abundance_projection: Project abundances from population dynamics models

In cxr: A Toolbox for Modelling Species Coexistence in R

Project abundances from population dynamics models

Description

The function projects a number of steps of a time-discrete model, with model parameters taken from a 'cxr_pm_multifit' object or as function arguments.

Usage

abundance_projection(
  cxr_fit = NULL,
  model_family = NULL,
  alpha_form = NULL,
  lambda_cov_form = NULL,
  alpha_cov_form = NULL,
  lambda = NULL,
  alpha_matrix = NULL,
  lambda_cov = NULL,
  alpha_cov = NULL,
  covariates = NULL,
  timesteps = 2,
  initial_abundances = 0
)

Arguments

cxr_fit	object of type 'cxr_pm_multifit'. If this is not specified, all parameters below are needed.
model_family	acronym for model family. Included by default in 'cxr' are 'BH' (Beverton-Holt), 'RK' (Ricker), 'LW' (Law-Watkinson), 'LV' (Lotka-Volterra).
alpha_form	character, either "none","global", or "pairwise".
lambda_cov_form	character, either "none" or "global".
alpha_cov_form	character, either "none","global", or "pairwise".
lambda	named vector with lambda values for all taxa to be projected.
alpha_matrix	square matrix with taxa names in rows and columns.
lambda_cov	optional named matrix with covariates in columns and taxa in rows, representing the effect of each covariate on the lambda parameter of each taxa.
alpha_cov	optional list. Each element of the named list represents the effects of a covariate over alpha values. Thus, each list element contains a square matrix of the same dimensions as 'alpha_matrix', as returned from the function 'cxr_pm_fit'. Note that for alpha_cov_form = "global", all columns in this matrix are the same, as there is a single value per species.
covariates	matrix or dataframe with covariates in columns and timesteps in rows.
timesteps	number of timesteps to project.
initial_abundances	named vector of initial abundances for all taxa.

Value

named matrix with projected abundance values for each taxa at each timestep.

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