fitGBPD: Fitting Mixture Model of Generalized Beta and Pareto

In bda: Binned Data Analysis

Fitting Mixture Model of Generalized Beta and Pareto

Description

To fit a mixture model of generalize beta and Pareto to grouped data.

Usage

  fit.GBP(x,breaks)

Arguments

x	'x' can be a vector or a matrix, or a 'histogram'.
breaks	a matrix with two columns if 'x' is a matrix. Otherwise, it is a vector. Can be missing if 'x' is a vector and 'x' will be grouped uisng the default parameters with hist.

Value

pars	estimated parameters.

Examples

data(FSD)
x <- as.numeric(FirmSize[nrow(FirmSize),])
brks.size <- c(0,4.5,9.5,19.5,49.5,99.5,249.5,499.5,
               999.5,2499.5,4999.5, 9999.5,Inf)
xhist1 <- binning(counts=x,breaks=brks.size)

(out <- fit.GBP(x,brks.size))
(out <- fit.GBP(xhist1))

plot(xhist1,xlim=c(0,110))
x0 <- seq(0,110,length=1000)
f0 <- dGBP(x0,out)
lines(f0~x0, col=2,lwd=2)

ZipfPlot(xhist1,plot=TRUE)
F0 <- pGBP(brks.size,out)
lines(log(1-F0)~log(brks.size), col=2)

bda documentation built on Sept. 11, 2024, 9:01 p.m.