Type: Package
Title: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
Version: 0.2.7
Maintainer: Falco Joannes Bargagli Stoffi <fbargaglistoffi@hsph.harvard.edu>
Description: Provides a new method for interpretable heterogeneous treatment effects characterization in terms of decision rules via an extensive exploration of heterogeneity patterns by an ensemble-of-trees approach, enforcing high stability in the discovery. It relies on a two-stage pseudo-outcome regression, and it is supported by theoretical convergence guarantees. Bargagli-Stoffi, F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects. arXiv preprint <doi:10.48550/arXiv.2009.09036>.
License: GPL-3
URL: https://github.com/NSAPH-Software/CRE
BugReports: https://github.com/NSAPH-Software/CRE/issues
Depends: R (≥ 3.5.0)
Imports: MASS, stats, logger, gbm, randomForest, methods, xgboost, RRF, data.table, xtable, glmnet, bartCause, stabs, stringr, SuperLearner, magrittr, ggplot2, arules
Suggests: grf, BART, gnm, covr, knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Copyright: Harvard University
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2024-10-18 18:23:19 UTC; falco
Author: Naeem Khoshnevis ORCID iD [aut], Daniela Maria Garcia ORCID iD [aut], Riccardo Cadei ORCID iD [aut], Kwonsang Lee ORCID iD [aut], Falco Joannes Bargagli Stoffi ORCID iD [aut, cre]
Repository: CRAN
Date/Publication: 2024-10-19 15:00:12 UTC

The CRE package

Description

In health and social sciences, it is critically important to identify subgroups of the study population where a treatment has notable heterogeneity in the causal effects with respect to the average treatment effect. Data-driven discovery of heterogeneous treatment effects (HTE) via decision tree methods has been proposed for this task. Despite its high interpretability, the single-tree discovery of HTE tends to be highly unstable and to find an oversimplified representation of treatment heterogeneity. To accommodate these shortcomings, we propose Causal Rule Ensemble (CRE), a new method to discover heterogeneous subgroups through an ensemble-of-trees approach. CRE has the following features:

  1. provides an interpretable representation of the HTE; 2) allows extensive exploration of complex heterogeneity patterns; and 3) guarantees high stability in the discovery. The discovered subgroups are defined in terms of interpretable decision rules, and we develop a general two-stage approach for subgroup-specific conditional causal effects estimation, providing theoretical guarantees.

Author(s)

Naeem Khoshnevis

Daniela Maria Garcia

Riccardo Cadei

Kwonsang Lee

Falco Joannes Bargagli Stoffi

References

Bargagli-Stoffi, F. J., Cadei, R., Lee, K. and Dominici, F. (2023). Causal rule ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects,arXiv preprint arXiv:2009.09036

See Also

Useful links:

A helper function for cre object

Description

A helper function to plot cre object using ggplot2 package.

Usage

## S3 method for class 'cre'
autoplot(object, ...)

Arguments

object

A cre object.

...

Additional arguments passed to customize the plot.

Value

Returns a ggplot object.

Check input parameters

Description

Checks consistency in input (hyper) parameters for the cre function.

Usage

check_hyper_params(X_names, params)

Arguments

X_names

The observed covariates names.

params

The list of parameters required to run the function.

Value

A modified input params. A list of parameters that might be changed during the checks.

Check input data

Description

Conducts sanity checks for the input data.

Usage

check_input_data(y, z, X, ite = NULL)

Arguments

y

The observed response vector.

z

The treatment vector.

X

The features matrix.

ite

The estimated ITE vector.

Value

The number of data samples.

Check method-related parameters

Description

Checks method-related parameters.

Usage

check_method_params(y, ite, params)

Arguments

y

The observed response vector.

ite

The estimated ITE vector.

params

The list of parameters required to run the method functions.

Value

A modified input params. A list of parameters that might be changed during the checks.

Causal rule ensemble

Description

Performs the Causal Rule Ensemble on a data set with a response variable, a treatment variable, and various features.

Usage

cre(y, z, X, method_params = NULL, hyper_params = NULL, ite = NULL)

Arguments

y

An observed response vector.

z

A treatment vector.

X

A covariate matrix (or a data frame). Should be provided as numerical values.

method_params

The list of parameters to define the models used, including:

  • Parameters for Honest Splitting

    • ratio_dis: The ratio of data delegated to rules discovery (default: 0.5).

