R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("ineq-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('ineq')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "Lc"
> 
> ### * Lc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Lc
> ### Title: Lorenz Curve
> ### Aliases: Lc
> ### Keywords: misc
> 
> ### ** Examples
> 
> ## Load and attach income (and metadata) set from Ilocos, Philippines
> data(Ilocos)
> attach(Ilocos)
> ## extract and rescale income for the provinces "Pangasinan" und "La Union"
> income.p <- income[province=="Pangasinan"]/10000
> income.u <- income[province=="La Union"]/10000
> 
> ## compute the Lorenz curves
> Lc.p <- Lc(income.p)
> Lc.u <- Lc(income.u)
> ## it can be seen the the inequality in La Union is higher than in
> ## Pangasinan because the respective Lorenz curve takes smaller values.
> plot(Lc.p)
> lines(Lc.u, col=2)
> ## the picture becomes even clearer with generalized Lorenz curves
> plot(Lc.p, general=TRUE)
> lines(Lc.u, general=TRUE, col=2)
> ## inequality measures emphasize these results, e.g. Atkinson's measure
> ineq(income.p, type="Atkinson")
[1] 0.1291856
> ineq(income.u, type="Atkinson")
[1] 0.1672924
> ## or Theil's entropy measure
> ineq(income.p, type="Theil", parameter=0)
[1] 0.2837236
> ineq(income.u, type="Theil", parameter=0)
[1] 0.3622122
> 
> 
> # income distribution of the USA in 1968 (in 10 classes)
> # x vector of class means, n vector of class frequencies
> x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261)
> n <- c(482, 825, 722, 690, 661, 760, 745, 2140, 1911, 1024)
> 
> # compute minimal Lorenz curve (= no inequality in each group)
> Lc.min <- Lc(x, n=n)
> # compute maximal Lorenz curve (limits of Mehran)
> Lc.max <- Lc.mehran(x,n)
> # plot both Lorenz curves in one plot
> plot(Lc.min)
> lines(Lc.max, col=4)
> 
> # add the theoretic Lorenz curve of a Lognormal-distribution with variance 0.78
> lines(Lc.lognorm, parameter=0.78)
> # add the theoretic Lorenz curve of a Dagum-distribution
> lines(Lc.dagum, parameter=c(3.4,2.6))
> 
> 
> 
> cleanEx(); ..nameEx <- "Lc.mehran"
> 
> ### * Lc.mehran
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Lc.mehran
> ### Title: Mehran Bounds For Lorenz Curves
> ### Aliases: Lc.mehran
> ### Keywords: misc
> 
> ### ** Examples
> 
> # income distribution of the USA in 1968 (in 10 classes)
> # x vector of class means, n vector of class frequencies
> x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261)
> n <- c(482, 825, 722, 690, 661, 760, 745, 2140, 1911, 1024)
> 
> # compute minimal Lorenz curve (= no inequality in each group)
> Lc.min <- Lc(x, n=n)
> # compute maximal Lorenz curve (limits of Mehran)
> Lc.max <- Lc.mehran(x,n)
> # plot both Lorenz curves in one plot
> plot(Lc.min)
> lines(Lc.max, col=4)
> 
> # add the theoretic Lorenz curve of a Lognormal-distribution with variance 0.78
> lines(Lc.lognorm, parameter=0.78)
> # add the theoretic Lorenz curve of a Dagum-distribution
> lines(Lc.dagum, parameter=c(3.4,2.6))
> 
> 
> 
> cleanEx(); ..nameEx <- "Pen"
> 
> ### * Pen
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Pen
> ### Title: Pen's Parade
> ### Aliases: Pen
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # load and attach Philippine income data
> data(Ilocos)
> attach(Ilocos)
> # plot Pen's Parade of income
> Pen(income)
> Pen(income, fill = hsv(0.1, 0.3, 1))
> 
> # income distribution of the USA in 1968 (in 10 classes)
> # x vector of class means, n vector of class frequencies
> x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261)
> n <- c(482, 825, 722, 690, 661, 760, 745, 2140, 1911, 1024)
> Pen(x, n = n)
> # create artificial grouping variable
> myfac <- factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3))
> Pen(x, n = n, group = myfac)
> 
> 
> 
> cleanEx(); ..nameEx <- "conc"
> 
> ### * conc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: conc
> ### Title: Concentration Measures
> ### Aliases: conc Herfindahl Rosenbluth
> ### Keywords: univar
> 
> ### ** Examples
> 
> # generate vector (of sales)
> x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261)
> # compute Herfindahl coefficient with parameter 1
> conc(x)
[1] 0.1840812
> # compute coefficient of Hall/Tiedemann/Rosenbluth
> conc(x, type="Rosenbluth")
[1] 0.1859051
> 
> 
> 
> cleanEx(); ..nameEx <- "ineq"
> 
> ### * ineq
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ineq
> ### Title: Inequality Measures
> ### Aliases: ineq Gini RS Atkinson Theil Kolm var.coeff entropy
