fit_it: Fit a model using Genaralized Simulated Annealing (SA)...

In ibartomeus/traitmatch: Package to predict trait matching from species interactions

Description

Fit the paramaters of a given model to data using SA. Can be slow for large datasets.

Usage

fit_it(model, Tlevel1, Tlevel2, mean_Tlevel1, sd_Tlevel1, pars = c(a0 = 0, a1
  = 0, b0 = 0, b1 = 0), par_lo = c(a0 = -5000, a1 = -5000, b0 = -5000, b1 =
  -5000), par_hi = c(a0 = 5000, a1 = 5000, b0 = 5000, b1 = 5000),
  max.time = 1800)

Arguments

model	One of the three models implemented integrated_model, niche_model or neutral_model.
Tlevel1	Vector of trait values of the first interaction partner.
Tlevel2	Vector of trait values of the second interaction partner.
mean_Tlevel1	Mean of trait values of the first interaction partner. Can be weighted or not, and can use independent information on the trait distribution to be calculated.
sd_Tlevel1	Standard deviation of trait values of the first interaction partner. Can be weighted or not, and can use independent information on the trait distribution to be calculated.
pars	a vector of the form c(a0 = 0, a1 = 0, b0 = 0, b1 = 0) with the initial parameters.
max.time	value in seconds to stop the estimation of params. Set to 1800 (30 minuts)
pars_lo	a vector of the form c(a0 = 0, a1 = 0, b0 = 0, b1 = 0) with the lower limits that the parameters can reach. You can use a priosy information to constrain the posible values of the parameters (e.g. the slope, a1, has to be positive)
pars_hi	a vector of the form c(a0 = 0, a1 = 0, b0 = 0, b1 = 0) with the higher limits that the parameters can reach. You can use a priosy information to constrain the posible values of the parameters

Value

a vector with the estimated parameters of the form c(a0 = x, a1 = y, b0 = z, b1 = w)

Note

See readme file here: https://github.com/ibartomeus/trait_match

Author(s)

Dominique Gravel and Ignasi Bartomeus

ibartomeus/traitmatch documentation built on May 18, 2019, 1:29 a.m.