MarginPlots: Histograms, quantile and probability plots for the...

In SGB: Simplicial Generalized Beta Regression

Histograms, quantile and probability plots for the z(u)-transforms of parts

Description

These functions draw a plot for each part in the dataset.

Usage

hzbeta(u, obj, weight = rep(1,dim(u)[1]) )
qzbeta(u, obj, weight = rep(1,dim(u)[1]) )
pzbeta(u, obj, weight = rep(1,dim(u)[1]) )

Arguments

u	data matrix of compositions (independent variables) (N \times D); D: number of parts
obj	list, result of regSGB. See regSGB.
weight	vector of length n; positive observation weights, default rep(1,n).

Details

Let U follow a SGB(shape1,scale,shape2) distribution. Then the composition

Z=C[(U/scale)^{shape1}]

is called the z(u)-transform of U.
Z follows a Dirichlet(shape2) distribution and each part Z_i, i=1,...,D is Beta-distributed with parameters (shape2[i],sum(shape2)-shape2[i]).
Goodness of fit plots are produced for the parts of the z(u)-transforms against the Beta distribution. Each function creates D plots, where D is the number of parts.
hzbeta: histograms and the corresponding Beta-densities,
qzbeta: marginal quantile plots,
pzbeta: marginal probability plots.
If weight is specified, weighted histgrams, quantile and probability plots are drawn.

Value

D plots are produced comparing the marginal distribution of the parts of the z(u) compositions with the theoretical Beta distribution.

Examples

## Arctic lake data
data(arc)
# Compositions
ua <- arc[,1:3]

# SGB regression
data(oilr)

# plot
par(mfrow=c(3,3))
hzbeta(ua,oilr)
qzbeta(ua,oilr)
pzbeta(ua,oilr)

SGB documentation built on May 29, 2024, 4 a.m.