cxr_pm_multifit: Multi-species parameter optimization

In cxr: A Toolbox for Modelling Species Coexistence in R

Multi-species parameter optimization

Description

This function is a wrapper for estimating parameters for several focal species, instead of making separate calls to cxr_pm_fit.

Usage

cxr_pm_multifit(
  data,
  model_family = c("BH"),
  focal_column = NULL,
  covariates = NULL,
  optimization_method = c("BFGS", "CG", "Nelder-Mead", "ucminf", "L-BFGS-B", "nlm",
    "nlminb", "Rcgmin", "Rvmmin", "spg", "bobyqa", "nmkb", "hjkb", "nloptr_CRS2_LM",
    "nloptr_ISRES", "nloptr_DIRECT_L_RAND", "DEoptimR", "GenSA"),
  alpha_form = c("none", "global", "pairwise"),
  lambda_cov_form = c("none", "global"),
  alpha_cov_form = c("none", "global", "pairwise"),
  initial_values = NULL,
  lower_bounds = NULL,
  upper_bounds = NULL,
  fixed_terms = NULL,
  bootstrap_samples = 0
)

Arguments

data	named list in which each component is a dataframe with a fitness column and a number of columns representing neighbours
model_family	family of model to use. Available families are BH (Beverton-Holt), LV (Lotka-Volterra), RK (Ricker), and LW (Law-Watkinson). Users may also define their own families and models (see vignette 4).
focal_column	character vector with the same length as data, giving the names of the columns representing intraspecific observations for each species, or numeric vector giving the position of such columns.
covariates	optional named list in which each component is a dataframe with values of each covariate for each observation. The ith component of covariates are the covariate values that correspond to the ith component of data, so they must have the same number of observations.
optimization_method	numerical optimization method.
alpha_form	what form does the alpha parameter take? one of "none" (no alpha in the model), "global" (a single alpha for all pairwise interactions), or "pairwise" (one alpha value for every interaction).
lambda_cov_form	form of the covariate effects on lambda. Either "none" (no covariate effects) or "global" (one estimate per covariate).
alpha_cov_form	form of the covariate effects on alpha. One of "none" (no covariate effects), "global" (one estimate per covariate on every alpha), or "pairwise" (one estimate per covariate and pairwise alpha)
initial_values	list with components "lambda","alpha_intra","alpha_inter","lambda_cov", "alpha_cov", specifying the initial values for numerical optimization. Single values are allowed.
lower_bounds	optional list with single values for "lambda","alpha_intra","alpha_inter","lambda_cov", "alpha_cov".
upper_bounds	optional list with single values for "lambda","alpha_intra","alpha_inter","lambda_cov", "alpha_cov".
fixed_terms	optional named list in which each component is itself a list containing fixed terms for each focal species.
bootstrap_samples	number of bootstrap samples for error calculation. Defaults to 0, i.e. no error is calculated.

Value

an object of class 'cxr_pm_multifit' which is a list with the following components:

• model_name: string with the name of the fitness model

• model: model function

• data: data supplied

• taxa: names of the taxa fitted

• covariates: covariate data supplied

• optimization_method: optimization method used

• initial_values: list with initial values

• fixed_terms: list with fixed terms

• lambda: fitted values for lambda, or NULL if fixed

• alpha_intra: fitted values for alpha_intra, or NULL if fixed

• alpha_inter: fitted values for alpha_inter, or NULL if fixed

• lambda_cov: fitted values for lambda_cov, or NULL if fixed

• alpha_cov: fitted values for alpha_cov, or NULL if fixed

• lambda_standard_error: standard errors for lambda, if computed

• alpha_standard_error: standard errors for alpha, if computed

• lambda_cov_standard_error: standard errors for lambda_cov, if computed

• alpha_cov_standard_error: standard errors for alpha_cov, if computed

• log_likelihood: log-likelihoods of the fits

Examples

# fit three species at once
data("neigh_list")
data <- neigh_list[1:3]
# keep only fitness and neighbours columns
for(i in 1:length(data)){
  data[[i]] <- data[[i]][,2:length(data[[i]])]
}
# be explicit about the focal species
focal.sp <- names(data)
# covariates: salinity
data("salinity_list")
salinity <- salinity_list[1:3]
# keep only salinity column
for(i in 1:length(salinity)){
  salinity[[i]] <- data.frame(salinity = salinity[[i]][,2:length(salinity[[i]])])
}

  fit_3sp <- cxr_pm_multifit(data = data,
                             optimization_method = "bobyqa",
                             model_family = "BH",
                             focal_column = focal.sp,
                             covariates = salinity,
                             alpha_form = "pairwise",
                             lambda_cov_form = "global",
                             alpha_cov_form = "global",
                             initial_values = list(lambda = 1,
                                                   alpha_intra = 0.1,
                                                   alpha_inter = 0.1,
                                                   lambda_cov = 0.1, 
                                                   alpha_cov = 0.1),
                             lower_bounds = list(lambda = 0.01,
                                                 alpha_intra = 0,
                                                 alpha_inter = 0,
                                                 lambda_cov = 0, 
                                                 alpha_cov = 0),
                             upper_bounds = list(lambda = 100,
                                                 alpha_intra = 1,
                                                 alpha_inter = 1,
                                                 lambda_cov = 1, 
                                                 alpha_cov = 1),
                             bootstrap_samples = 3)
  # brief summary
  summary(fit_3sp)
  # interaction matrix
  fit_3sp$alpha_matrix

cxr documentation built on Oct. 27, 2023, 1:08 a.m.