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Type:

Package

Title:

Machine Learning Workflows for Supervised, Unsupervised, and Causal Analysis

Version:

2.4.2

Date:

2026-10-25

Description:

Provides single-function pipelines for applied machine learning with tabular data, as commonly found in psychological, behavioral, and health research. The supervised pipeline, supervised_ml(), performs exploratory checks, preprocessing, training of several models with cross-validation, hyperparameter tuning, test-set evaluation, threshold optimization, calibration, fairness analysis, decision curve analysis, and model interpretation with permutation importance and Shapley values. The unsupervised pipeline, unsupervised_ml(), covers k-means, hierarchical, DBSCAN, Gaussian mixture, and latent class clustering with selection of the number of clusters, dimensionality reduction (PCA, t-SNE, UMAP), and anomaly detection (isolation forest, local outlier factor, Mahalanobis distance). The causal pipeline, causal_ml(), estimates treatment effects with propensity scores, meta-learners, double machine learning, causal forests, and targeted maximum likelihood estimation. Also includes learning-curve based sample size estimation, block and diversity-based data splitting, and export of results to text and JSON.

License:

MIT + file LICENSE

URL:

https://github.com/jventural/PsyML

BugReports:

https://github.com/jventural/PsyML/issues

Encoding:

UTF-8

RoxygenNote:

7.3.2

Depends:

R (≥ 4.1.0)

Imports:

dials, dplyr, ggplot2, grDevices, graphics, hardhat, jsonlite, nnet, parallel, parsnip, pROC, ranger, recipes, rlang, rsample, stats, tibble, tidyr, tidyselect, tune, utils, workflows, xgboost, yardstick

Suggests:

Boruta, bslib, cluster, cobalt, commonmark, dbscan, doParallel, DoubleML, DT, e1071, finetune, flextable, foreach, ggdendro, glmnet, grf, htmltools, httr, httr2, isotree, kernelshap, kernlab, knitr, MatchIt, mclust, mlr3, mlr3learners, officer, patchwork, poLCA, readxl, reticulate, rmarkdown, rpart, Rtsne, shapviz, shiny, shinydashboard, SuperLearner, testthat (≥ 3.0.0), themis, tmle, umap, VIM

VignetteBuilder:

knitr

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-10-26 03:23:54 UTC; PC

Author:

Jose Ventura-Leon

[aut, cre]

Maintainer:

Jose Ventura-Leon <jventuraleon@gmail.com>

Repository:

CRAN

Date/Publication:

2026-11-12 15:00:02 UTC

PsyML: Machine Learning Workflows for Supervised, Unsupervised, and Causal Analysis

Description

PsyML is organized around three approaches to machine learning with tabular data, each run by a single function that returns an object with print(), summary(), plot() and predict() methods.

Supervised learning (predicting a known outcome)

supervised_ml() fits and compares classification or regression models for an outcome that is already observed. It covers exploratory checks, preprocessing, cross-validated training of several models, hyperparameter tuning, test-set evaluation, threshold optimization, calibration, fairness and decision curve analysis, and interpretation with variable importance and Shapley values. Each step is also available as a separate function: preprocess_data(), train_models(), compare_models(), tune_best_model(), evaluate_model(), optimize_threshold(), analyze_fairness(), analyze_dca() and interpret_model(). See the "Other supervised learning" links in each help page.

Unsupervised learning (finding structure without an outcome)

unsupervised_ml() looks for groups and unusual cases when there is no outcome variable. It compares k-means, hierarchical, DBSCAN, Gaussian mixture and latent class clustering, chooses the number of clusters, reduces dimensionality (PCA, t-SNE, UMAP) and detects anomalies (isolation forest, local outlier factor, Mahalanobis distance). cluster_profile() describes the clusters found.

Causal inference (estimating the effect of a treatment)

causal_ml() estimates the effect of a treatment or exposure on an outcome from observational data. It checks overlap and balance, and estimates average and heterogeneous effects with propensity scores (matching, inverse probability weighting, doubly robust), meta-learners (S, T and X), double machine learning, causal forests and targeted maximum likelihood estimation.

