likelihood.augmented | R Documentation |
This function uses the likelihood
function and adds the linear constraints used in
fit.gev
and fit.gpd
to produce the
augmented Lagrangian version of the GEV or GP negative
log-likelihood function.
likelihood.augmented(parameters, x.in, model = c("gev", "gpd"), lagrangian.multiplier = rep(0, length(x.in) + 2), penalty.parameter = 1000)
parameters |
Numerical vector containing the location, scale,
and shape parameters for the GEV or the scale and shape
parameters for the GP. If NULL,
|
x.in |
Time series of class xts. |
model |
String determining whether to calculate the initial parameters of the GEV ("gev") or GP ("gpd") function. Default = "gev" |
lagrangian.multiplier |
Lagrangian multipliers used to weight the linear contribution of the constraints. In most cases all of them are zero, since optimization of the GEV/GP likelihood usually doesn't take place inside a region of constraint violations. When supplying this parameter it has to have the same length as present number of constraints: number of points in x.in + 2. Default = 0 for all constraints. |
penalty.parameter |
Penalty parameter used to weight the quadratic contribution of the constraints. In the end of a typical constrained GEV or GP optimization this parameter is 1000. Default = 1000. |
A convenience function not used by the fitting routines.
It is only meant to work with constant parameters and no covariates.
x.in is not called "x", since the call grad( func
= likelihood, x = parameters, ... )
wouldn't be possible.
Numerical value of the augmented negative log likelihood.
Philipp Mueller
Other optimization: fit.gev.default
,
fit.gev.list
, fit.gev.xts
,
fit.gev
, fit.gpd.default
,
fit.gpd.list
, fit.gpd.xts
,
fit.gpd
,
likelihood.gradient.augmented
,
likelihood.gradient
,
likelihood.initials
,
likelihood
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