lp, x, y, values, orientation, m, n, n_units, n_vars) { + if (orientation == 'in') { + e_values <- values + f_values <- rep(1, n_units) + } else if (orientation == 'out') { + f_values <- values + e_values <- rep(1, n_units) + } + rhs <- cbind(e_values * x, -f_values * y) + mn <- m+n + mn1 <- mn+1 + values <- rep(NA_real_, n_units) + sx <- matrix(NA_real_, n_units, m) + sy <- matrix(NA_real_, n_units, n) + lambda <- matrix(NA_real_, n_units, n_units) + for (i in seq_len(n_units)) { + lpSolveAPI::set.rhs(lp, rhs[i,], 1:mn) + lpSolveAPI::set.basis(lp, default=TRUE) + status <- solve(lp) + if (status != 0) { + if (status == 2 || status == 3) { + values[i] <- 0 + solution <- rep(0, n_vars) #m+n+Kr + solution[mn+i] <- 1 + } else { + values[i] <- NA_real_ + solution <- NA_real_ + } + } else { + values[i] <- lpSolveAPI::get.objective(lp) + solution <- lpSolveAPI::get.variables(lp) + } + sx[i,] <- solution[1:m] + sy[i,] <- solution[(m+1):mn] + lambda[i,] <- solution[mn1:n_vars] + } + colnames(sx) <- paste('sx',1:m,sep='') + colnames(sy) <- paste('sy',1:n,sep='') + list( + lp = lp, + values = values, + sx = sx, + sy = sy, + lambda = lambda + ) +} + +#' scale_vars (for slack analysis) +#' @inheritParams create_lp +#' @noRd +scale_vars <- function(x, y, m, n, n_units) { + mmm <- colMeans(x) + nnn <- colMeans(y) + if (min(mmm) < 1e-4 || max(mmm) > 1e4 || + min(nnn) < 1e-4 || max(nnn) > 1e4) { + x <- x / matrix(mmm, nrow=n_units, ncol=m, byrow=TRUE) + y <- y / matrix(nnn, nrow=n_units, ncol=n, byrow=TRUE) + scale <- TRUE + } else { + scale = FALSE + } + list(x=x, y=y, mmm = mmm, nnn = nnn, scale = scale) +} + +#' adjust_values +#' @noRd +adjust_values <- function(unadj_values, lambda, eps) { + # efficiency scores close to 1 are converted to 1 + values <- adjust_efficiency(unadj_values, eps) + # lambda values close to 0 and 1 are converted to 0 and 1 respectively + lambda <- adjust_lambda(lambda, eps) + list( + values = values, + unadj_values = unadj_values, + lambda = lambda) +} + +#' adjust_slack +#' @noRd +adjust_slack <- function(lp, values, sx, sy, lambda, scaling, m, n, n_units){ + lpcontr <- lpSolveAPI::lp.control(lp) + eps <- lpcontr$epsilon['epsint'] + lambda <- adjust_lambda(lambda, eps) + sx[abs(sx) < eps] <- 0 + sy[abs(sy) < eps] <- 0 + if (scaling$scale) { + sx <- sx * matrix(scaling$mmm, nrow=n_units, ncol=m, byrow=TRUE) + sy <- sy * matrix(scaling$nnn, nrow=n_units, ncol=n, byrow=TRUE) + } + sum <- rowSums(sx) + rowSums(sy) + is_slack <- sum > eps + list(values = sum, + unadj_values = values, + lambda = lambda, + data = data.frame(sum = sum, is_slack, sx, sy) + ) +} + +#' adjust_efficiency +#' @noRd +adjust_efficiency <- function(x, eps){ + x[abs(x-1) < eps] <- 1 + x +} + +#' adjust_lambda +#' @noRd +adjust_lambda <- function(x, eps) { + x[abs(x-1) < eps] <- 1 # 'lambda' close to 1 should be 1 + x[abs(x) < eps] <- 0 # 'lambda' close to 0 should be 0 + x +} + +#' Get model dimensions +#' @inheritParams create_lp +#' @noRd +get_dims <- function(x, y, xref = NULL, yref = NULL, rts, slack = FALSE) { + n_inputs <- ncol(x) + n_outputs <- ncol(y) + n_units <- if (!is.null(xref)) nrow(xref) else nrow(x) + if (rts == 'crs') { + n_lambda <- 0 + } else if (rts %in% c('irs', 'drs')) { + n_lambda <- 1 + } else { + n_lambda <- 2 + } + if (slack) { + n_vars <- n_inputs + n_outputs + n_units + } else { + n_vars <- 1 + n_units + } + list( + n_inputs = n_inputs, + n_outputs = n_outputs, + n_units = n_units, + n_constraints = n_inputs + n_outputs + n_lambda, + n_vars = n_vars, + n_lambda = n_lambda + ) +} + +#' get_invalid_objective +#' @noRd +get_invalid_objective <- function(status, orientation) { + if (status != 0) { + if (status == 2 || status == 3) { + # if the unit is not in the technology set the value to Inf + if (orientation == 'in') return(Inf) else return(-Inf) + } else { + return(NA_real_) + } + } +} + +#' get_lp_objective +#' @noRd +get_lp_objective <- function(lp, status, orientation) { + if (status != 0) { + get_invalid_objective(status, orientation) + } else { + lpSolveAPI::get.objective(lp) + } +} + +#' get_lp_solution +#' @noRd +get_lp_solution <- function(lp, status) { + if (status != 0) { + n <- lpSolveAPI::dim.lpExtPtr(lp)[2] + rep(NA_real_, n) + } else { + lpSolveAPI::get.variables(lp) + } +} diff --git a/R/summary-dea.R b/R/summary-dea.R index 5e8b8bf..210d507 100644 --- a/R/summary-dea.R +++ b/R/summary-dea.R @@ -25,6 +25,13 @@ summary_tbl_dea.Farrell <- function(x) { summary_tbl_dea(eff) } +#' @method summary_tbl_dea pioneer_dea +#' @export +summary_tbl_dea.pioneer_dea <- function(x) { + eff <- x$results$efficiency + summary_tbl_dea(eff) +} + #' @method summary_tbl_dea numeric #' @export summary_tbl_dea.numeric <- function(x) { diff --git a/inst/app/R/fct_dea-boostrap.R b/inst/app/R/fct_dea-bootstrap.R similarity index 89% rename from inst/app/R/fct_dea-boostrap.R rename to inst/app/R/fct_dea-bootstrap.R index 264274f..81b110a 100644 --- a/inst/app/R/fct_dea-boostrap.R +++ b/inst/app/R/fct_dea-bootstrap.R @@ -38,11 +38,11 @@ perform_boot <- function(x, y, rts, orientation, i, h, theta, boot) { beta <- bootstrap_sample(theta, h = h) if (orientation == 'in') { x_ref <- (theta / beta) * x - boot[, i] <- Benchmarking::dea(x, y, RTS = rts, ORIENTATION = orientation, XREF = x_ref, FAST = TRUE) + boot[, i] <- pioneeR:::compute_efficiency(x, y, rts = rts, orientation = orientation, xref = x_ref, yref = y, values_only = TRUE)$values } else if (orientation == 'out') { beta <- 1 / beta y_ref <- (theta / beta) * y - boot[, i] <- Benchmarking::dea(x, y, RTS = rts, ORIENTATION = orientation, YREF = y_ref, FAST = TRUE) + boot[, i] <- pioneeR:::compute_efficiency(x, y, rts = rts, orientation = orientation, xref = x, yref = y_ref, values_only = TRUE)$values } return(invisible(boot)) } diff --git a/inst/app/dea_analysis.Rmd b/inst/app/dea_analysis.Rmd index 80ec932..24ce93f 100644 --- a/inst/app/dea_analysis.Rmd +++ b/inst/app/dea_analysis.Rmd @@ -64,12 +64,12 @@ knitr::kable(df) The technology for the model is `r mod$rts` and the orientation is `r mod$orient`. ```{r, echo=FALSE} -tbl <- summary_tbl_dea(params$model$eff) +tbl <- summary_tbl_dea(params$model$values) knitr::kable(tbl) ``` ```{r, echo=FALSE} -x <- summary(params$model$eff) +x <- summary(params$model$values) x <- data.frame(as.list(x)) colnames(x) <- gsub('^X', '', colnames(x)) knitr::kable(x) @@ -81,7 +81,7 @@ The efficiency scores have been estimated using the `dea` function from the `Ben ```{r, echo=FALSE} d <- data.frame( - `Efficiency score` = round(params$model$eff, params$settings$digits), + `Efficiency score` = round(params$model$values, params$settings$digits), check.names = FALSE ) knitr::kable(d) @@ -173,26 +173,35 @@ idvar <- params$params$id inputs <- c(params$inputs) outputs <- c(params$outputs) -x <- create_matrix(df, inputs, idvar, normalize = params$params$normalize) -y <- create_matrix(df, outputs, idvar, normalize = params$params$normalize) - # Estimate the DEA model -model <- Benchmarking::dea( - x, y, RTS = rts, ORIENTATION = orientation +model <- compute_dea( + df, + idvar, + inputs, + outputs, + rts = rts, + orientation = orientation ) # Print out the summary of the DEA model summary_tbl_dea(model) -# Summary statistics for the efficiency scores -summary(model$eff) - -# Estimate slack for all DMUs using the DEA model -slack <- Benchmarking::slack(x, y, model) +# Estimate the model with slack and super efficiency +model <- compute_dea( + df, + idvar, + inputs, + outputs, + rts = rts, + orientation = orientation, + slack = TRUE, + super = TRUE +) -# Estimate super efficiency -super_eff <- Benchmarking::sdea(x, y, RTS = rts, ORIENTATION = orientation) +# Create input and output matrises for scale efficiency +x <- create_matrix(df, inputs, idvar, normalize = params$params$normalize) +y <- create_matrix(df, outputs, idvar, normalize = params$params$normalize) # Get table for scale efficiency -compute_scale_efficiency(inputs, outputs, orientation, digits = 4L) +compute_scale_efficiency(x, y, orientation, digits = 4L) ``` diff --git a/inst/app/global.R b/inst/app/global.R index 6486c81..6ab1034 100644 --- a/inst/app/global.R +++ b/inst/app/global.R @@ -1,4 +1,5 @@ # Load packages +library(pioneeR) library(shiny) library(bslib) source('R/utils.R') diff --git a/inst/app/server.R b/inst/app/server.R index ec139ee..62782eb 100644 --- a/inst/app/server.R +++ b/inst/app/server.R @@ -1,9 +1,6 @@ # Load required packages require(readxl) require(ucminf) -require(lpSolveAPI) -require(lpSolve) -require(Benchmarking) require(productivity) require(ggplot2) require(scales) @@ -254,9 +251,9 @@ shinyServer(function(input, output, session) { req(data(), dea.in(), dea.out()) - d <- Benchmarking::dea( - dea.in(), dea.out(), RTS = model_params$rts, - ORIENTATION = model_params$orientation) + d <- pioneeR:::compute_efficiency( + dea.in(), dea.out(), rts = model_params$rts, + orientation = model_params$orientation) d @@ -264,7 +261,7 @@ shinyServer(function(input, output, session) { dea.slack <- reactive({ x <- tryCatch({ - Benchmarking::slack(dea.in(), dea.out(), dea.prod()) + pioneeR:::compute_slack(dea.in(), dea.out(), dea.prod()) }, warning = function(e) { NULL }, error = function(e) { @@ -277,9 +274,10 @@ shinyServer(function(input, output, session) { req(data(), dea.in(), dea.out()) - d <- Benchmarking::sdea( - dea.in(), dea.out(), RTS = model_params$rts, - ORIENTATION = model_params$orientation) + d <- pioneeR:::compute_super_efficiency( + dea.in(), dea.out(), rts = model_params$rts, + orientation = model_params$orientation + ) d @@ -296,7 +294,7 @@ shinyServer(function(input, output, session) { if (is.matrix(x)) x <- rowSums(x) if (is.matrix(y)) y <- rowSums(y) - data.frame(dmu = rownames(dea.in()), x = unname(x), y = unname(y), eff = unname(prod$eff)) + data.frame(dmu = rownames(dea.in()), x = unname(x), y = unname(y), eff = unname(prod$values)) }) dea_plot <- reactive({ @@ -380,7 +378,7 @@ shinyServer(function(input, output, session) { dea_scaleeff <- reactive({ - out <- compute_scale_efficiency(dea.in(), dea.out(), model_params$orientation, 4L) + out <- compute_scale_efficiency(dea.in(), dea.out(), model_params$orientation, input$dea_round) out }) @@ -393,7 +391,7 @@ shinyServer(function(input, output, session) { dmu = selection()[[params()$id]], inputs = sapply(seq_len(nrow(dea.in())), function(i) sum(dea.in()[i,])), outputs = sapply(seq_len(nrow(dea.out())), function(i) sum(dea.out()[i,])), - eff = dea.prod()$eff, + eff = dea.prod()$values, stringsAsFactors = FALSE ) @@ -474,16 +472,15 @@ shinyServer(function(input, output, session) { req(dea.prod()) - eff_tbl <- summary_tbl_dea(dea.prod()) - eff <- dea.prod()$eff - + eff <- dea.prod()$values + eff_tbl <- summary_tbl_dea(eff) if (model_params$orientation == 'in') sum_eff <- sum(dea.in() * eff) / sum(dea.in()) else if (model_params$orientation == 'out') sum_eff <- sum(dea.out() * eff) / sum(dea.out()) rts <- switch( - dea.prod()$RTS, + input$dea_rts, crs = 'Constant', vrs = 'Variable', drs = 'Non-increasing', @@ -491,7 +488,7 @@ shinyServer(function(input, output, session) { ) orient <- switch( - dea.prod()$ORIENTATION, + input$dea_orientation, 'in' = 'Input', 'out' = 'Output' ) @@ -582,7 +579,7 @@ shinyServer(function(input, output, session) { dea.tbl <- reactive({ - deff <- matrix(dea.prod()$eff, ncol = 1, dimnames = list(NULL, 'Efficiency')) + deff <- matrix(dea.prod()$values, ncol = 1, dimnames = list(NULL, 'Efficiency')) # Initialize to NULL ins <- outs <- sl <- seff <- NULL @@ -598,13 +595,13 @@ shinyServer(function(input, output, session) { outs <- apply(dea.out(), 1, sum) if (input$out.slack) - sl <- matrix(dea.slack()$sum, ncol = 1, dimnames = list(NULL, 'Slack')) + sl <- matrix(dea.slack()$values, ncol = 1, dimnames = list(NULL, 'Slack')) if (input$out.sdea) - seff <- matrix(sdea.prod()$eff, ncol = 1, dimnames = list(NULL, 'sDEA')) + seff <- matrix(sdea.prod()$values, ncol = 1, dimnames = list(NULL, 'sDEA')) df <- data.frame( - DMU = names(dea.prod()$eff), + DMU = selection()[, input$dea_id], round(cbind(ins, outs, deff, sl, seff), input$dea_round), stringsAsFactors = FALSE, row.names = NULL ) @@ -627,8 +624,7 @@ shinyServer(function(input, output, session) { }) output$dea.slack <- renderReactable({ - sl <- dea.slack() - df <- data.frame(round(cbind(sl$sx, sl$sy, sl$sum), 3)) + df <- round(dea.slack()$data, input$dea_round) colnames(df)[ncol(df)] <- 'Total' opts <- list2(!!!reactable_opts, data = df) @@ -636,11 +632,7 @@ shinyServer(function(input, output, session) { }) output$peers.table <- renderReactable({ - pr <- dea.prod() - pe <- Benchmarking::peers(pr, NAMES = TRUE) - df <- data.frame( - cbind(selection()[, input$dea_id], pe), - stringsAsFactors = FALSE) + df <- pioneeR:::get_peers(dea.prod(), ids = selection()[, input$dea_id], threshold = 0) colnames(df)[1] <- 'DMU' opts <- list2(!!!reactable_opts, data = df, columns = list( @@ -692,13 +684,14 @@ shinyServer(function(input, output, session) { observeEvent(input$save_model, { if (length(models()) >= 10) return() + # EDIT HERE! mod <- dea.prod() mod_save <- list( id = rand_id(), data = data.frame( - idx = seq_len(length(mod$eff)), - dmu = names(mod$eff), - eff = round(unname(mod$eff), input$dea_round) + idx = seq_len(length(mod$values)), + dmu = selection()[, input$dea_id], #names(mod$eff), + eff = round(unname(mod$values), input$dea_round) ), params = list( rts = mod$RTS, @@ -860,7 +853,7 @@ shinyServer(function(input, output, session) { y <- isolate(dea.out()) withProgress(message = 'Running', value = 0, { - theta <- isolate(as.vector(dea.prod()$eff)) + theta <- isolate(as.vector(dea.prod()$values)) # theta >= 1 if 'out', <= if 'in' h <- if (is.numeric(bw_rule)) bw_rule else bw_rule(theta, rule = bw_rule) boot <- matrix(NA, nrow = nrow(x), ncol = b) @@ -906,16 +899,16 @@ shinyServer(function(input, output, session) { return() # Add DMU names and round inputs - df <- cbind(data.frame(DMU = names(dea.prod()$eff)), round(res$tbl, input$boot_round)) + df <- cbind(data.frame(DMU = names(dea.prod()$values)), round(res$tbl, input$boot_round)) opts <- list2(!!!reactable_opts, data = df, columns = list( - eff = colDef(show = input$boot_show_eff, name = 'Efficiency'), - bias = colDef(show = input$boot_show_bias, name = 'Bias'), - eff_bc = colDef(name = 'Bias corr. score'), - lower = colDef(name = 'Lower bound'), - upper = colDef(name = 'Upper bound'), - range = colDef(name = 'CI range') - ) + eff = colDef(show = input$boot_show_eff, name = 'Efficiency'), + bias = colDef(show = input$boot_show_bias, name = 'Bias'), + eff_bc = colDef(name = 'Bias corr. score'), + lower = colDef(name = 'Lower bound'), + upper = colDef(name = 'Upper bound'), + range = colDef(name = 'CI range') + ) ) tbl <- do.call(reactable, opts) @@ -945,7 +938,7 @@ shinyServer(function(input, output, session) { }, content = function(file) { res <- dea_boostrap() - df <- cbind(data.frame(DMU = names(dea.prod()$eff)), round(res$tbl, input$boot_round)) + df <- cbind(data.frame(DMU = names(dea.prod()$values)), round(res$tbl, input$boot_round)) # Export based on chosen file format if (input$boot_fileformat == 'dta') { colnames(df) <- gsub('\\s', '_', colnames(df)) diff --git a/man/compute_dea.Rd b/man/compute_dea.Rd new file mode 100644 index 0000000..0f87a52 --- /dev/null +++ b/man/compute_dea.Rd @@ -0,0 +1,49 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/compute_dea.R +\name{compute_dea} +\alias{compute_dea} +\title{Compute DEA} +\usage{ +compute_dea( + data, + id, + input, + output, + rts = c("crs", "vrs", "drs", "irs"), + orientation = c("in", "out"), + super = FALSE, + slack = FALSE, + peers = FALSE +) +} +\arguments{ +\item{data}{Dataset to analyse.} + +\item{id}{A string with the DMU id or name variable.} + +\item{input}{A character vector with input variables.} + +\item{output}{A character vector with output variables.} + +\item{rts}{Returns to scale. +\tabular{rlll}{ +\tab crs \tab\tab Constant returns to scale, convexity and free disposability. \cr +\tab vrs \tab\tab Variable returns to scale, convexity and free disposability. \cr +\tab drs \tab\tab Decreasing returns to scale, convexity, down-scaling and free disposability. \cr +\tab irs \tab\tab Increasing returns to scale, (up-scaling, but not down-scaling), convexity and free disposability. +}} + +\item{orientation}{Model orientation.} + +\item{super}{If \code{TRUE} super efficiency scores are calculated.} + +\item{slack}{If \code{TRUE} slack values are calculated.} + +\item{peers}{If \code{TRUE} peers are added to the response.