  • Parameters for Discovery and Inference

    • ite_method: The method for ITE (pseudo-outcome) estimation (default: "aipw", options: "aipw" for Augmented Inverse Probability Weighting, "cf" for Causal Forest, "bart" for Causal Bayesian Additive Regression Trees, "slearner" for S-Learner, "tlearner" for T-Learner, "xlearner" for X-Learner, "tpoisson" for T-Learner with Poisson regression).

    • learner_ps: The model for the propensity score estimation (default: "SL.xgboost", options: any SuperLearner prediction model i.e., "SL.lm", "SL.svm", used only for "aipw", "bart", "cf" ITE estimators).

    • learner_y: The model for the outcome estimation (default: "SL.xgboost", options: any SuperLearner prediction model i.e., "SL.lm", "SL.svm", used only for "aipw", "slearner", "tlearner" and "xlearner" ITE estimators).

hyper_params

The list of hyper parameters to fine-tune the method, including:

  • General hyper parameters

    • intervention_vars: Array with intervention-able covariates names used for Rules Generation. Empty or null array means that all the covariates are considered as intervention-able (default: NULL).

    • ntrees: The number of decision trees for random forest (default: 20).

    • node_size: Minimum size of the trees' terminal nodes (default: 20).

    • max_rules: Maximum number of generated candidates rules (default: 50).

    • max_depth: Maximum rules length (default: 3).

    • t_decay: The decay threshold for rules pruning. Higher values will carry out an aggressive pruning (default: 0.025).

    • t_ext: The threshold to truncate too generic or too specific (extreme) rules (default: 0.01, range: [0, 0.5)).

    • t_corr: The threshold to define correlated rules (default: 1, range: ⁠[0,+inf)⁠).

    • stability_selection: Method for stability selection for selecting the rules. "vanilla" for stability selection, "error_control" for stability selection with error control and "no" for no stability selection (default: "vanilla").

    • B: Number of bootstrap samples for stability selection in rules selection and uncertainty quantification in estimation (default: 20).

    • subsample: Bootstrap ratio subsample for stability selection in rules selection and uncertainty quantification in estimation (default: 0.5).

  • Method specific hyper parameters

    • offset: Name of the covariate to use as offset (i.e., "x1") for T-Poisson ITE estimation. Use NULL if offset is not used (default: NULL).

    • cutoff: Threshold (percentage) defining the minimum cutoff value for the stability scores for Stability Selection (default: 0.9).

    • pfer: Upper bound for the per-family error rate (tolerated amount of falsely selected rules) for Error Control Stability Selection (default: 1).

ite

The estimated ITE vector. If given both the ITE estimation steps in Discovery and Inference are skipped (default: NULL).

Value

An S3 object composed by:

M

the number of Decision Rules extracted at each step,

CATE

the data.frame of Conditional Average Treatment Effect decomposition estimates with corresponding uncertainty quantification,

method_params

the list of method parameters,

hyper_params

the list of hyper parameters,

rules

the list of rules (implicit form) decomposing the CATE.

Note

Examples



set.seed(123)
dataset <- generate_cre_dataset(n = 400,
                                rho = 0,
                                n_rules = 2,
                                p = 10,
                                effect_size = 2,
                                binary_covariates = TRUE,
                                binary_outcome = FALSE,
                                confounding = "no")
y <- dataset[["y"]]
z <- dataset[["z"]]
X <- dataset[["X"]]

method_params <- list(ratio_dis = 0.5,
                      ite_method ="aipw",
                      learner_ps = "SL.xgboost",
                      learner_y = "SL.xgboost")

hyper_params <- list(intervention_vars = NULL,
                     offset = NULL,
                     ntrees = 20,
                     node_size = 20,
                     max_rules = 50,
                     max_depth = 3,
                     t_decay = 0.025,
                     t_ext = 0.025,
                     t_corr = 1,
                     stability_selection = "vanilla",
                     cutoff = 0.6,
                     pfer = 1,
                     B = 20,
                     subsample = 0.5)

cre_results <- cre(y, z, X, method_params, hyper_params)

Discover rules

Description

Discover the minimal set of rules linearly decomposing the Conditional Average Treatment Effect (CATE).

Usage

discover_rules(X, ite, method_params, hyper_params)

Arguments

X

A covariate matrix.

ite

An estimated ITE.

method_params

A vector of method parameters.

hyper_params

A vector of hyper parameters.

