additiveDEA (version 1.1)

dea.fast: Faster Solving of Additive DEA Models with Large Datasets

Description

Reduce calculation time of additive DEA efficiency models when the data comprise of several thousand DMUs

Usage

dea.fast(base, noutput, fixed = NULL, rts = 2, bound = NULL,
  add.model = c("additive", "RAM", "BAM", "MIP", "LovPast", "SBM"),
  blockSize = 200)

Arguments

base

A data frame with N rows and S+M columns, where N is the number of Decision-Making Units (DMUs), S is the number of outputs and M is the number of inputs.

noutput

The number of outputs produced by the DMUs. All DMUs must produce the same number of outputs.

fixed

A numeric vector containing column indices for fixed (non-controllable) outputs and/or inputs (if any) in the data. Defaults to NULL.

rts

Returns to scale specification. 1 for constant returns to scale and 2 (default) for variable returns to scale.

bound

A data frame with N rows and S+M columns containing user-defined bounds on the slacks of each DMU. If bounds are supplied by the user in cases where some outputs and/or inputs are fixed, values should be 0 for these fixed variables. Same for slacks that do not require bounds. Defaults to NULL.

add.model

Additive model to calculate efficiency. additive: unweighted additive model (Cooper et al., 2007); RAM: Range Adjusted Measure (Cooper et al., 1999; 2001); BAM: Bounded Adjusted Measure (Cooper et al., 2011); MIP: Measure of Inefficiency Proportions (Cooper et al., 1999); LovPast: the Lovell-Pastor Measure (Lovell and Pastor, 1995); SBM: Slacks-Based Measure (Tone, 2001).

blockSize

How many DMUs should each sub-problem comprise of? Defaults to 200.

Value

Returns a numeric vector containing the (in)efficiency scores of the DMUs.

Details

dea.fast speeds up computation time of functions dea.gem and dea.sbm when the data comprise of several thousand DMUs. It does so by dividing the data into several blocks consisting of a few hundred DMUs. Then, it finds the efficient DMUs in each block. The next step is to merge the efficient DMUs into one final set and to find the efficient DMUs in this set. Finally, the DMUs in each block are benchmarked against the DMUs that were found to be efficient in the final set of the previous step. See Newsletter 16 in http://www.saitech-inc.com/Products/Prod-DSP.asp. If N is not divisible by blockSize, dea.fast will split the data into a number of even blocks plus a final block with the remaining DMUs. For instance, if N=1050 and blockSize=200, there will be five blocks with 200 DMUs and a sixth one with 50 DMUs.

References

Cooper W. W., Park K. S., Pastor J. T. (1999) RAM: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Journal of Productivity Analysis, 11, 5--42

Cooper W. W., Park K. S., Pastor J. T. (2001) The range adjusted measure (RAM) in DEA: a response to the comment by Steinmann and Zweifel. Journal of Productivity Analysis, 15, 145--152

Cooper W. W., Pastor J. T., Borras F., Aparicio J., Pastor D. (2011) BAM: a bounded adjusted measure of efficiency for use with bounded additive models. Journal of Productivity Analysis, 35, 85--94

Cooper W. W., Seiford L., Tone K. (2007) Data Envelopment Analysis: a comprehensive text with models, applications, references and DEA-Solver software. New York: Springer

Lovell, C. A. K., Pastor J. T. (1995) Units invariant and translation invariant DEA models. Operations Research Letters, 18, 147--151

Tone K. (2001) A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498--509

See Also

dea.gem, dea.sbm

Examples

# NOT RUN {
# Get data from package Benchmarking:
library(Benchmarking)
data(pigdata)
base <- pigdata[, 2:9][, c(7,8,1:6)]
# Create trivial but large dataset
base <- rbind(base,base,base,base)
system.time(dea.fast(base, noutput= 2, rts= 2,
  add.model= "LovPast", blockSize = 200))