Tools shared by the three approaches

Study design: ml_sample_size() estimates the sample size needed for a target performance from learning curves; block_split() and diversity_split() build train and test sets.
Exploratory data analysis: eda_summary() and the other ⁠eda_*()⁠ functions.
Reporting: export_verbose_txt(), export_verbose_json() and generate_report_from_json().

The vignette vignette("three-approaches", package = "PsyML") runs a short example of each approach.

Author(s)

Maintainer: Jose Ventura-Leon jventuraleon@gmail.com (ORCID)

Advanced Residual Analysis

Description

Runs a residual analysis for regression predictions: descriptive statistics, Shapiro-Wilk normality test (on at most 5000 residuals), a simplified heteroscedasticity check (correlation between absolute residuals and predictions), residual outliers (3 x IQR rule), influential points (standardized residuals with absolute value > 3) and an approximate Durbin-Watson statistic.

Usage

advanced_residual_analysis(predictions, target, verbose = TRUE)

Arguments

predictions

Data frame with the observed target and a .pred column with the predictions (e.g. the predictions element of a regression supervised_ml() result).

target

Name of the target variable.

verbose

Logical. Print results (default: TRUE).

Value

An object of class c("supervisedml_residuals", "list") with elements basic_stats, normality_test (htest), homoscedasticity (correlation, p_value, heteroscedastic), outliers (n_outliers, pct_outliers, indices), influential_points (n_influential, indices), autocorrelation (durbin_watson, interpretation), residuals, predicted and observed.

Examples

set.seed(1)
obs <- rnorm(150, 10, 2)
preds <- data.frame(y = obs, .pred = obs + rnorm(150))
ra <- advanced_residual_analysis(preds, target = "y", verbose = FALSE)
ra$autocorrelation

Clinical Utility Analysis (Decision Curve Analysis)

Description

Computes the net benefit of a binary classification model across probability thresholds and compares it with the "treat all" and "treat none" strategies, following Vickers and Elkin (2006).

Usage

analyze_dca(
  predictions,
  target,
  thresholds = seq(0.01, 0.99, by = 0.01),
  verbose = TRUE
)

Arguments

predictions

Data frame with one row per case containing the observed outcome (target, a factor with 2 levels) and the predicted probability of the positive class (column .pred_<positive level>).

target

Name of the outcome variable.

thresholds

Numeric vector of probability thresholds to evaluate (default seq(0.01, 0.99, by = 0.01)).

verbose

Logical (default TRUE). Print the results to the console.

Value

A list with:

dca_data: Data frame with one row per threshold and columns threshold, nb_model (net benefit of the model), nb_all (treat all), nb_none (treat none, always 0), tp_rate and fp_rate (true and false positives divided by n), nnt (number needed to treat, 1 / nb_model, Inf when the net benefit is not positive) and model_useful (TRUE when the model beats both reference strategies).
useful_range: Lowest and highest threshold at which the model is useful (NA, NA if never).
max_net_benefit: Maximum net benefit of the model.
optimal_threshold_dca: Threshold with the maximum net benefit.
prevalence: Observed prevalence of the positive class.
n: Number of cases.

References

Vickers, A. J., & Elkin, E. B. (2006). Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making, 26(6), 565-574.

Examples

set.seed(1)
n <- 300
prob <- plogis(rnorm(n, sd = 1.5))
preds <- data.frame(
  outcome = factor(ifelse(runif(n) < prob, "yes", "no"), levels = c("no", "yes")),
  .pred_yes = prob
)
dca <- analyze_dca(preds, target = "outcome")
dca$useful_range
head(dca$dca_data)

Fairness Analysis of a Classification Model

Description

Evaluates the fairness of a binary classification model by comparing performance metrics across the subgroups of a protected variable. Computes demographic parity, equalized odds, predictive parity, calibration by group and the disparate impact ratio.

Usage

analyze_fairness(
  predictions,
  target,
  protected_var,
  fairness_threshold = 0.8,
  verbose = TRUE
)

Arguments

predictions

Data frame with one row per case containing the observed outcome (target, a factor with 2 levels), the predicted class (.pred_class), the protected variable and, optionally, the probability of the positive class (.pred_<positive level>).

target

Name of the outcome variable.

protected_var

Name of the protected variable (e.g., "sex", "race"). It must have at least 2 groups.

fairness_threshold

Threshold for the four-fifths rule of disparate impact (default 0.8).

verbose

Logical (default TRUE). Print the results to the console.