Value

A minimal set of rules linearly decomposing the Conditional Average Treatment Effect (CATE).

Estimate the Conditional Average Treatment Effect

Description

Estimates the Conditional Average Treatment Effect (CATE) by linearly modeling the Individual Treatment Effect by a set of rules.

Usage

estimate_cate(rules_matrix, rules_explicit, ite, B = 1, subsample = 1)

Arguments

rules_matrix

A rules matrix.

rules_explicit

A list of select rules in terms of covariate names.

ite

The estimated ITEs.

B

The number of bootstrap samples for uncertainty quantification in estimation.

subsample

The bootstrap ratio subsample for uncertainty quantification in estimation.

Value

A list with 2 elements: summary: A data frame summarizing the CATE linear decomposition:

Estimate the Individual Treatment Effect (ITE)

Description

Estimates the Individual Treatment Effect given a response vector, a treatment vector, a covariate matrix, and a desired algorithm.

Usage

estimate_ite(y, z, X, ite_method, ...)

Arguments

y

An observed response vector.

z

An observed treatment vector.

X

A covariate matrix.

ite_method

A method for estimating the Individual Treatment Effect. Some methods requires additional parameters. These parameters are mentioned in the indented blocks for each method and their definitions are provided at the end of this parameters list.

  • "slearner": S-Learner. - learner_y

  • "tlearner": T-Learner.

    • learner_y

  • "tpoisson": T-Poisson.

    • offset

  • "xlearner": X-Learner.

    • learner_y

  • "aipw": Augmented Inverse Probability Weighting.

    • learner_ps and learner_y

  • "bart": Bayesian Additive Regression Trees.

    • learner_ps

  • "cf": Causal Forest.

    • learner_ps

...

Additional parameters passed to different models.

Details

Additional parameters

Value

A list of ITE estimates.

Estimate the Individual Treatment Effect (ITE) using Augmented Inverse Probability Weighting (AIPW)

Description

Estimates the Individual Treatment Effect using Augmented Inverse Probability Weighting given a response vector, a treatment vector, a features matrix, an estimation model for the propensity score and estimation model for the outcome.

Usage

estimate_ite_aipw(y, z, X, learner_ps = "SL.xgboost", learner_y = "SL.xgboost")

Arguments

y

An observed response vector.

z

A treatment vector.

X

A features matrix.

learner_ps

A estimation model for the propensity score.

learner_y

A estimation model for the outcome.

Value

A list of ITE estimates.

Estimate the Individual Treatment Effect (ITE) using Bayesian Additive Regression Trees (BART)

Description

Estimates the Individual Treatment Effect using Bayesian Additive Regression Trees given a response vector, a treatment vector, and a features matrix.

Usage

estimate_ite_bart(y, z, X, learner_ps)

Arguments

y

An observed response vector.

z

A treatment vector.

X

A features matrix.

learner_ps

Method for the estimation of the propensity score.

Value

A list of ITE estimates.

Note

The number of samples and the number of burn are set by default equal to 500.

Estimate the Individual Treatment Effect (ITE) using Causal Forest (CF)

Description

Estimates the Individual Treatment Effect using Causal Forest given a response vector, a treatment vector, and a features matrix.

Usage

estimate_ite_cf(y, z, X, learner_ps)

Arguments

y

An observed response vector.

z

A treatment vector.

X

A features matrix.

learner_ps

A method for the estimation of the propensity score.

Value

A list of ITE estimates.

Estimate the Individual Treatment Effect (ITE) using S-Learner

Description

Estimates the Individual Treatment Effect using S-Learner given a response vector, a treatment vector, a features matrix and estimation model for the outcome.

Usage

estimate_ite_slearner(y, z, X, learner_y = "SL.xgboost")

Arguments

y

An observed response vector.

z

A treatment vector.

X

A features matrix.

learner_y

An estimation model for the outcome.

Value

A list of ITE estimates.

Estimate the Individual Treatment Effect (ITE) using T-Learner

Description

Estimates the Individual Treatment Effect using T-Learner given a response vector, a treatment vector, a features matrix and estimation model for the outcome.

Usage

estimate_ite_tlearner(y, z, X, learner_y = "SL.xgboost")

Arguments

y

An observed response vector.

z

A treatment vector.

X

A features matrix.

learner_y

A estimation model for the outcome.

Value

A list of ITE estimates.

