R/scratch.R

In syanco/checkyourself: Simulates a simple agent-based species distribution model

# source("R/makefood.R")
# source("R/eatfood.R")
# source("R/gettissueiso.R")
#
# #declarations
# num_sources <- 3 #number of diet endpoints
# popsize <- c(1000,1000,1000) #size of each pop to simulate
# mu_carb <- c(-1, -10, -30) #vector of mean carbon values for each source
# sd_carb <- c(1,1,1) #vector of carbon source sds
# mu_nit <- c(10,6,11) #vector of carbon discrimination factors (set to 0)
# sd_nit <-c(1,1,1) #vector of nitrogen discrimination factors (set to 0)
# diet_prop <- c(.1, .25, .65) #"real" proportional diet composition
# steps <- 100 #number of steps per model run
# num_individ <- 100 #number of consumers to simulate
#
# #make the food sources
# food <- makefood(3, popsize, mu_carb, sd_carb, mu_nit, sd_nit)
#
# #save diet source data to file for import to MixSIAR
# # savesources(food, filename = "simulated_sources.csv", mu_carb, sd_carb, mu_nit,
# #             sd_nit, popsize)
#
#sample and save diet source data to file for import to MixSIAR
# samplesources(5, food = food, filename = "testsource.csv")
#
# #create and save discrimination factor data
# #(NOTE: model only accomodates TDR = 0)
# savediscrimination(food, filename="simulated_discrimination.csv")
#
# #simulate agents over time
# specimens <- eatfood(num_individ, steps, num_sources, diet_prop, food)
#
# #collect "observed" isotop values
# obs_iso <- getiso(specimens, time = steps, window = 10)
#
# #format to data frame ans save as csv for import to MixSIAR
# formatiso(obs_iso, filename = "simulated_iso.csv")
#
# #sample analysis
# library(MixSIAR)
# library(splancs)
#
# #load consumer/mixture data
# mix <- load_mix_data(filename="simulated_iso.csv",
#                      iso_names=c("d13C","d15N"),
#                      factors=NULL,
#                      fac_random=NULL,
#                      fac_nested=NULL,
#                      cont_effects=NULL)
#
# #load diet source data
# source <- load_source_data(filename="simulated_sources.csv",
#                            source_factors=NULL,
#                            conc_dep=FALSE,
#                            data_type="means",
#                            mix)
# #load discrimination data
# discr <- load_discr_data(filename="simulated_discrimination.csv", mix)
#
# # #make plot of the isoscape
# # plot_data(filename="isospace_plot", plot_save_pdf=TRUE, plot_save_png=FALSE,
# #           mix, source, discr)
# #
# # #calculate convex hull of isoscape
# # calc_area(source=source, mix=mix, discr=discr)
# #
# # #plot priors (using generalist/uninformative prior)
# # plot_prior(alpha.prior=1, source)
# #
# # # Write the JAGS model file
# # model_filename <- "MixSIAR_sim_test_model_040419.txt"
# # resid_err <- FALSE
# # process_err <- TRUE
# # write_JAGS_model(model_filename, resid_err, process_err, mix, source)
#
# # #run the model (using a 'test' lengthed chain)
# # jags.1 <- run_model(run="very short", mix, source, discr, model_filename,
# #                     alpha.prior = 1, resid_err, process_err)
# #
# # #if test works, run analysis length
# # #jags.1 <- run_model(run="normal", mix, source, discr, model_filename,
# # #                    alpha.prior = 1, resid_err, process_err)
# #
# #set JAGS output options
# output_options <- list(summary_save = TRUE,
#                        summary_name = "summary_statistics",
#                        sup_post = FALSE,
#                        plot_post_save_pdf = TRUE,
#                        plot_post_name = "posterior_density",
#                        sup_pairs = FALSE,
#                        plot_pairs_save_pdf = TRUE,
#                        plot_pairs_name = "pairs_plot",
#                        sup_xy = TRUE,
#                        plot_xy_save_pdf = FALSE,
#                        plot_xy_name = "xy_plot",
#                        gelman = TRUE,
#                        heidel = FALSE,
#                        geweke = TRUE,
#                        diag_save = TRUE,
#                        diag_name = "diagnostics",
#                        indiv_effect = FALSE,
#                        plot_post_save_png = FALSE,
#                        plot_pairs_save_png = FALSE,
#                        plot_xy_save_png = FALSE)
#
# #view the output
# output_JAGS(jags.1, mix, source, output_options)