Value

A list with:

group_metrics: Data frame with one row per group and columns group, n, prevalence, positive_rate, tpr, fpr, ppv, npv, accuracy, mean_prob and mean_prob_pos (mean predicted probability among observed positives).
demographic_parity_diff: Largest difference in positive prediction rates between groups.
equalized_odds_tpr_diff, equalized_odds_fpr_diff: Largest differences in true and false positive rates.
predictive_parity_diff: Largest difference in PPV.
calibration_diff: Largest difference in mean_prob_pos (NA without probabilities).
disparate_impact_ratio: Lowest divided by highest positive prediction rate.
fairness_threshold: Threshold used.
passes_four_fifths: Logical, TRUE if the ratio is at least fairness_threshold.
protected_var: Name of the protected variable.
is_fair: Logical, TRUE if the demographic parity and TPR differences are below 0.1 and the four-fifths rule is met.

Examples

set.seed(1)
n <- 300
sex <- sample(c("female", "male"), n, replace = TRUE)
prob <- plogis(rnorm(n) + ifelse(sex == "male", 0.5, 0))
preds <- data.frame(
  outcome = factor(ifelse(runif(n) < prob, "yes", "no"), levels = c("no", "yes")),
  .pred_yes = prob,
  sex = sex
)
preds$.pred_class <- factor(ifelse(prob >= 0.5, "yes", "no"), levels = c("no", "yes"))

fair <- analyze_fairness(preds, target = "outcome", protected_var = "sex")
fair$group_metrics
fair$disparate_impact_ratio

Analyze Interactions and Nonlinear Effects

Description

Screens for potentially important interactions among the top predictors (categorical x numeric and categorical x categorical pairs) and for nonlinear effects of numeric predictors (comparison of a linear and a quadratic fit with an F test), and suggests interaction terms or splines to improve linear models.

Usage

analyze_interactions_splines(
  data,
  target,
  task,
  importance = NULL,
  top_n = 5,
  verbose = TRUE
)

Arguments

data

Data frame with the data.

target

Name of the target variable.

task

Type of task: "classification" or "regression".

importance

Optional data frame of variable importance with a Variable column (e.g. from get_importance()); its first top_n variables are analyzed. If NULL, the first top_n predictors are used.

top_n

Number of top variables to analyze (default: 5).

verbose

Logical. Print results and suggestions (default: TRUE).

Value

A list with interactions (named list; each element has var1, var2, type and strength), nonlinear (named list; each element has variable, p_value of the quadratic term, r2_gain in percentage points and a recommendation) and summary (list with n_interactions, n_nonlinear, top_interaction and top_nonlinear).

Examples

set.seed(1)
n <- 200
df <- data.frame(x1 = runif(n, -2, 2), x2 = rnorm(n),
                 g = factor(sample(c("a", "b"), n, TRUE)))
df$y <- df$x1^2 + df$x2 + rnorm(n)
res <- analyze_interactions_splines(df, target = "y", task = "regression",
                                    verbose = FALSE)
res$summary

Domain Feature Engineering

Description

Lets the user create new variables from R expressions. Each expression is evaluated using the columns of the data frame. Expressions that fail, or that do not return one value per row, are skipped and reported in failed.

Usage

apply_custom_features(data, custom_features, verbose = TRUE)

Arguments

data

Data frame with the data.

custom_features

Named list: each name is the new variable and each value an R expression given as a string (e.g. "SibSp + Parch").

verbose

Logical. Print progress (default: TRUE).

Value

A list with data (the data frame with the new variables), created (names of the variables created) and failed (names of the expressions that could not be evaluated).

Examples

res <- apply_custom_features(mtcars,
  custom_features = list(power_weight = "hp / wt",
                         heavy = "ifelse(wt > 3, 1, 0)"),
  verbose = FALSE)
head(res$data[, c("power_weight", "heavy")])
res$created

Apply a Classification Threshold

Description

Reclassifies binary predictions with a given probability threshold (for instance, one found with optimize_threshold()).