Estimate the Individual Treatment Effect (ITE) using T-Poisson regression

Description

Estimates the Individual Treatment Effect using Poisson regression given a response vector, a treatment vector, and a features matrix.

Usage

estimate_ite_tpoisson(y, z, X, offset)

Arguments

y

An observed response vector.

z

A treatment vector.

X

A features matrix.

offset

A name of the covariate to use as offset (i.e. “x1”) to model the corresponding outcome rate. NULL to model directly the outcome counts without offset.

Value

A vector of ITE estimates.

Estimate the Individual Treatment Effect (ITE) using X-Learner

Description

Estimates the Individual Treatment Effect using X-Learner given a response vector, a treatment vector, a features matrix and an estimation model for the outcome.

Usage

estimate_ite_xlearner(y, z, X, learner_y = "SL.xgboost")

Arguments

y

An observed response vector.

z

A treatment vector.

X

A features matrix.

learner_y

A estimation model for the outcome.

Value

A list of ITE estimates.

Estimate the propensity score

Description

Estimates the Propensity Score given a treatment vector and features data frame.

Usage

estimate_ps(z, X, ps_method = "SL.xgboost")

Arguments

z

A treatment vector.

X

A features data frame.

ps_method

An estimation model for the propensity score (default: SL.xgboost).

Value

A vector of propensity score estimates.

Discovery (performance) evaluation

Description

Computes different metrics for discovery evaluation.

Usage

evaluate(ground_truth, prediction)

Arguments

ground_truth

List of true (CDR/EM) values.

prediction

List of predicted (CDR/EM) values.

Value

Intersection over union, precision, recall.

Extract effect modifiers

Description

Extracts the effect modifiers from a list of (causal) decision rules.

Usage

extract_effect_modifiers(rules_list, X_names)

Arguments

rules_list

A list of (causal) decision rules.

X_names

A list of the covariate names.

Value

A list of the effect modifiers.

Extract (causal) decision rules

Description

Extracts causal rules from the random forest or the gradient boosting algorithms.

Usage

extract_rules(treelist, X, max_depth, digits = 2)

Arguments

treelist

A list of decision trees.

X

Features matrix.

max_depth

A number of top levels from each tree considered.

digits

A Number of digits for rounding decision rules to extract conditions.

Value

A vector of (causal) decision rules.

Filter correlated rules

Description

Discards highly correlated rules (i.e., Cov(rule_1,rule_2) > t_{corr}).

Usage

filter_correlated_rules(rules_matrix, rules_list, t_corr)

Arguments

rules_matrix

A rules matrix.

rules_list

A list of rules (names).

t_corr

A threshold to define correlated rules.

Value

A rules matrix without the highly correlated rules (columns).

Filter extreme decision rules

Description

Discards rules with too few or too many observations.

Usage

filter_extreme_rules(rules_matrix, rules_list, t_ext)

Arguments

rules_matrix

A rules matrix.

rules_list

A list of rules (names).

t_ext

A threshold in [0, 0.5) range to truncate too generic or too specific rules.

Value

A rules matrix without the rare/common rules.

Filter irrelevant decision rules using leave-one-out pruning

Description

Filters the irrelevant decision rules. The irrelevant rules are interpreted as an error increase after removing a variable-value pair from the decision rules (see “Interpreting tree ensembles with the inTrees package” by Houtao Deng, 2019).

Usage

filter_irrelevant_rules(rules, X, ite, t_decay)

Arguments

rules

A list of rules.

X

A features matrix.

ite

An estimated ITE.

t_decay

The decay threshold for rules pruning.

Value

A list of “relevant” rules.

Generate CRE synthetic data

Description

Generates synthetic data sets to run simulation for causal inference experiments composed by an outcome vector (y), a treatment vector (z), a covariates matrix (X), and an unobserved individual treatment effects vector (ite). The arguments specify the data set characteristic, including the number of individuals (n), the number of covariates (p), the correlation within the covariates (rho), the number of decision rules (n_rules) decomposing the Conditional Average Treatment Effect (CATE), the treatment effect magnitude (effect_size), the confounding mechanism (confounding), and whether the covariates and outcomes are binary or continuous (binary_covariates, binary_outcome).

