optimizer_smooth_model_MEV_pwm {SpatialModelsZAMG} | R Documentation |
this function optimizes the coefficients of the best fitted linear MEV models
(from the function model_selection_MEV
) via probability weighted moments optimization
optimizer_smooth_model_MEV_pwm(m_select, data, method = c("nlminb","BFGS", "ucminf","Nelder-Mead"), follow.on = FALSE, itnmax = NULL, printParam = FALSE)
m_select |
this input should be a list including max_data, covariables and models as in the output of the function |
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 |
follow.on |
logical value; if |
itnmax |
if provided as a vector of the same length as the length of |
printParam |
logical value; if |
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. |
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.
model_selection_MEV
, optimizer_smooth_model_MEV_pwm