glo.hist.fit: Maximum Likelihood Fitting for the GLO distribution in the...

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

Maximum Likelihood Fitting for the GLO distribution in the presence of Historical Data

Description

ML fitting procedures for sample of both systematic and historical data. Function structure inspired by gev.fit in ismev and similar to gev.hist.fit. Arguments naming and likelihood implementation follow Stedinger and Cohn (1986).

Usage

glo.hist.fit(
  xdat,
  k = 0,
  h = NULL,
  X0 = NULL,
  binomialcens = FALSE,
  mulink = identity,
  siglink = identity,
  shlink = identity,
  muinit = NULL,
  siginit = NULL,
  shinit = NULL,
  show = TRUE,
  method = "Nelder-Mead",
  maxit = 10000,
  ...
)

Arguments

xdat	vector of historical and systematic/observed data - the first k elements of the vector should be the historical events
k	number of historical events available. These events should be stored as the first observations of the xdat vector
h	length of years covered by the information of the historical period
X0	the perception threshold which is exceeded by the historical events. This is most likely different from the lowest historical value. It should indicate a value after which we are confident the event would have been recorded or left traces
binomialcens	a logical value. Indicates whether the actual k values are to be used, or if only the information that the threshold X0 has been exceeded is used.
mulink	the link function for the location parameter - default to identity
siglink	the link function for the scale parameter - default to identity
shlink	the link function for the shape parameter - default to identity
muinit	initial values for the location parameter
siginit	initial values for the scale parameter
shinit	initial values for the shape parameter
show	Logical; if TRUE (the default), print details of the fit.
method	The optimization method (see optim for details)
maxit	The maximum number of iterations
...	Other control parameters for the optimization. These are passed to components of the control argument of optim.

Value

An object of the class glo.fit.

References

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

Stedinger, J.R. and Cohn, T.A., 1986. Flood frequency analysis with historical and paleoflood information. Water resources research, 22(5), pp.785-793.

Macdonald, N., Kjeldsen, T.R., Prosdocimi, I. and Sangster, H., 2014. Reassessing flood frequency for the Sussex Ouse, Lewes: the inclusion of historical flood information since AD 1650. Natural Hazards and Earth System Sciences, 14(10), pp.2817-2828.

See Also

gev.hist.fit, dglo, Vignette on historical data

Examples

set.seed(7821567)
xx <- rglo(500, 40, 6, -0.2)
xxsist <- xx[471:500]; xxhist <- xx[1:470][xx[1:470] > 80]
glo.hist.fit(c(xxhist,xxsist), k = length(xxhist), h = 470, X0 = 80)
glo.hist.fit(c(xxhist,xxsist), k = length(xxhist), h = 470, X0 = 80, binomialcens = TRUE)
glod.fit(xxsist) ## notice the higher standard errors

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