Validator: R6 class representing a pattern-oriented validator.
In poems: Pattern-Oriented Ensemble Modeling System
Validator R Documentation
R6 class representing a pattern-oriented validator.
Description
R6 class for pattern-oriented validation and simulation
model ensemble selection. Pattern-oriented validation is a statistical
approach to compare patterns generated in simulations against observed
empirical patterns.
The class wraps functionality for the validation approach,
typically utilizing an external library, the default being the approximate Bayesian
computation (ABC) abc library, and includes methods for
resolving non-finite metrics, centering and scaling the validator inputs, running
the validator analysis, and generating diagnostics (see abc).
Super classes
poems::GenericClass -> poems::GenericModel -> Validator
Public fields
attachedA list of dynamically attached attributes (name-value pairs).
Active bindings
model_attributesA vector of model attribute names.
simulation_parametersA data frame of sample model parameters for each simulation.
simulation_summary_metricsA data frame of result summary metrics for each simulation.
observed_metric_targetsA vector of observed targets for each summary metric.
random_seedA seed for randomizing the order of the simulation samples (no randomization is utilized when left NULL).
random_indicesRandomized simulation sample indices for the validator inputs and consequently the validator results when random seed is used.
non_finite_replacementsA list of numeric values or function names (character strings) or direct assignments (assigned or loaded via source paths) for replacing NAs in specified (list names) summary metrics.
input_center_scale_valuesA nested list of center and scale values for validator input parameters/metrics.
output_dirDirectory path for validator (default: abc) regression diagnostic and other outputs.
validation_call_functionDynamically assigned function: function(observed_metric_targets, simulation_parameters, simulation_summary_metrics, tolerance, method, ...) for calling the validation function (default calls abc library function).
validator_return_objectObject returned by the validator function (see abc documentation if using default).
selected_simulationsA data frame of simulation sample indices and weights selected/assigned by the validation function (abc by default).
attribute_aliasesA list of alternative alias names for model attributes (form: alias = "attribute") to be used with the set and get attributes methods.
error_messagesA vector of error messages encountered when setting model attributes.
warning_messagesA vector of warning messages encountered when setting model attributes.
Methods
Public methods
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Inherited methods
Method new()
Initialization method sets given attributes individually and/or from a list.
Usage
Validator$new(...)
Arguments
...Parameters passed via a params list or individually.
templateTemplate population model containing fixed (non-sampled) attributes.
Method run()
Pre-processes inputs, runs validator function for input parameters, and stores the function (and optionally diagnostic) outputs (see abc documentation if using the default).
Usage
Validator$run(
simulation_parameters = NULL,
simulation_summary_metrics = NULL,
observed_metric_targets = NULL,
tolerance = 0.01,
method = "neuralnet",
output_diagnostics = FALSE,
...
)
Arguments
simulation_parametersA data frame of sample model parameters for each simulation.
simulation_summary_metricsA data frame of result summary metrics for each simulation.
observed_metric_targetsA vector of observed targets for each summary metric.
toleranceTolerance or proportion of models to select.
methodValidator algorithm to be applied (default is a neural network algorithm - see abc documentation) .
output_diagnosticsBoolean to indicate whether or not to output diagnostics (PDF file - default is FALSE).
...Additional validator parameters passed individually (see abc documentation if using default).
Method resolve_nonfinite_metrics()
Attempts to resolve any non-finite simulation summary metric values (and optionally changing them to NAs) via the non finite replacements parameter (a list of values/functions for replacing non-finite values).
Usage
Validator$resolve_nonfinite_metrics(use_nas = TRUE)
Arguments
use_nasBoolean to indicate whether or not to replace all non-finite values with NAs (default is TRUE).
Method center_scale_inputs()
Centers and scales the model parameters, result summary metrics and observed targets.
Usage
Validator$center_scale_inputs()
Method generate_diagnostics()
Generates the validation diagnostics (see abc documentation if using default) as a PDF file in the output directory.
Usage
Validator$generate_diagnostics(output_dir = NULL)
Arguments
output_dirOutput directory path for the diagnostics PDF file (must be present if not already set within validator class object).
Method clone()
The objects of this class are cloneable with this method.
Usage
Validator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
# Example parameter sample data
sample_data <- data.frame(
growth_rate_max = round(log(seq(1.11, 1.30, 0.01)), 3),
harvest_rate = seq(0.11, 0.30, 0.01),
initial_n = seq(105, 200, 5),
density_max = seq(132, 170, 2)
)
# Example simulation result summary metrics
summary_metric_data <- data.frame(
trend_n = seq(10, -9, -1),
total_h = seq(70, 355, 15)
)
# Create a validator for selecting the 'best' example models
validator <- Validator$new(
simulation_parameters = sample_data,
simulation_summary_metrics = summary_metric_data,
observed_metric_targets = c(trend_n = 0, total_h = 250),
output_dir = tempdir()
)
suppressWarnings(validator$run(tolerance = 0.25, output_diagnostics = TRUE))
dir(tempdir(), "*.pdf") # plus validation diagnostics (see abc library documentation)
validator$selected_simulations # top 5 models
poems documentation built on April 4, 2025, 5:07 a.m.
