MLEZ: Fitting parameters of PDF by means of maximum likelihood

In dazamora/IDFtool: Estimation Intensity-Duration-Frequency Curves for Rainfall Series

Fitting parameters of PDF by means of maximum likelihood

Description

This function fits parameters of probability distribution functions (see selecDIST) by means of maximum likelihood and performs evolutionary global optimization via the Differential Evolution algorithm (see DEoptim package).

Usage

MLEZ(Intensity, type, para.int = NULL, silent = TRUE,
  null.on.not.converge = TRUE, pretransf = function(t) return(t))

Arguments

Intensity	a numeric vector with intensity [mm/h] values of different years for a specific time duration (e.g. 5, 15, 120 minutes, etc.).
type	a character specifying the name of the distribution function that will be employed: exponencial, gamma, gev, gumbel, log.normal3, normal, pearson, log.pearson3 and wakeby (see selecDIST).
para.int	Initial parameters as a vector Θ.
silent	a logical to silence the try function wrapping the DEoptim function.
null.on.not.converge	a logical to trigger simple return of NULL if the DEoptim function returns a nonzero convergence status.
pretransf	An optional parameter retransformation function (see Examples) that is useful to guide the optimization run. For example, suppose the first parameter of a three parameter distribution resides in the positive domain, then pretransf(t) = function(t) c(exp(t[1]), t[2], t[3]).

Value

A list of:

• Parameters a list with type of distribution fitted and values of its parameters

Examples

data(inten) 
TEST.MLE <- MLEZ(Intensity = inten[,4], type = "Gumbel", 
                 para.int = NULL, silent = TRUE, 
                 null.on.not.converge = TRUE, 
                 pretransf = function(t) return(t))
## Results: xi = 71.178 ; alpha = 15.204 

dazamora/IDFtool documentation built on Jan. 1, 2023, 3:29 p.m.