> ### Keywords: univar
> 
> ### ** Examples
> 
> # generate vector (of incomes)
> x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261)
> # compute Gini coefficient
> ineq(x)
[1] 0.4620911
> # compute Atkinson coefficient with parameter=0.5
> ineq(x, parameter=0.5, type="Atkinson")
[1] 0.1796591
> 
> 
> 
> cleanEx(); ..nameEx <- "major"
> 
> ### * major
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: major
> ### Title: Majorization
> ### Aliases: major
> ### Keywords: logic
> 
> ### ** Examples
> 
> # generate vectors (of incomes)
> x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261)
> y <- c(841, 2063, 2445, 3438, 4437, 5401, 6392, 8304, 11304, 21961)
> # test whether x majorizes y (TRUE, because y is result of
> # Pigou-Dalton-transfers)
> major(x,y)
[1] TRUE
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.Lc"
> 
> ### * plot.Lc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.Lc
> ### Title: Plot Lorenz Curve
> ### Aliases: plot.Lc lines.Lc
> ### Keywords: hplot
> 
> ### ** Examples
> 
> ## Load and attach income (and metadata) set from Ilocos, Philippines
> data(Ilocos)
> attach(Ilocos)
> ## extract and rescale income for the provinces "Pangasinan" und "La Union"
> income.p <- income[province=="Pangasinan"]/10000
> income.u <- income[province=="La Union"]/10000
> ## compute the Lorenz curves
> Lc.p <- Lc(income.p)
> Lc.u <- Lc(income.u)
> ## plot both Lorenz curves
> plot(Lc.p)
> lines(Lc.u, col=2)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.theorLc"
> 
> ### * plot.theorLc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.theorLc
> ### Title: Plot Theoretical Lorenz Curves
> ### Aliases: plot.theorLc lines.theorLc
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # income distribution of the USA in 1968 (in 10 classes)
> # x vector of class means, n vector of class frequencies
> x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261)
> n <- c(482, 825, 722, 690, 661, 760, 745, 2140, 1911, 1024)
> 
> # compute minimal Lorenz curve (= no inequality in each group)
> Lc.min <- Lc(x, n=n)
> # compute maximal Lorenz curve (limits of Mehran)
> Lc.max <- Lc.mehran(x,n)
> # plot both Lorenz curves in one plot
> plot(Lc.min)
> lines(Lc.max, col=4)
> 
> # add the theoretic Lorenz curve of a Lognormal-distribution with variance 0.78
> lines(Lc.lognorm, parameter=0.78)
> # add the theoretic Lorenz curve of a Dagum-distribution
> lines(Lc.dagum, parameter=c(3.4,2.6))
> 
> 
> 
> cleanEx(); ..nameEx <- "pov"
> 
> ### * pov
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pov
> ### Title: Poverty Measures
> ### Aliases: pov Watts Sen Foster
> ### Keywords: univar
> 
> ### ** Examples
> 
> # generate vectors (of incomes)
> x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261)
> y <- c(841, 2063, 2445, 3438, 4437, 5401, 6392, 8304, 11304, 21961)
> # compute Watts index with poverty boundary 2000
> pov(x, 2000)
[1] 0.1620141
> pov(y, 2000)
[1] 0.08663108
> # compute head count ratio with poverty boundary 2000
> pov(x, 2000, parameter=1, type="Foster")
[1] 0.2
> pov(y, 2000, parameter=1, type="Foster")
[1] 0.1
> 
> 
> cleanEx(); ..nameEx <- "theorLc"
> 
> ### * theorLc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: theorLc
> ### Title: Theoretical Lorenz Curves
> ### Aliases: theorLc Lc.dagum Lc.singh Lc.pareto Lc.lognorm Lc.exp
> ### Keywords: misc
> 
> ### ** Examples
> 
> ## Load and attach income (and metadata) set from Ilocos, Philippines
> data(Ilocos)
> attach(Ilocos)
> ## extract income for the province "Pangasinan"
> income.p <- income[province=="Pangasinan"]
> 
> ## plot empirical Lorenz curve and add theoretical Lorenz curve of
> ## a lognormal distribution with an estimate of the standard
> ## deviation parameter
> Lc.p <- Lc(income.p)
> plot(Lc.p)
> lines(Lc.lognorm, parameter=sd(log(income.p)), col=4)
> 
> 
> # vector of percentages
> p <- (1:10)*0.1
> # compute values of theoretic Lorenz curve of a Dagum-distribution
> Lc.dagum(p, parameter=c(3.4,2.6))
 [1] 0.01309781 0.04457476 0.09148283 0.15291724 0.22887426 0.32020415
 [7] 0.42897407 0.55962902 0.72335212 1.00000000
> # or
> mydagum <- theorLc(type="Dagum", parameter=c(3.4,2.6))
> mydagum(p)
 [1] 0.01309781 0.04457476 0.09148283 0.15291724 0.22887426 0.32020415
 [7] 0.42897407 0.55962902 0.72335212 1.00000000
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  0.27 0.02 0.3 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')