tests/testthat/test-calibrate.R
In ncsu-landscape-dynamics/rpops: Pest or Pathogen Spread Model
context("test-calibrate")
test_that("Model stops if files don't exist or aren't the correct extension", {
infected_file_list <-
system.file("extdata", "simple2x2", "infected.tif", package = "PoPS")
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple2x2", "total_plants.tif", package = "PoPS")
prior_means <- c(0, 21, 1, 500, 0, 0)
prior_cov_matrix <- matrix(0, nrow = 6, ncol = 6)
number_of_observations <- 1
prior_number_of_observations <- 0
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <- system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
expect_error(calibrate(infected_years_file = infected_years_file,
number_of_observations = number_of_observations,
prior_number_of_observations = prior_number_of_observations,
prior_means = prior_means,
prior_cov_matrix = prior_cov_matrix,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list = "",
host_file_list = host_file_list,
total_populations_file = host_file_list),
file_exists_error)
})
test_that("Model stops if success metric is incorrect", {
infected_file_list <-
system.file("extdata", "simple2x2", "infected.tif", package = "PoPS")
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple2x2", "total_plants.tif", package = "PoPS")
prior_means <- c(0, 21, 1, 500, 0, 0)
prior_cov_matrix <- matrix(0, nrow = 6, ncol = 6)
number_of_observations <- 1
prior_number_of_observations <- 0
success_metric <- "yeah"
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <- system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
expect_error(calibrate(infected_years_file = infected_years_file,
number_of_observations = number_of_observations,
prior_number_of_observations = prior_number_of_observations,
prior_means = prior_means,
prior_cov_matrix = prior_cov_matrix,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list = infected_file_list,
host_file_list = host_file_list,
total_populations_file = host_file_list,
success_metric = success_metric),
success_metric_error)
})
test_that("ABC calibration has correctly formatted returns with multiple output
comparisons with mask", {
skip_on_ci()
skip_on_covr()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "ABC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "summary_outputs"
output_folder_path <- tempdir()
success_metric <- "mcc"
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
use_initial_condition_uncertainty <- FALSE
use_host_uncertainty <- FALSE
weather_type <- "deterministic"
temperature_coefficient_sd_file <- ""
precipitation_coefficient_sd_file <- ""
dispersers_to_soils_percentage <- 0
quarantine_directions <- ""
multiple_random_seeds <- FALSE
file_random_seeds <- NULL
use_soils <- FALSE
soil_starting_pest_file <- ""
start_with_soil_populations <- FALSE
county_level_infection_data <- FALSE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty,
weather_type,
temperature_coefficient_sd_file,
precipitation_coefficient_sd_file,
dispersers_to_soils_percentage,
quarantine_directions,
multiple_random_seeds,
file_random_seeds,
use_soils,
soil_starting_pest_file,
start_with_soil_populations,
county_level_infection_data = county_level_infection_data)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns and runs with a
single output comparison with network", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_single.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "month"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "network"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "year"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "rmse and distance"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns and runs with a
single output comparison with network", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_single.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "month"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "network"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "year"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "accuracy, precision, recall, and specificity"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns and runs with a
single output comparison with network", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_single.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "month"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "network"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "year"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "quantity, allocation, and configuration"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns/runs with host and initial
condition uncertainty", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_single.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "infected_wsd.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host_w_sd2.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "month"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "year"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "quantity, allocation, and configuration"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns/runs with county level data", {
infected_years_file <-
system.file("extdata", "simple20x20", "county_infected_years.gpkg", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "county_infected.gpkg", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host_w_sd2.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-01-14"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-07")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "accuracy"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
use_distance <- FALSE
use_configuration <- FALSE
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
weather_type <- "deterministic"
temperature_coefficient_sd_file <- ""
precipitation_coefficient_sd_file <- ""
dispersers_to_soils_percentage <- 0
quarantine_directions <- ""
multiple_random_seeds <- FALSE
file_random_seeds <- NULL
use_soils <- FALSE
soil_starting_pest_file <- ""
start_with_soil_populations <- FALSE
county_level_infection_data <- TRUE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty,
county_level_infection_data = county_level_infection_data)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("MCMC calibration has correctly formatted returns with multiple output
comparisons", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "mcc"
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with multiple output
comparisons with mask", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "rmse and distance"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with multiple output
comparisons with mask", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "accuracy, precision, recall, and specificity"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with multiple output
comparisons with mask", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "quantity, allocation, and configuration"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with host and initial condition
uncertainty", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "infected_wsd.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host_w_sd2.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "quantity, allocation, and configuration"
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with host and initial condition
uncertainty", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "county_infected_years.gpkg", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "county_infected.gpkg", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host_w_sd2.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-01-14"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-05")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "quantity, allocation, and configuration"
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
county_level_infection_data <- TRUE
quarantine_directions <- ""
weather_type <- "deterministic"
temperature_coefficient_sd_file <- ""
precipitation_coefficient_sd_file <- ""
dispersers_to_soils_percentage <- 0
quarantine_directions <- ""
multiple_random_seeds <- FALSE
file_random_seeds <- NULL
use_soils <- FALSE
soil_starting_pest_file <- ""
start_with_soil_populations <- FALSE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty,
county_level_infection_data = county_level_infection_data)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
ncsu-landscape-dynamics/rpops documentation built on May 1, 2024, 10:21 a.m.
