optimizer_smooth_model_MEV_pwm: 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 probability weighted moments optimization

Usage

optimizer_smooth_model_MEV_pwm(m_select, 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
data	list whose elements are vectors including all observed daily values at one station, the stations have to be the same and used in the same order as the stations used for model selection.
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.