intervalPlot: Generate interval plot for inferring the existence of...

In LiangCZhang/Rcess: Rcess: An R package for calculating estimates of costs and economies of scale and scope

Description

Generate interval plot for inferring the existence of economies of scale or scope

Usage

intervalPlot(intervalData = intervalData, estimates,
  meanLevels = intervalData$meanLevels, lowerLevel = intervalData$Lo_GSE,
  UpperLevel = intervalData$Hi_GSE, ylab, h, ylim = c(-1, 1))

Arguments

intervalData	The data used for plotting intervals.
estimates	A vector of estimates which you want to plot with.
meanLevels	A vector of percentage of output mean.
lowerLevel	A vector of lower confidence intervals.
ylab	Y axis label.
h	A horizontal red line for inferring the existence of economies of scale (=1) or scope (=0).
ylim	Y axis limit.
upperLevel	A vector of upper confidence intervals.

Details

This function generates a interval plot (Zhang, 2015) to infer the existence of scale and scope economies. If the intervals of point estimates do not include one for scale economies or zero for the scope economies, it suggests that these estimates are significantly different from the thresholds (one and zero, respectively) at the 5% significance level. Thus, there is evidence for the existence of scale and scope economies (if their intervals are higher than the threshold) or diseconomies (if their intervals are lower than the threshold).

Value

A interval plot

Author(s)

Liang-Cheng Zhang

References

Zhang, L.-C., Worthington, A. C., & Hu, M. (in press). Cost economies in the provision of higher education for international students: Australian evidence. Higher Education. doi: 10.1007/s10734-016-0078-9

Zhang, L.-C. (2015). Inferring (dis)economies of scope with a proper procedure: using an interval plot as an alternative solution. Paper presented at the 2015 AFE PhD Research Symposium in Economics, Economics and Business Statistics Discipline, Griffith Business School, Griffith University. PDF Link

Examples

#interval plot for GSE
data(unidat)
data = unidat
library(minpack.lm)
model <- nlsLM(costFunction(costName = colnames(unidat)[3], outputName = colnames(unidat)[7:11],
priceName = colnames(unidat)[4:6], controlName = colnames(unidat)[12:24],
form = "FFCQ-M"), start = list(b0 = 600, b1 = 0, b2 = 0,
                               b3 = 0, b4 = 0, b5 = 0, b11 = 0, b22 = 0, b33 = 0, b44 = 0,
                               b55 = 0, b12 = 0, b13 = 0, b14 = 0, b15 = 0, b23 = 0, b24 = 0,
                               b25 = 0, b34 = 0, b35 = 0, b45 = 0, bp2 = 0, bp3 = 0, bz1 = 0,
                               bz2 = 0, bz3 = 0, bz4 = 0, bz5 = 0, bz6 = 0, bz7 = 0, bz8 = 0,
                               bz9 = 0, bz10 = 0, bz11 = 0, bz12 = 0, bz13 = 0), data = unidat,
                               trace = F)
vcovCL <- clusterEst(model = model , cluster = unidat$unicode)$vcovCL
##interval plot for GSE
intervalPlot(intervalData = intervalData, estimates = intervalData$GSE,
meanLevels = intervalData$meanLevels, lowerLevel = intervalData$Lo_GSE,
UpperLevel = intervalData$Hi_GSE, ylab = "Degree of economies of scope",
h = 0,ylim = c(-1,1))

##interval plot for SRAY
intervalPlot(intervalData = intervalData, estimates = intervalData$SRAY,
meanLevels = intervalData$meanLevels, lowerLevel = intervalData$Lo_SRAY,
UpperLevel = intervalData$Hi_SRAY, ylab = "Degree of economies of scale",
h = 1,ylim = c(min(intervalData$Lo_SRAY,1),max(intervalData$Hi_SRAY)))

LiangCZhang/Rcess documentation built on May 8, 2019, 8:52 p.m.