optimizer_smooth_model_MEV_mle: Optimize the coefficients of the best fitted linear MEV...

In SpatialModelsZAMG: Spatial Modeling of Extreme Events

Description

this function optimizes the coefficients of the best fitted linear MEV models
(from the function model_selection_MEV) via smooth modeling
and with maximum likelihood estimation

Usage

optimizer_smooth_model_MEV_mle(m_select, max_data, method = c("nlminb","BFGS",
                       "ucminf","Nelder-Mead"),
                       follow.on = FALSE, itnmax = NULL,
                       printParam = FALSE)

Arguments

m_select	this input should be a list including max_data, covariables and models as in the output of the function model_selection
max_data	matrix with the annual maximum of year j at station i at entry (i,j), meaning stations in rows, years in columns.
method	optimization method(s) for external function optimx, this can also be a vector. possible methods are: Nelder-Mead, BFGS, CG, L-BFGS-B, nlm, nlminb, spg, ucminf, newuoa, bobyqa, nmkb, hjkb, Rcgmin, Rvmmin default is method = c("nlminb","BFGS","ucminf","Nelder-Mead")
follow.on	logical value; if TRUE, and there are multiple methods, then the last set of coefficients from one method is used as the starting set for the next default is FALSE
itnmax	if provided as a vector of the same length as the length of method, this gives the maximum number of iterations or function values for the corresponding method. if a single number is provided, this will be used for all methods
printParam	logical value; if TRUE, the GEV parameters during the optimization are printed. this might be useful to check the proper functioning of the optimization (shape parameter should be approximately between -0.5 and 0.5) default is FALSE

Value

a list with

summary	a summary of the optimization results, including an information message whether the optimization was successful or not and which method delivered the best coefficients
coefficients	a list with the optimized coefficients. containing: scalecoeff, shapecoeff and all_coeff

References

Blanchet, J. & Lehning, M. (2010): Mapping snow depth return levels: smooth spatial modeling versus station interpolation. Hydrology and Earth System Sciences 14(12): 2527-2544.

Schellander, H., Lieb, A. and Hell, T. (2019) 'Error Structure of Metastatistical and Generalized Extreme Value Distributions for Modeling Extreme Rainfall in Austria', Earth and Space Science, 6, pp. 1616-1632. doi: 10.1029/2019ea000557.

See Also

model_selection_MEV, optimizer_smooth_model_MEV_pwm

SpatialModelsZAMG documentation built on Nov. 11, 2019, 3 p.m.