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context("test-calibrate")
test_that("Model stops if files don't exist or aren't the correct extension", {
infected_file_list <-
system.file("extdata", "simple2x2", "infected.tif", package = "PoPS")
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple2x2", "total_plants.tif", package = "PoPS")
prior_means <- c(0, 21, 1, 500, 0, 0)
prior_cov_matrix <- matrix(0, nrow = 6, ncol = 6)
number_of_observations <- 1
prior_number_of_observations <- 0
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <- system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
expect_error(calibrate(infected_years_file = infected_years_file,
number_of_observations = number_of_observations,
prior_number_of_observations = prior_number_of_observations,
prior_means = prior_means,
prior_cov_matrix = prior_cov_matrix,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list = "",
host_file_list = host_file_list,
total_populations_file = host_file_list),
file_exists_error)
})
test_that("Model stops if success metric is incorrect", {
infected_file_list <-
system.file("extdata", "simple2x2", "infected.tif", package = "PoPS")
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple2x2", "total_plants.tif", package = "PoPS")
prior_means <- c(0, 21, 1, 500, 0, 0)
prior_cov_matrix <- matrix(0, nrow = 6, ncol = 6)
number_of_observations <- 1
prior_number_of_observations <- 0
success_metric <- "yeah"
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <- system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
expect_error(calibrate(infected_years_file = infected_years_file,
number_of_observations = number_of_observations,
prior_number_of_observations = prior_number_of_observations,
prior_means = prior_means,
prior_cov_matrix = prior_cov_matrix,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list = infected_file_list,
host_file_list = host_file_list,
total_populations_file = host_file_list,
success_metric = success_metric),
success_metric_error)
})
test_that("ABC calibration has correctly formatted returns with multiple output
comparisons with mask", {
skip_on_ci()
skip_on_covr()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "ABC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "summary_outputs"
output_folder_path <- tempdir()
success_metric <- "mcc"
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
use_initial_condition_uncertainty <- FALSE
use_host_uncertainty <- FALSE
weather_type <- "deterministic"
temperature_coefficient_sd_file <- ""
precipitation_coefficient_sd_file <- ""
dispersers_to_soils_percentage <- 0
quarantine_directions <- ""
multiple_random_seeds <- FALSE
file_random_seeds <- NULL
use_soils <- FALSE
soil_starting_pest_file <- ""
start_with_soil_populations <- FALSE
county_level_infection_data <- FALSE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty,
weather_type,
temperature_coefficient_sd_file,
precipitation_coefficient_sd_file,
dispersers_to_soils_percentage,
quarantine_directions,
multiple_random_seeds,
file_random_seeds,
use_soils,
soil_starting_pest_file,
start_with_soil_populations,
county_level_infection_data = county_level_infection_data)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns and runs with a
single output comparison with network", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_single.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "month"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "network"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "year"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "rmse and distance"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns and runs with a
single output comparison with network", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_single.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "month"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "network"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "year"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "accuracy, precision, recall, and specificity"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns and runs with a
single output comparison with network", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_single.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "month"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "network"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "year"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "quantity, allocation, and configuration"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns/runs with host and initial