Usage

apply_threshold(predictions, target, threshold)

Arguments

predictions

Data frame of predictions with the observed target (a factor with two levels) and a probability column named ⁠.pred_<positive class>⁠.

target

Name of the target variable.

threshold

Probability threshold between 0 and 1; observations with probability >= threshold are assigned to the positive class.

Value

The predictions data frame with an added factor column .pred_class_optimized.

Examples

set.seed(1)
x <- rnorm(100)
y <- factor(ifelse(x + rnorm(100) > 0.8, "yes", "no"), levels = c("no", "yes"))
preds <- data.frame(y = y, .pred_yes = plogis(1.5 * x - 1))
out <- apply_threshold(preds, target = "y", threshold = 0.3)
table(out$.pred_class_optimized)

Automatic Feature Engineering

Description

Applies generic transformations that may improve model performance without requiring domain knowledge: (1) suspicious zeros in numeric variables whose non-zero minimum is far from zero are converted to NA; (2) strictly positive variables with skewness > 2 get an additional log1p() version (suffix ⁠_log⁠); (3) variables with more than 5% (and less than 90%) missing values get a missing indicator (suffix ⁠_missing⁠); (4) pairwise products (suffix ⁠_x_⁠) are created among the three numeric variables most correlated with the target.

Usage

auto_feature_engineering(data, target, verbose = TRUE)

Arguments

data

Data frame with the data.

target

Name of the target variable.

verbose

Logical. Print progress (default: TRUE).

Value

A list with data (the modified data frame), new_features (names of the created variables), transformations (list with elements zeros_to_na, log_transform, missing_indicators and/or interactions listing the variables involved), n_original (number of original predictors) and n_new (number of new variables).

Examples

set.seed(1)
n <- 100
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rexp(n)^3)
df$x2[sample(n, 10)] <- NA
df$y <- df$x1 + rnorm(n)
fe <- auto_feature_engineering(df, target = "y", verbose = FALSE)
fe$new_features

Block-Based Train/Test Split for Enhanced Generalization

Description

Splits data into train and test sets by sampling from sequential blocks, ensuring the test set represents different segments of the data distribution. This approach helps evaluate model generalization across different "contexts" or time periods, reducing the risk of overfitting to a specific data segment.

Usage

block_split(
  data,
  order_col = NULL,
  num_blocks = 5,
  test_prop = 0.2,
  test_n = NULL,
  stratify = NULL,
  balance_blocks = TRUE,
  seed = NULL
)

Arguments

data

Data frame to split.

order_col

Column name (character) or index used to order the data (e.g., date, response order, ID). If NULL, uses row order.

num_blocks

Number of sequential blocks to divide the data into (default 5).

test_prop

Proportion of data to use for test set (default 0.20). Alternatively, use test_n to specify exact number.

test_n

Exact number of observations for test set. Overrides test_prop.

stratify

Column name for stratified sampling within blocks (e.g., outcome variable). Ensures proportional class representation in both train and test sets.

balance_blocks

Logical. If TRUE, samples equal amounts from each block. If FALSE, samples proportionally to block size (default TRUE).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

Details

The function addresses the concern that traditional random splits may not adequately test generalization, since train and test come from the same homogeneous distribution. By sampling from different sequential blocks, the test set captures variability across different "periods" or segments.

When to use:

Data collected over time (surveys, longitudinal studies)
Data with natural ordering (response sequence, ID order)
When concerned about temporal or contextual generalization

Stratification: When stratify is specified, the function ensures that within each block, samples are drawn proportionally from each class. This prevents class imbalance issues in train or test sets.

Value

A list of class c("block_split", "list") containing:

train: Training data frame (rows of data in their original order).
test: Test data frame (rows of data in their original order).
train_idx: Sorted row indices of the training observations in data.
test_idx: Sorted row indices of the test observations in data.
block_info: Data frame with one row per block and columns block, start_row, end_row (positions after ordering), block_size and test_sampled (test cases drawn from the block).
settings: List with the configuration used and the realized sizes (n_total, n_train, n_test, num_blocks, test_prop_actual, order_col, stratify, balance_blocks, seed).

References

Ventura-Leon, J. (2026). Block-based sampling for machine learning validation.

