# estim_options

Sets options for estim.pars

## Contents

## Syntax

**estim_options** (key, val)

## Description

Sets options for estimation one by one, some apply to methods nm and mmea, others to mmea only

Input

- no input: print values to screen
- one input:

'default': sets options at default values any other key (see below): print value to screen

- two inputs

'loss_function': 'sb': multiplicative symmetric bounded (default) 'su': multiplicative symmetric unbounded 're': relative error (not recommanded)

'filter': 0: do not use filters; 1: use filters (default)

'pars_init_method': 0: get initial estimates from automatized computation 1: read initial estimates from .mat file (for continuation) 2: read initial estimates from pars_init file (default)

'results_output': 0 - only saves data to .mat (no printing to html or screen and no figures) - use this for (automatic) continuations 1, -1 - no saving to .mat file, prints results to html (1) or screen (-1), shows figures but does not save them 2, -2 - saves to .mat file, prints results to html (2) or screen (-2), shows figures but does not save them 3, -3 - like 2 (or -2), but also prints graphs to .png files (default is 3) 4, -4 - like 3 (or -3), but also prints html with implied traits 5, -5 - like 4 (or -4), but includes related species in the implied traits 6 - like 5, but also prints html with population traits

'method': 'no': do not estimate 'nm': Nelder-Mead method (default) 'mmea': multimodal evolutionary algorithm

'max_fun_evals': maximum number of function evaluations (default 10000)

'report': 0 - do not report 1 - report steps to screen (default)

'max_step_number': maximum number of steps (default 500)

'tol_simplex': tolerance for how close the simplex points must be together to call them the same (default 1e-4)

'tol_fun': tolerance for how close the loss-function values must be together to call them the same (default 1e-4)

'simplex_size': fraction added (subtracted if negative) to the free parameters when building the simplex (default 0.05)

'search_method' (method mmea only): 'mm1' - use shade method (default) 'mm2' - do not estimate

'num_results' (method mmea only): The size for the multimodal algorithm's population. The author recommended 100 for SHADE ('search_method mm1', default) 18 * number of free parameters for L-SHADE ('search method mm2')

'gen_factor' (method mmea only): percentage to build the ranges for initializing the first population of individuals (default 0.5)

'bounds_from_ind' (method mmea only): 0: use ranges from pseudodata if exist (these ranges not existing will be taken from data) 1: use ranges from data (default)

'max_calibration_time' (method mmea only): maximum calibration time in minutes (default 30)

'num_runs' (method mmea only): the number of independent runs to perform (default 1)

'add_initial' (method mmea only): if the initial individual is added in the first population. 1: activated 0: not activated (default)

'refine_initial' (method mmea only): if the initial individual is refined using Nelder-Mead. 0: not activated (default) 1: activated

'refine_best' (method mmea only): if the best individual found is refined using Nelder-Mead. 0: not activated (default) 1: activated

'refine_running' (method mmea only): If to apply local search to some individuals while simulation is running 0: not activated (default) 1: activated

'refine_run_prob' (method mmea only): The probability to apply a local search to an individual while algorithm is running (default 0.05)

'refine_firsts' (method mmea only): If to apply a local search to the first population 0: not activated (default) 1: activated (this is recommended when the algorithm is not able to converge to good solutions till the end of its execution)

'verbose_options' (method mmea only): The number of solutions to show from the set of optimal solutions found by the algorithm through the calibration process (default 10)

'verbose' (method mmea only): prints some information while the calibration process is running 0: not activated (default) 1: activated

'seed_index' (method mmea only): index of vector with values for the seeds used to generate random values each one is used in a single run of the algorithm (default 1, must be between 1 and 30)

'ranges' (method mmea only): Structure with ranges for the parameters to be calibrated (default empty) one value (factor between [0, 1], if not: 0.01 is set) to increase and decrease the original parameter values. two values (min, max) for the minimum and maximum range values. Consider: (1) Use min < max for each variable in ranges. If it is not, then the range will be not used (2) Do not take max/min too high, use the likely ranges of the problem (3) Only the free parameters (see 'pars_init_my_pet' file) are considered Set range with cell string of name of parameter and value for range, e.g. estim_options('ranges',{'kap', [0.3 1]}} Remove range-specification with e.g. estim_options('ranges', {'kap'}} or estim_options('ranges', 'kap'}

'results_display (method mmea only)': Basic - Does not show results in screen (default) Best - Plots the best solution results in DEBtool style Set - Plots all the solutions remarking the best one html with pars (best pars and a measure of the variance of each parameter in the solutions obtained for example) Complete - Joins all options with zero variate data with input and a measure of the variance of all the solutions considered

'results_filename (method mmea only)': The name for the results file (solutionSet_my_pet_time)

'save_results' (method mmea only): If the results output images are going to be saved 0: no saving (default) 1: saving

'mat_file' (method mmea only): The name of the .mat-file with results from where to initialize the calibration parameters (only useful if pars_init_method option is equal to 1 and if there is a result file) This file is outputted as results_my_pet.mat ("my pet" replaced by name of species) using method nm, results_output 0, 2-6.

Output

- no output, but globals are set to values or values are printed to screen

## Remarks

See **estim_pars** for application of the option settings. Initial estimates are controlled by option 'pars_init_method', but the free-setting is always taken from the pars_init file A typical estimation procedure is

- first use estim_options('pars_init_method',2) with estim_options('max_step_number',500),
- then estim_options('pars_init_method',1), repeat till satiation or convergence (using arrow-up + enter)
- type mat2pars_init in the Matlab's command window to copy the results in the .mat file to the pars_init file

The default setting for max_step_number on 500 in method nm is on purpose not enough to reach convergence. Continuation (using arrow-up + 'enter' after 'pars_init_method' set on 1) is important to restore simplex size.

Typical simplex options are also used in the evolutionary algorithm via local_search in shade and lshade

## Example of use

estim_options('default'); estim_options('filter', 0); estim_options('method', 'no')