} +} +\value{ +list +} +\description{ +DEA analysis. +} diff --git a/tests/script/create-testdata-benchmarking.R b/tests/script/create-testdata-benchmarking.R new file mode 100644 index 0000000..a533394 --- /dev/null +++ b/tests/script/create-testdata-benchmarking.R @@ -0,0 +1,89 @@ +# ---- Load datasets ---- # + +# Simple example (taken from https://pystoned.readthedocs.io/en/latest/examples/DEA/dea_ref.html) +x <- as.matrix(c(100, 200, 300, 500, 100, 200, 600, 400, 550, 600)) +y <- as.matrix(c(75, 100, 300, 400, 25, 50, 400, 260, 180, 240)) +xref <- as.matrix(c(100, 300, 500, 100, 600)) +yref <- as.matrix(c(75, 300, 400, 25, 400)) + +# Frontier 4.1 +frontier41 <- readRDS('tests/testdata/frontier41.RDS') +f41_x <- as.matrix(frontier41[c('capital', 'labour')]) +f41_y <- as.matrix(frontier41[c('output')]) + +# Norwegian District Courts +norCourts2018 <- readRDS('tests/testdata/norCourts2018.RDS') +nc_x <- as.matrix(norCourts2018[c('judges', 'case_workers', 'costs')]) +nc_y <- as.matrix(norCourts2018[c( + 'civil_cases', 'criminal_case_single', + 'criminal_case_full_bench', + 'other_civil_cases')]) + +# Electric Plants +electricPlants <- readRDS('tests/testdata/electricPlants.RDS') +ecp_x <- as.matrix(electricPlants[c('labor', 'fuel', 'capital')]) +ecp_y <- as.matrix(electricPlants[c('output')]) + +# Hospitals +hospitals <- readRDS('tests/testdata/hospitals.RDS') +hospitals_x <- as.matrix(hospitals[c('labor', 'capital')]) +hospitals_y <- as.matrix(hospitals[c('inpatients', 'outpatients')]) + + +# ---- Helper functions ---- # + +#' Create DEA test data (using Benchmarking) +#' @param x A matrix with input values. +#' @param y A matrix with output values. +create_testdata <- function(x, y, xref = NULL, yref = NULL, ids) { + orientations <- c('in', 'out') + rts_ <- c('crs', 'vrs', 'drs', 'irs') + dl <- list() + for (a in seq_along(orientations)) { + for (b in seq_along(rts_)) { + tmp <- create_testdata_single( + x, y, xref, yref, ids, + rts = rts_[b], + orientation = orientations[a]) + tmp <- list(tmp) + names(tmp) <- paste0(orientations[a], '_', rts_[b]) + dl <- append(dl, tmp) + } + } + dl +} + +#' Create DEA test data (single) +#' @param x A matrix with input values. +#' @param y A matrix with output values. +#' @param rts Returns to scale +#' @param orientation Model orientation +create_testdata_single <- function(x, y, xref, yref, ids, rts, orientation) { + eff <- Benchmarking::dea(x, y, XREF = xref, YREF = yref, RTS = rts, ORIENTATION = orientation) + super_eff <- Benchmarking::sdea(x, y, RTS = rts, ORIENTATION = orientation) + slack <- Benchmarking::slack(x, y, e = eff) + colnames(eff$lambda) <- ids + peers <- Benchmarking::peers(eff, NAMES = TRUE) + + dimnames(eff$lambda) <- NULL + dimnames(super_eff$lambda) <- NULL + dimnames(slack$lambda) <- NULL + + dl <- list(efficiency = list(eff = eff$eff, lambda = eff$lambda, objval = eff$objval, peers = peers), + super_efficiency = list(eff = super_eff$eff, lambda = super_eff$lambda, objval = super_eff$objval), + slack = list(eff = slack$eff, lambda = slack$lambda, objval = slack$objval, sum = slack$sum) + ) + dl +} + + +# ---- Create testdata ---- # + +results <- list( + 'simple' = create_testdata(x, y, xref, yref, ids = rownames(x)), + 'frontier41' = create_testdata(f41_x, f41_y, ids = frontier41$firm), + 'norCourts2018' = create_testdata(nc_x, nc_y, ids = norCourts2018$district_court), + 'electricPlants' = create_testdata(ecp_x, ecp_y, ids = electricPlants$plant), + 'hospitals' = create_testdata(hospitals_x, hospitals_y, ids = hospitals$firm_id) +) +saveRDS(results, 'tests/testdata/benchmarking-results.RDS') diff --git a/tests/testdata/benchmarking-results.RDS b/tests/testdata/benchmarking-results.RDS new file mode 100644 index 0000000000000000000000000000000000000000..0f9f2d31c5e287070d43620a6ed0715444829cdf GIT binary 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