Usage

generate_cre_dataset(
  n = 1000,
  rho = 0,
  n_rules = 2,
  p = 10,
  effect_size = 2,
  binary_covariates = TRUE,
  binary_outcome = TRUE,
  confounding = "no"
)

Arguments

n

An integer number that represents the number of observations. Non-integer values will be converted into an integer number.

rho

A positive double number that represents the correlation within the covariates (default: 0, range: [0,1)).

n_rules

The number of causal rules (default: 2, range: {1,2,3,4}).

p

The number of covariates (default: 10).

effect_size

The treatment effect size magnitude (default: 2, range: \geq0).

binary_covariates

Whether to use binary or continuous covariates (default: TRUE).

binary_outcome

Whether to use binary or continuous outcomes (default: TRUE).

confounding

Only for continuous outcome, add confounding variables:

  • "lin" for linear confounding,

  • "nonlin" for non-linear confounding,

  • "no" for no confounding (default).

Details

The covariates matrix is generated with the specified correlation among individuals, and each covariate is sampled either from a Bernoulli(0.5) if binary, or a Gaussian(0,1) if continuous. The treatment vector is sampled from a Bernoulli(\frac{1}{1+ \exp(1-x_1+x_2-x_3)}), enforcing the treatment assignment probabilities to be a function of observed covariates. The potential outcomes (y(0) and y(1)) are then sampled from a Bernoulli if binary, or a Gaussian (with standard deviation equal to 1) if continuous. Their mean is equal to a confounding term (null, linear or non-linear and always null for binary outcome) plus 1-4 decision rules weighted by the treatment effect magnitude. The two potential outcomes characterizes the CATE (and then the unobserved individual treatment effects vector) as the sum of different additive contributions for each decision rules considered (plus an intercept). The final expression of the CATE depends on the treatment effect magnitude and the number of decision rules considered.

The 4 decision rules are:

In example, setting effect_size=4 and n_rules=2:

\text{CATE}(\textbf{x}) = -4 \cdot 1\{x_1 > 0.5; x_2 \leq 0.5\}(\textbf{x}) + 4 \cdot 1\{x_5 > 0.5; x_6 \leq 0.5\}(\textbf{x})

The final outcome vector y is finally computed by combining the potential outcomes according to the treatment assignment.

Value

A list, representing the generated synthetic data set, containing:

y

an outcome vector,

z

a treatment vector,

X

a covariates matrix,

ite

an individual treatment vector.

Note

Set the covariates domain (binary_covariates) and outcome domain (binary_outcome) according to the experiment of interest. Increase complexity in heterogeneity discovery:

Examples

set.seed(123)
dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,
                                effect_size = 2, binary_covariates = TRUE,
                                binary_outcome = TRUE, confounding = "no")

Generate rules

Description

Generates a list of rules characterizing the heterogeneity in the Conditional Average Treatment Effect (CATE) by tree-based methods (i.e., random forest).

Usage

generate_rules(X, ite, ntrees, node_size, max_rules, max_depth)

Arguments

X

A covariate matrix.

ite

A vector of estimated ITE.

ntrees

The number of decision trees for the random forest algorithm.

node_size

Minimum size of the trees' terminal nodes.

max_rules

Maximum number of candidate decision rules.

max_depth

Maximum rules length.

Value

A list of rules (names).

Generate rules matrix

Description

Generates the rules matrix from the feature covariate matrix and a vector of rules. The number of rows in rules_matrix is equal to the number of samples in X, and the number of columns is equal to the number of rules in rules_list. Each element of rules_matrix corresponds to a specific data sample and rule. If the data sample satisfies a rule, the corresponding element in rules_matrix is set to 1. Otherwise, the element is set to 0.

Usage

generate_rules_matrix(X, rules_list)

Arguments

X

Features matrix.

rules_list

A vector of rules.

Value

A causal rules matrix.

Get Logger settings

Description

Returns current logger settings.

Usage

get_logger()

Value

Returns a list that includes logger_file_path and logger_level.

See Also

set_logger for information on setting the log level and file path.

Examples


set_logger("mylogger.log", "INFO")
log_meta <- get_logger()

Honest splitting

Description

Splits data into discovery and inference sub-samples.

Usage

honest_splitting(y, z, X, ratio_dis, ite = NULL)

Arguments

y

An observed response vector.

z

A treatment vector.

X

A features matrix.

ratio_dis

A double number indicating the ratio of data delegated to the discovery sub-sample.

ite

A vector of estimated ITE.

Value

A list containing the discovery and inference sub-samples.

