
gevglmFit
fits empirical or simulated data to the distribution,
print
print method for a fitted GEV object of class ...,
plot
plot method for a fitted GEV object,
summary
summary method for a fitted GEV object,
gevglmprofPlot
profile log-likelihoods for return levels,
gevglmprofxiPlot
profile log-likelihoods for shape parameters. }gevglmFit(x, y = NULL, gumbel = FALSE, mul = NULL, sigl = NULL, shl = NULL,
mulink = identity, siglink = identity, shlink = identity, show = FALSE,
method = "Nelder-Mead", maxit = 10000, ...)
## S3 method for class 'gevglmFit':
print(x, \dots)
## S3 method for class 'gevglmFit':
plot(x, which = "ask", \dots)
## S3 method for class 'gevglmFit':
summary(object, doplot = TRUE, which = "all", \dots)
gevglmprofPlot(object, m, xlow, xup, conf = 0.95, nint = 100)
gevglmprofxiPlot(object, xlow, xup, conf = 0.95, nint = 100)
shl
and and
shlink
are omitted.m
.optim
for details).ydat
that contain covariates for generalized linear
modelling of the location, scale and shape parameters repectively
(or NULL
(the defa"gevglmFit"
.TRUE
(the default), print details of
the fit."gevglmFit"
.NULL
(the default) for stationary
fitting). The number of rows should be the same as the length
of xdat
.control
argument of
optim
.
[plot][sgevglmFit
returns a list containing the following components.
A subset of these components are printed after the fit. If show
is TRUE
, then assuming that successful convergence is
indicated, the components nllh
, mle
and se
are always printed.mul
, sigl
and shl
.optim
. A zero indicates successful convergence.gevglmprofPlot
gevglmprofxiPlot
a plot of the profile likelihood is produced, with a horizontal line
representing a profile confidence interval with confidence coefficient
conf
.gevSim
.
Parameter Estimation:
gevglmFit
fits by the Maximum-likelihood approach the generalized
extreme value distribution, including generalized linear modelling
of each parameter.
Methods:
print.gevglm
, plot.gevglm
and summary.gevglm
are print, plot, and summary methods for a fitted object of class
gevglmFit
.
Profile Likelihood:
gevglmprofPlot
and gevglmprofxiPlot
produce profile
log-likelihoods for shape parameters and m
year/block return
levels for stationary GEV models using the output of the function
gevFit
.
Nonstationary Models:
For non-stationary fitting it is recommended that the covariates
within the generalized linear models are (at least approximately)
centered and scaled (i.e. the columns of ydat
should be
approximately centered and scaled).## SOURCE("fExtremes.54A-ExtremesGlm")
## Simulated GEV Data:
xmpExtremes("Start: Simulte GEV Sample >")
x = gevSim(model = list(shape = 0.25, location = 0, scale = 1), n = 1000)
par(mfrow = c(2, 2))
plot(x, main = "Simulated GEV Data")
# Fit GEV Data:
fit = gevglmFit(x)
print(fit)
# Summarize Results:
summary(fit, which = c(TRUE, TRUE, TRUE, FALSE))
## Simulated GEV Data:
xmpExtremes("Next: Estimate Parameters >")
##> x = gevSim(model = list(shape = 0, location = 0, scale = 1), n = 1000)
##> par(mfrow = c(2, 2))
##> plot(x, main = "Simulated Gumbel Data")
# Fit GEV Data:
##> fit = gevglmFit(x, gumbel = TRUE)
##> print(fit)
# Summarize Results:
##> summary(fit, which = c(TRUE, TRUE, TRUE, FALSE))
## Portpirie Data:
xmpExtremes("Next: Fit Portpirie Data >")
par(mfrow = c(2, 1))
data(portpirie)
fit = gevglmFit(portpirie[, 2])
gevglmprofPlot(fit, m = 10, 4.1, 5)
title(main = "Portpirie")
gevglmprofxiPlot(fit, -0.3, 0.3)
title(main = "Portpirie")
## Interactive Plot:
##> par(mfrow = c(2, 2))
##> plot(fit)
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