View source: R/family.extremes.R
hurea | R Documentation |
Estimating the parameter of the Husler-Reiss angular surface distribution by maximum likelihood estimation.
hurea(lshape = "loglink", zero = NULL, nrfs = 1,
gshape = exp(3 * ppoints(5) - 1), parallel = FALSE)
lshape , gshape |
Details at |
nrfs , zero , parallel |
Details at |
The Husler-Reiss angular surface distribution has a probability density function that can be written
f(y;s) = (s / (4 * sqrt(2*pi) * y(1-y)^2))
exp(-(2 + s^2 * logit y)^2 / [8 s^2])
for 0<y<1
and positive shape parameter s
.
The mean of Y
is currently unknown to me,
as well as its quantiles.
Hence s
is currently returned as the
fitted values.
Fisher-scoring is implemented.
An object of class "vglmff"
(see vglmff-class
).
The object is used by modelling functions
such as vglm
,
and vgam
.
This VGAM family function handles multiple responses.
It may struggle and/or fail
when s
is close to 0.
Some comments about āuā-shaped versus unimodal
densities accommodated by this distribution
are at dhurea
.
T. W. Yee
Mhalla, L. and de Carvalho, M. and Chavez-Demoulin, V. (2019). Regression-type models for extremal dependence. Scandinavian Journal of Statistics, 46, 1141ā1167.
hurea
.
nn <- 100; set.seed(1)
hdata <- data.frame(x2 = runif(nn))
hdata <-
transform(hdata, # Cannot generate proper random variates!
y1 = rbeta(nn, shape1 = 0.5, shape2 = 0.5), # "U" shaped
y2 = rnorm(nn, 0.65, sd = exp(-3 - 4 * x2)))
# Multiple responses:
hfit <- vglm(cbind(y1, y2) ~ x2, hurea, hdata, trace = TRUE)
coef(hfit, matrix = TRUE)
summary(hfit)
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