condition uncertainty", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_single.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "infected_wsd.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host_w_sd2.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "month"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "year"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "quantity, allocation, and configuration"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("ABC calibration has correctly formatted returns/runs with county level data", {
infected_years_file <-
system.file("extdata", "simple20x20", "county_infected_years.gpkg", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "county_infected.gpkg", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host_w_sd2.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 0
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-01-14"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-07")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
output_frequency_n <- 1
use_spreadrates <- FALSE
calibration_method <- "ABC"
number_of_iterations <- 10
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "accuracy"
network_filename <-
system.file("extdata", "simple20x20", "segments.csv", package = "PoPS")
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
use_distance <- FALSE
use_configuration <- FALSE
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
weather_type <- "deterministic"
temperature_coefficient_sd_file <- ""
precipitation_coefficient_sd_file <- ""
dispersers_to_soils_percentage <- 0
quarantine_directions <- ""
multiple_random_seeds <- FALSE
file_random_seeds <- NULL
use_soils <- FALSE
soil_starting_pest_file <- ""
start_with_soil_populations <- FALSE
county_level_infection_data <- TRUE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty,
county_level_infection_data = county_level_infection_data)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_generations * generation_size)
})
test_that("MCMC calibration has correctly formatted returns with multiple output
comparisons", {
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- NULL
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
success_metric <- "mcc"
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gt(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with multiple output
comparisons with mask", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "rmse and distance"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with multiple output
comparisons with mask", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "accuracy, precision, recall, and specificity"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with multiple output
comparisons with mask", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "initial_infection.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "quantity, allocation, and configuration"
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with host and initial condition
uncertainty", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "infected_years.tif", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "infected_wsd.tif", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host_w_sd2.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-02-11"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-24")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "quantity, allocation, and configuration"
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
test_that("MCMC calibration has correctly formatted returns with host and initial condition
uncertainty", {
skip_on_ci()
infected_years_file <-
system.file("extdata", "simple20x20", "county_infected_years.gpkg", package = "PoPS")
number_of_observations <- 68
prior_number_of_observations <- 0
prior_means <- c(0, 0, 0, 0, 0, 0)
prior_cov_matrix <- matrix(ncol = 6, nrow = 6, 0)
params_to_estimate <- c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
number_of_generations <- 2
generation_size <- 2
pest_host_table <-
system.file("extdata", "pest_host_table_singlehost_nomort.csv", package = "PoPS")
competency_table <-
system.file("extdata", "competency_table_singlehost.csv", package = "PoPS")
infected_file_list <-
system.file("extdata", "simple20x20", "county_infected.gpkg", package = "PoPS")
host_file_list <-
system.file("extdata", "simple20x20", "host_w_sd2.tif", package = "PoPS")
total_populations_file <-
system.file("extdata", "simple20x20", "all_plants.tif", package = "PoPS")
temp <- FALSE
temperature_coefficient_file <- ""
precip <- FALSE
precipitation_coefficient_file <- ""
model_type <- "SI"
latency_period <- 14
time_step <- "week"
season_month_start <- 1