Examples

# Simulated survey data collected over 100 days
set.seed(1)
survey_data <- data.frame(
  response_date = as.Date("2025-01-01") + sample(0:99, 150, replace = TRUE),
  x1 = rnorm(150),
  outcome = factor(sample(c("yes", "no"), 150, replace = TRUE))
)

# Basic usage with row order
result <- block_split(survey_data, num_blocks = 5, test_prop = 0.20, seed = 42)
nrow(result$train)
nrow(result$test)

# With date ordering and stratification
result <- block_split(
  data = survey_data,
  order_col = "response_date",
  num_blocks = 5,
  test_prop = 0.20,
  stratify = "outcome",
  seed = 42
)
result

# View block information
result$block_info

SHAP Values of a Fitted Workflow

Description

Computes Kernel SHAP values with kernelshap::kernelshap() for a sample of test observations, using a sample of the training data as background. For binary classification the explained output is the probability of the second class level; for multiclass outcomes the mean absolute SHAP value across classes is returned. Predictors removed by the preprocessing recipe are excluded from the output.

Usage

calculate_shap(
  final_fit,
  train_data,
  test_data,
  target,
  n_samples = 100,
  bg_n = 50,
  verbose = TRUE
)

Arguments

final_fit

A fitted workflow.

train_data

Training data frame (background data).

test_data

Test data frame (observations to explain).

target

Name of the outcome variable.

n_samples

Number of test observations to explain. In classification the sample is stratified by class.

bg_n

Number of background observations sampled from train_data.

verbose

Logical. If TRUE, progress is printed to the console.

Value

NULL if the package kernelshap is not installed or the computation fails; otherwise a list with elements shap_values (data frame of SHAP values, one column per predictor), importance (data frame with columns Variable and Mean_Abs_SHAP), test_sample (explained observations), removed_vars (predictors dropped by the recipe), collinear_vars (predictors with VIF > 5), and sample_design.

Examples


if (requireNamespace("kernelshap", quietly = TRUE)) {
  set.seed(1)
  df <- data.frame(x1 = rnorm(120), x2 = rnorm(120), x3 = rnorm(120))
  df$y <- df$x1 - df$x2 + rnorm(120)
  wf <- workflows::workflow() |>
    workflows::add_formula(y ~ .) |>
    workflows::add_model(parsnip::linear_reg() |> parsnip::set_engine("lm"))
  fit <- parsnip::fit(wf, data = df[1:100, ])
  sh <- calculate_shap(fit, df[1:100, ], df[101:120, ], "y",
                       n_samples = 20, bg_n = 30, verbose = FALSE)
  sh$importance
}

Calibrate Predicted Probabilities

Description

Calibrates the predicted probabilities of a binary classifier using Platt scaling (logistic regression of the outcome on the probabilities) or isotonic regression, and reports the Brier score and the expected calibration error (ECE, 10 bins) before and after calibration. The calibration model is fitted and evaluated on the same predictions, so the reported improvement is optimistic.

Usage

calibrate_probabilities(predictions, target, method = "platt", verbose = TRUE)

Arguments

predictions

Data frame of predictions with the observed target (a factor with two levels) and a probability column named ⁠.pred_<positive class>⁠.

target

Name of the target variable.

method

Calibration method: "platt" (default, sigmoid) or "isotonic".

verbose

Logical. Print results (default: TRUE).

Value

A list with calibration_model (a glm or isoreg object), predictions (the input with an added .pred_calibrated column), target, method, brier_original, brier_calibrated, ece_original and ece_calibrated.

Examples

set.seed(1)
x <- rnorm(300)
y <- factor(ifelse(x + rnorm(300) > 0, "yes", "no"), levels = c("no", "yes"))
preds <- data.frame(y = y, .pred_yes = plogis(3 * x))
cal <- calibrate_probabilities(preds, target = "y", verbose = FALSE)
c(cal$brier_original, cal$brier_calibrated)

Automated Causal Inference Pipeline

Description

Complete causal inference pipeline for observational data. It includes a causal exploratory analysis, propensity score methods (matching, inverse probability weighting and augmented IPW), Double Machine Learning, causal forests, meta-learners (S, T and X) and, optionally, TMLE and Python methods (EconML through 'reticulate').