Interpret rules

Description

Replaces the column numbers in the rules vector with their real names.

Usage

interpret_rules(rules, X_names)

Arguments

rules

A vector of rules.

X_names

A vector of real names of the covariates.

Value

A list of explicit (human-readable) rules.

Extend generic plot functions for cre class

Description

A wrapper function to extend generic plot functions for cre class.

Usage

## S3 method for class 'cre'
plot(x, ...)

Arguments

x

A cre object.

...

Additional arguments passed to customize the plot.

Value

Returns a ggplot2 object, invisibly. This function is called for side effects.

Predict individual treatment effect via causal rule ensemble

Description

Predicts individual treatment effect via causal rule ensemble algorithm.

Usage

## S3 method for class 'cre'
predict(object, X, ...)

Arguments

object

A cre object from running the CRE function.

X

A covariate matrix (or data.frame)

...

Additional arguments passed to customize the prediction.

Value

An array with the estimated Individual Treatment Effects

Extend print function for the CRE object

Description

Prints a brief summary of the CRE object

Usage

## S3 method for class 'cre'
print(x, verbose = 2, ...)

Arguments

x

A cre object from running the CRE function.

verbose

Set level of results description details: 0 for only results summary, 1 for results and parameters summary, 2 for results and parameters and rules summary (default 2).

...

Additional arguments passed to customize the results description.

Value

No return value. This function is called for side effects.

Select rules

Description

Given a set of rules, selects the minimal set linearly decomposing the Conditional Average Treatment Effect (CATE) by LASSO (optionally with Stability Selection).

Usage

select_rules(rules_matrix, rules, ite, stability_selection, cutoff, pfer, B)

Arguments

rules_matrix

The rules matrix.

rules

A list of rules.

ite

The estimated ITE.

stability_selection

Stability selection method.

cutoff

Threshold (percentage) defining the minimum cutoff value for the stability scores. Only for stability selection.

pfer

Upper bound for the per-family error rate (tolerated amount of falsely selected rules). Only for stability selection.

B

Number of bootstrap samples.

Value

A minimal set of rules linearly decomposing the CATE.

Set Logger settings

Description

Updates logger settings, including log level and location of the file.

Usage

set_logger(logger_file_path = "CRE.log", logger_level = "INFO")

Arguments

logger_file_path

A path (including file name) to log the messages. (Default: CRE.log)

logger_level

The log level. When a log level is set, all log levels below it are also activated (if implemented). Available levels include:

  • TRACE: Provides verbose detailed logging, including the steps taken to achieve a result, often used for debugging. Activating TRACE will also enable DEBUG, INFO, SUCCESS, WARN, ERROR, and FATAL logs.

  • DEBUG: Provides detailed logging about the flow of the application, used mostly by developers to understand potential issues. Activating DEBUG will also enable INFO, SUCCESS, WARN, ERROR, and FATAL logs.

  • INFO (Default): Standard messages that inform the user about the normal operation of the system. Activating INFO will also enable SUCCESS, WARN, ERROR, and FATAL logs.

  • SUCCESS: Messages indicating successful completion of a particular operation or task. Activating SUCCESS will also enable WARN, ERROR, and FATAL logs.

  • WARN: Warning messages about events that might cause problems in the future, but are not yet errors. Activating WARN will also enable ERROR and FATAL logs.

  • ERROR: Reports an error due to which the system may not be able to achieve its functionality, but the application won't halt. Activating ERROR will also enable FATAL logs.

  • FATAL: Reports very severe error events that will presumably lead the application to abort.

Value

No return value. This function is called for side effects.

Note

Log levels are specified by developers during the initial implementation. Future developers or contributors can leverage these log levels to better capture and document the application's processes and events.

Examples


set_logger("Debug")

Standardize Rules Matrix

Description

Standardize (i.e. mean=0 and stdev=1) the rules matrix.

Usage

standardize_rules_matrix(rules_matrix)

Arguments

rules_matrix

The rules matrix.

Value

Standardized rules matrix

Print summary of CRE object

Description

Prints a brief summary of the CRE object

Usage

## S3 method for class 'cre'
summary(object, verbose = 2, ...)

Arguments

object

A cre object from running the CRE function.

verbose

Set level of results description details: only results summary 0, results+parameters summary 1, results+parameters+rules summary (default 2).

...

Additional arguments passed to customize the results description.

Value

A summary of the CRE object