kappacvd.fit: Maximum Likelihood Fitting for the Kappa distribution - CV...

In ilapros/ilaprosUtils: A Miscellaneous Collection of Functions I Tend to Use

Maximum Likelihood Fitting for the Kappa distribution - CV model

Description

Maximum-likelihood fitting for the Kappa distribution, including generalized linear modelling of each parameter. The function differs from kappad.fit because it uses a different parametrisation of the distribution based on the \tau, the ratio of the scale parameter and the location parameter. This means that when regression models are applied for the location, these also affect the scale. The function allows any parameter to be kept fixed and to not be estimated.

Usage

kappacvd.fit(
  xdat,
  ydat = NULL,
  mul = NULL,
  taul = NULL,
  shl = NULL,
  sh2l = NULL,
  mulink = identity,
  taulink = identity,
  shlink = identity,
  sh2link = identity,
  muinit = NULL,
  tauinit = NULL,
  shinit = NULL,
  sh2init = NULL,
  show = TRUE,
  method = "Nelder-Mead",
  optimPars = NULL,
  maxit = 10000,
  fixedPars = list(mu = NULL, sig = NULL, sh = NULL, sh2 = NULL),
  ...
)

Arguments

xdat	A numeric vector of data to be fitted
ydat	A matrix of covariates for generalized linear modelling of the parameters (or NULL (the default) for stationary fitting). The number of rows should be the same as the length of xdat
mul	Numeric vectors of integers, giving the columns of ydat that contain covariates for generalized linear modelling of the location parameter (or NULL (the default) if the corresponding parameter is stationary)
taul	As mul for the tau parameter
shl	As mul for the shape parameter
sh2l	As mul for the second shape parameter
mulink	the link function for the location parameter - default to identity
taulink	the link function for the tau parameter - default to identity
shlink	the link function for the shape parameter - default to identity
sh2link	the link function for the second shape parameter - default to identity
muinit	initial values for the location parameter
tauinit	initial values for the tau parameter
shinit	initial values for the shape parameter
sh2init	initial values for the second shape parameter
show	Logical; if TRUE (the default), print details of the fit.
method	The optimization method (see optim for details)
optimPars	A string with other parameters to pass into optim. For example, depending on method, one could have "lower = 10, upper = 20"
maxit	The maximum number of iterations
fixedPars	a named list to fix any of the distribution parameter to a given value. When the named parameter is set to NULL its value is estimated.
...	Other control parameters for the optimization. These are passed to components of the control argument of optim.

Value

An object of the kappacv.fit class - with values which mirror the ones of the gev.fit class in ismev.

In the output the vals matrix gives the location and scale values obtained as scale = \tau * location.

References

Hosking, J.R.M. and Wallis, J.R., 2005. Regional frequency analysis: an approach based on L-moments. Cambridge university press.

See Also

kappad.fit

Examples

set.seed(12)
x <- runif(120)
y <- rkappa(120,loc = 40+3*x,
   scale = 0.2*(40+3*x), sh = -0.2, sh2=-0.4)
fit1 <- kappacvd.fit(y, show=FALSE)
fit1
## now add a regression model for the location
fit2 <- kappacvd.fit(y, ydat = cbind(x), mul=1, show=FALSE)
fit2
## now a fit with a fixed shape parameter 
fitf2 <- kappacvd.fit(y, show=FALSE, fixedPars = list(sh2 = -0.4))
fitf2 ## only three parameters are estimated 

ilapros/ilaprosUtils documentation built on April 6, 2023, 4:44 a.m.