## The function is currently defined as
function (base, noutput, fixed = NULL, rts = 2, bound = NULL, 
    add.model = c("additive", "RAM", "BAM", "MIP", "LovPast", 
        "SBM"), blockSize = 200) 
{
    baseEfficient <- list()
    n <- nrow(base)
    mod <- (n - (n%%blockSize))/blockSize
    blocks <- c(1, 1:mod * blockSize + 1)
    for (i in 1:mod) {
        aux <- blocks[i]:(blocks[i + 1] - 1)
        base1 <- base[aux, ]
        bound1 <- bound[aux, ]
        if (add.model != "SBM") {
            eff <- round(dea.gem(base = base1, noutput, fixed, 
                rts, bound = bound1, add.model)$eff, 7)
            index <- which(is.na(eff))
            if (length(index) > 0) {
                eff[index] <- round(dea.gem(base = base1, noutput, 
                  fixed, rts, bound = bound1, add.model, whichDMUs = index)$eff, 
                  7)
            }
            baseEfficient[[i]] <- base1[which(eff == 0), ]
        }
        else {
            eff <- round(dea.sbm(base = base1, noutput, fixed, 
                rts, bound = bound1)$eff, 7)
            index <- which(is.na(eff))
            if (length(index) > 0) {
                eff[index] <- round(dea.sbm(base = base1, noutput, 
                  fixed, rts, bound = bound1, whichDMUs = index)$eff, 
                  7)
            }
            baseEfficient[[i]] <- base1[which(eff == 1), ]
        }
    }
    if (n%%blockSize != 0) {
        aux <- (n - (n%%blockSize) + 1):n
        base1 <- base[aux, ]
        bound1 <- bound[aux, ]
        if (add.model != "SBM") {
            eff <- round(dea.gem(base = base1, noutput, fixed, 
                rts, bound = bound1, add.model)$eff, 7)
            index <- which(is.na(eff))
            if (length(index) > 0) {
                eff[index] <- round(dea.gem(base = base1, noutput, 
                  fixed, rts, bound = bound1, add.model, whichDMUs = index)$eff, 
                  7)
            }
            baseEfficient[[i + 1]] <- base1[which(eff == 0), 
                ]
        }
        else {
            eff <- round(dea.sbm(base = base1, noutput, fixed, 
                rts, bound = bound1)$eff, 7)
            index <- which(is.na(eff))
            if (length(index) > 0) {
                eff[index] <- round(dea.sbm(base = base1, noutput, 
                  fixed, rts, bound = bound1, whichDMUs = index)$eff, 
                  7)
            }
            baseEfficient[[i + 1]] <- base1[which(eff == 1), 
                ]
        }
    }
    baseEfficient <- do.call("rbind", baseEfficient)
    if (add.model != "SBM") {
        eff <- round(dea.gem(base = base1, noutput, fixed, rts, 
            bound = bound1, add.model)$eff, 7)
        index <- which(is.na(eff))
        if (length(index) > 0) {
            eff[index] <- round(dea.gem(base = base1, noutput, 
                fixed, rts, bound = bound1, add.model, whichDMUs = index)$eff, 
                7)
        }
        baseEfficient <- base1[which(eff == 0), ]
    }
    else {
        eff <- round(dea.sbm(base = base1, noutput, fixed, rts, 
            bound = bound1)$eff, 7)
        index <- which(is.na(eff))
        if (length(index) > 0) {
            eff[index] <- round(dea.sbm(base = base1, noutput, 
                fixed, rts, bound = bound1, whichDMUs = index)$eff, 
                7)
        }
        baseEfficient <- base1[which(eff == 1), ]
    }
    eff <- list()
    for (i in 1:mod) {
        aux <- blocks[i]:(blocks[i + 1] - 1)
        base1 <- base[aux, ]
        base1 <- rbind(base1, baseEfficient)
        bound1 <- bound[aux, ]
        if (!is.null(bound)) {
            df <- data.frame(matrix(0, nrow = nrow(base1[1:(nrow(base1) - 
                blockSize), ]), ncol = ncol(base1)))
            names(df) <- names(bound1)
            bound1 <- rbind(bound1, df)
        }
        if (add.model != "SBM") {
            eff[[i]] <- dea.gem(base = base1, noutput, fixed, 
                rts, bound = bound1, add.model, whichDMUs = 1:blockSize)$eff
            index <- which(is.na(eff[[i]]))
            if (length(index) > 0) {
                eff[[i]][index] <- dea.gem(base = base1, noutput, 
                  fixed, rts, bound = bound1, add.model, whichDMUs = index)$eff
            }
        }
        else {
            eff[[i]] <- dea.sbm(base = base1, noutput, fixed, 
                rts, bound = bound1, whichDMUs = 1:blockSize)$eff
            index <- which(is.na(eff[[i]]))
            if (length(index) > 0) {
                eff[[i]][index] <- dea.sbm(base = base1, noutput, 
                  fixed, rts, bound = bound1, whichDMUs = index)$eff
            }
        }
    }
    if (n%%blockSize != 0) {
        aux <- (n - (n%%blockSize) + 1):n
        base1 <- base[aux, ]
        base1 <- rbind(base1, baseEfficient)
        bound1 <- bound[aux, ]
        newBlockSize <- nrow(base) - mod * blockSize
        if (!is.null(bound)) {
            df <- data.frame(matrix(0, nrow = nrow(base1[1:(nrow(base1) - 
                newBlockSize), ]), ncol = ncol(base1)))
            names(df) <- names(bound1)
            bound1 <- rbind(bound1, df)
        }
        if (add.model != "SBM") {
            eff[[i + 1]] <- dea.gem(base = base1, noutput, fixed, 
                rts, bound = bound1, add.model, whichDMUs = 1:newBlockSize)$eff
            index <- which(is.na(eff[[i + 1]]))
            if (length(index) > 0) {
                eff[[i + 1]][index] <- dea.gem(base = base1, 
                  noutput, fixed, rts, bound = bound1, add.model, 
                  whichDMUs = index)$eff
            }
        }
        else {
            eff[[i + 1]] <- dea.sbm(base = base1, noutput, fixed, 
                rts, bound = bound1, whichDMUs = 1:newBlockSize)$eff
            index <- which(is.na(eff[[i + 1]]))
            if (length(index) > 0) {
                eff[[i + 1]][index] <- dea.sbm(base = base1, 
                  noutput, fixed, rts, bound = bound1, whichDMUs = index)$eff
            }
        }
    }
    eff <- unlist(eff)
    return(eff)
  }
# }