## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----eval = FALSE-------------------------------------------------------------
#  install.packages("spectre")

## ----eval = FALSE-------------------------------------------------------------
#  install.packages("devtools")
#  devtools::install_github("r-spatialecology/spectre")

## -----------------------------------------------------------------------------
library(spectre)

## -----------------------------------------------------------------------------
# load "observed" alpha-, beta- and gamma-diversity values of the random species composition
alpha_list <- minimal_example_data$alpha_list # richness
beta_list <- minimal_example_data$beta_list # Bray-Curtis dissimilarity
total_gamma <- dim(minimal_example_data$species_list)[1] # 20 species

## ----message = FALSE, warning = FALSE-----------------------------------------
# Calculate objective_matrix from (modelled) alpha-diversity and Bray-Curtis dissimilarity

objective_matrix <- spectre::generate_commonness_matrix_from_gdm(
  gdm_predictions = beta_list, 
  alpha_list = alpha_list)

## ----message = FALSE, warning = FALSE-----------------------------------------
res <- spectre::run_optimization_min_conf(
  alpha_list = alpha_list,
  total_gamma = total_gamma,
  target = objective_matrix,
  max_iterations = 1000) # n iterations

## ----message = FALSE----------------------------------------------------------
error_c <- spectre::calc_commonness_error(x = res, objective_matrix = objective_matrix)

## ----include = TRUE, out.width="50%", fig.align = "center"--------------------
# With an increasing number of iterations, the solution matrix improved
spectre::plot_error(x = res)

# Plot commonness error between objective function and solution matrix
spectre::plot_commonness(x = res, target = objective_matrix)

