MarginPlots | R Documentation |
These functions draw a plot for each part in the dataset.
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]) )
u |
data matrix of compositions (independent variables) |
obj |
list, result of regSGB. See |
weight |
vector of length |
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.
D
plots are produced comparing the marginal distribution of the parts of the z(u)
compositions with the theoretical Beta distribution.
## 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)
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