optimizer_smooth_model {SpatialModelsZAMG} | R Documentation |
this function optimizes the coefficients of the best fitted linear models
(from the function model_selection
) via smooth modeling
and with maximum likelihood estimation
optimizer_smooth_model(m_select, 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 |
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.
https://www.hydrol-earth-syst-sci.net/14/2527/2010/hess-14-2527-2010.pdf
model_selection
, optimizer_biv_hr_model
# load function output from model_selection sd_m_select = get(data("sd_m_select")) swe_m_select = get(data("swe_m_select")) # perform optimization sd_optim = optimizer_smooth_model(m_select = sd_m_select, method = c("nlminb","ucminf")) swe_optim = optimizer_smooth_model(m_select = swe_m_select, method = "nlminb", itnmax = 500)