tapnet (version 0.3)

fit_tapnet: Fit the tapnet model to a network

Description

Estimates the parameters of the tapnet model by log-likelihood based on the oberved network(s)

Usage

fit_tapnet(
  tapnet,
  ini = NULL,
  tmatch_type_pem = "normal",
  tmatch_type_obs = "normal",
  lambda = 0,
  method = "Nelder",
  maxit = 50000,
  hessian = FALSE,
  obj_function = "multinom",
  fit.delta = TRUE
)

Arguments

tapnet

a tapnet object;

ini

initial parameter values for the optimization; optional;

tmatch_type_pem

type of trait matching function for latent traits, currently "normal" or "shiftlnorm";

tmatch_type_obs

type of trait matching function for observed traits, currently "normal" or "shiftlnorm";

lambda

LASSO shrinkage factor for latent trait parameters;

method

Optimization method (most derivative-based approaches will not work! SANN is a (slow) alternative to the default);

maxit

Maximum number of steps for optimization;

hessian

logical: output hessian for calculation of standard errors?

obj_function

Objective function for the optimization, either "multinom" or "sq_diff";

fit.delta

logical; should the parameter delta be fitted? It allows tapnet to down-weigh the importance of trait matching relative to abundances.

Value

A tapnet-fit object, containing the tapnet model parameters as entries "par_opt", the settings of the tmatch_type for PEMs and observed traits, the parameter set for lambda, the optimisation method set, along with its maxit-value, and, finally, the output of the call to optim, including the target value (the negative log-likelihood), the convergence report and the parameters as fitted at the transformed scale. Note that the entries under "opt" will not be the same as those under "par_opt"!

Details

The core function for using the tapnet approach: it fits the model to the data (= networks). Then, the estimated parameters can be used to predict to other networks (using predict_tapnet).

References

Benadi et al. in prep

Examples

# NOT RUN {
 # takes about 35 s
 data(Tinoco)
 tap <- make_tapnet(tree_low = plant_tree, tree_high = humm_tree, networks = networks[2:3], 
        traits_low = plant_traits, traits_high = humm_traits, npems_lat = 4)
 fit <- fit_tapnet(tap) # fits to networks 2 and 3 only
 str(fit)
# }
# NOT RUN {
# }