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| Validator | R Documentation |
R6 class representing a pattern-oriented validator.
Description
R6 class for pattern-oriented validation and simulation
model ensemble selection. Pattern-oriented validation is a statistical
approach to compare patterns generated in simulations against observed
empirical patterns.
The class wraps functionality for the validation approach,
typically utilizing an external library, the default being the approximate Bayesian
computation (ABC) abc library, and includes methods for
resolving non-finite metrics, centering and scaling the validator inputs, running
the validator analysis, and generating diagnostics (see abc).
Super classes
poems::GenericClass -> poems::GenericModel -> Validator
Public fields
attachedA list of dynamically attached attributes (name-value pairs).
Active bindings
model_attributesA vector of model attribute names.
simulation_parametersA data frame of sample model parameters for each simulation.
simulation_summary_metricsA data frame of result summary metrics for each simulation.
observed_metric_targetsA vector of observed targets for each summary metric.
random_seedA seed for randomizing the order of the simulation samples (no randomization is utilized when left NULL).
random_indicesRandomized simulation sample indices for the validator inputs and consequently the validator results when random seed is used.
non_finite_replacementsA list of numeric values or function names (character strings) or direct assignments (assigned or loaded via source paths) for replacing NAs in specified (list names) summary metrics.
input_center_scale_valuesA nested list of center and scale values for validator input parameters/metrics.
output_dirDirectory path for validator (default:
abc) regression diagnostic and other outputs.validation_call_functionDynamically assigned function:
function(observed_metric_targets, simulation_parameters, simulation_summary_metrics, tolerance, method, ...)for calling the validation function (default callsabclibrary function).validator_return_objectObject returned by the validator function (see
abcdocumentation if using default).selected_simulationsA data frame of simulation sample indices and weights selected/assigned by the validation function (
abcby default).attribute_aliasesA list of alternative alias names for model attributes (form:
alias = "attribute") to be used with the set and get attributes methods.error_messagesA vector of error messages encountered when setting model attributes.
warning_messagesA vector of warning messages encountered when setting model attributes.
Methods
Public methods
Inherited methods
Method new()
Initialization method sets given attributes individually and/or from a list.
Usage
Validator$new(...)
Arguments
...Parameters passed via a params list or individually.
templateTemplate population model containing fixed (non-sampled) attributes.
Method run()
Pre-processes inputs, runs validator function for input parameters, and stores the function (and optionally diagnostic) outputs (see abc documentation if using the default).
Usage
Validator$run( simulation_parameters = NULL, simulation_summary_metrics = NULL, observed_metric_targets = NULL, tolerance = 0.01, method = "neuralnet", output_diagnostics = FALSE, ... )
Arguments
simulation_parametersA data frame of sample model parameters for each simulation.
simulation_summary_metricsA data frame of result summary metrics for each simulation.
observed_metric_targetsA vector of observed targets for each summary metric.
toleranceTolerance or proportion of models to select.
methodValidator algorithm to be applied (default is a neural network algorithm - see
abcdocumentation) .output_diagnosticsBoolean to indicate whether or not to output diagnostics (PDF file - default is FALSE).
...Additional validator parameters passed individually (see
abcdocumentation if using default).
Method resolve_nonfinite_metrics()
Attempts to resolve any non-finite simulation summary metric values (and optionally changing them to NAs) via the non finite replacements parameter (a list of values/functions for replacing non-finite values).
Usage
Validator$resolve_nonfinite_metrics(use_nas = TRUE)
Arguments
use_nasBoolean to indicate whether or not to replace all non-finite values with NAs (default is TRUE).
Method center_scale_inputs()
Centers and scales the model parameters, result summary metrics and observed targets.
Usage
Validator$center_scale_inputs()
Method generate_diagnostics()
Generates the validation diagnostics (see abc documentation if using default) as a PDF file in the output directory.
Usage
Validator$generate_diagnostics(output_dir = NULL)
Arguments
output_dirOutput directory path for the diagnostics PDF file (must be present if not already set within validator class object).
Method clone()
The objects of this class are cloneable with this method.
Usage
Validator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
# Example parameter sample data
sample_data <- data.frame(
growth_rate_max = round(log(seq(1.11, 1.30, 0.01)), 3),
harvest_rate = seq(0.11, 0.30, 0.01),
initial_n = seq(105, 200, 5),
density_max = seq(132, 170, 2)
)
# Example simulation result summary metrics
summary_metric_data <- data.frame(
trend_n = seq(10, -9, -1),
total_h = seq(70, 355, 15)
)
# Create a validator for selecting the 'best' example models
validator <- Validator$new(
simulation_parameters = sample_data,
simulation_summary_metrics = summary_metric_data,
observed_metric_targets = c(trend_n = 0, total_h = 250),
output_dir = tempdir()
)
suppressWarnings(validator$run(tolerance = 0.25, output_diagnostics = TRUE))
dir(tempdir(), "*.pdf") # plus validation diagnostics (see abc library documentation)
validator$selected_simulations # top 5 models
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