season_month_end <- 12
start_date <- "2003-01-01"
end_date <- "2003-01-14"
use_lethal_temperature <- FALSE
temperature_file <- ""
lethal_temperature <- -30
lethal_temperature_month <- 1
mortality_frequency <- "Year"
mortality_frequency_n <- 1
management <- FALSE
treatment_dates <- c("2003-01-05")
treatments_file <- ""
treatment_method <- "ratio"
natural_kernel_type <- "exponential"
anthropogenic_kernel_type <- "cauchy"
natural_dir <- "NONE"
natural_kappa <- 0
anthropogenic_dir <- "NONE"
anthropogenic_kappa <- 0
pesticide_duration <- c(0)
pesticide_efficacy <- 1.0
mask <- system.file("extdata", "simple20x20", "mask.tif", package = "PoPS")
output_frequency <- "week"
output_frequency_n <- 1
movements_file <- ""
use_movements <- FALSE
percent_natural_dispersal <- 1.0
anthropogenic_distance_scale <- 0.1
number_of_iterations <- 10
number_of_cores <- 2
start_exposed <- FALSE
generate_stochasticity <- TRUE
establishment_stochasticity <- TRUE
movement_stochasticity <- TRUE
dispersal_stochasticity <- FALSE
establishment_probability <- 0.5
dispersal_percentage <- 0.99
quarantine_areas_file <- ""
use_quarantine <- FALSE
use_spreadrates <- FALSE
calibration_method <- "MCMC"
use_overpopulation_movements <- FALSE
overpopulation_percentage <- 0
leaving_percentage <- 0
leaving_scale_coefficient <- 1
exposed_file_list <- ""
verbose <- TRUE
write_outputs <- "None"
output_folder_path <- ""
network_filename <- ""
use_survival_rates <- FALSE
survival_rate_month <- 3
survival_rate_day <- 15
survival_rates_file <- ""
network_movement <- "walk"
success_metric <- "quantity, allocation, and configuration"
use_initial_condition_uncertainty <- TRUE
use_host_uncertainty <- TRUE
county_level_infection_data <- TRUE
quarantine_directions <- ""
weather_type <- "deterministic"
temperature_coefficient_sd_file <- ""
precipitation_coefficient_sd_file <- ""
dispersers_to_soils_percentage <- 0
quarantine_directions <- ""
multiple_random_seeds <- FALSE
file_random_seeds <- NULL
use_soils <- FALSE
soil_starting_pest_file <- ""
start_with_soil_populations <- FALSE
data <- calibrate(infected_years_file,
number_of_observations,
prior_number_of_observations,
prior_means,
prior_cov_matrix,
params_to_estimate,
number_of_generations,
generation_size,
pest_host_table = pest_host_table,
competency_table = competency_table,
infected_file_list,
host_file_list,
total_populations_file,
temp,
temperature_coefficient_file,
precip,
precipitation_coefficient_file,
model_type,
latency_period,
time_step,
season_month_start,
season_month_end,
start_date,
end_date,
use_survival_rates,
survival_rate_month,
survival_rate_day,
survival_rates_file,
use_lethal_temperature,
temperature_file,
lethal_temperature,
lethal_temperature_month,
mortality_frequency,
mortality_frequency_n,
management,
treatment_dates,
treatments_file,
treatment_method,
natural_kernel_type,
anthropogenic_kernel_type,
natural_dir,
natural_kappa,
anthropogenic_dir,
anthropogenic_kappa,
pesticide_duration,
pesticide_efficacy,
mask,
output_frequency,
output_frequency_n,
movements_file,
use_movements,
start_exposed,
generate_stochasticity,
establishment_stochasticity,
movement_stochasticity,
dispersal_stochasticity,
establishment_probability,
dispersal_percentage,
quarantine_areas_file,
use_quarantine,
use_spreadrates,
use_overpopulation_movements,
overpopulation_percentage,
leaving_percentage,
leaving_scale_coefficient,
calibration_method,
number_of_iterations,
exposed_file_list,
verbose,
write_outputs,
output_folder_path,
network_filename,
network_movement,
success_metric,
use_initial_condition_uncertainty,
use_host_uncertainty,
county_level_infection_data = county_level_infection_data)
expect_length(data$posterior_means, 8)
expect_vector(data$posterior_means, ptype = double(), size = 8)
expect_gt(data$posterior_means[1], 0)
expect_gt(data$posterior_means[2], 0)
expect_lte(data$posterior_means[2], 1000)
expect_gte(data$posterior_means[3], 0)
expect_lte(data$posterior_means[3], 1)
expect_gt(data$posterior_means[4], 0)
expect_gte(data$posterior_means[5], 0)
expect_gte(data$posterior_means[6], 0)
expect_gt(data$posterior_means[7], 0)
expect_gte(data$posterior_means[8], 0)
expect_is(data$posterior_cov_matrix, class = "matrix")
expect_type(data$posterior_cov_matrix, "double")
expect_equal(nrow(data$posterior_cov_matrix), 8)
expect_equal(ncol(data$posterior_cov_matrix), 8)
expect_type(data$total_number_of_observations, "double")
expect_equal(data$total_number_of_observations, number_of_observations)
expect_equal(nrow(data$raw_calibration_data), number_of_iterations)
})
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