Usage

causal_ml(
  data,
  treatment,
  outcome,
  covariates = NULL,
  methods = c("propensity", "dml", "causal_forest", "meta_learners"),
  treatment_type = c("auto", "binary", "continuous"),
  ps_model = c("logistic", "rf", "xgboost"),
  ps_methods = c("matching", "ipw", "doubly_robust"),
  matching_ratio = 1,
  caliper = 0.2,
  dml_model = c("rf", "xgboost", "lasso"),
  dml_n_folds = 5,
  cf_num_trees = 2000,
  cf_honesty = TRUE,
  meta_learners = c("S", "T", "X"),
  meta_base_model = c("rf", "xgboost"),
  run_tmle = FALSE,
  use_python = FALSE,
  python_methods = c("econml_dml", "econml_forest"),
  exclude_cols = NULL,
  impute = TRUE,
  impute_method = c("median", "knn", "mean"),
  normalize = TRUE,
  normalize_method = c("zscore", "minmax"),
  run_eda = TRUE,
  confidence_level = 0.95,
  seed = NULL,
  verbose = TRUE
)

Arguments

data

A data frame with the data.

treatment

Character string with the name of the treatment variable.

outcome

Character string with the name of the outcome variable.

covariates

Character vector with the names of the covariates. If NULL (default), all remaining columns are used.

methods

Character vector with the methods to run: any of "propensity", "dml", "causal_forest" and "meta_learners" (default: all four).

treatment_type

Treatment type: "auto" (default, detected from the data), "binary" or "continuous".

ps_model

Model used to estimate the propensity score: "logistic" (default), "rf" or "xgboost".

ps_methods

Propensity score methods to use: any of "matching", "ipw" and "doubly_robust" (default: all three).

matching_ratio

Number of controls matched to each treated unit (default: 1).

caliper

Matching caliper in standard deviations of the propensity score (default: 0.2).

dml_model

Base learner for Double Machine Learning: "rf" (default), "xgboost" or "lasso".

dml_n_folds

Number of cross-fitting folds in DML (default: 5).

cf_num_trees

Number of trees of the causal forest (default: 2000).

cf_honesty

Logical; use honest splitting in the causal forest (default: TRUE).

meta_learners

Meta-learners to run: any of "S", "T" and "X" (default: all three).

meta_base_model

Base model of the meta-learners: "rf" (default) or "xgboost".

run_tmle

Logical; run TMLE (default: FALSE).

use_python

Logical; run Python methods through 'reticulate' (default: FALSE).

python_methods

Python methods to run: any of "econml_dml" and "econml_forest".

exclude_cols

Character vector of columns to exclude from the analysis (default: NULL).

impute

Logical; impute missing values (default: TRUE).

impute_method

Imputation method: "median" (default), "knn" or "mean".

normalize

Logical; normalize the covariates (default: TRUE).

normalize_method

Normalization method: "zscore" (default) or "minmax".

run_eda

Logical; run the causal exploratory analysis (default: TRUE).

confidence_level

Confidence level of the intervals (default: 0.95).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical; print progress to the console (default: TRUE).

Details

Propensity score methods and meta-learners are only run for binary treatments. Several methods rely on suggested packages that are used only when installed: 'MatchIt' (matching), 'cobalt' (love plot), 'grf' (causal forest), 'DoubleML', 'mlr3' and 'mlr3learners' (DML), 'tmle' and 'SuperLearner' (TMLE) and 'reticulate' (Python methods). When one of them is missing, the corresponding method is skipped with a warning.

Value

An object of class causalml (a list) with the components:

call: The matched call of the internal pipeline.
params: List of analysis parameters (treatment, outcome, covariates, methods, treatment_type, confidence_level, seed).
eda: Causal exploratory analysis: distributions, pre-adjustment balance and potential confounders (only if run_eda = TRUE).
preprocessed: List with the preprocessed data and the final covariates.
assumptions: Assumption checks: overlap, positivity and balance after weighting.
propensity: Propensity score results (scores, matching, IPW and AIPW estimates with their confidence intervals).
advanced: DML, causal forest (with CATE and variable importance) and TMLE results, when requested.
meta_learners: S-, T- and X-learner results with individual CATE estimates and the ATE.
python: EconML results (only if use_python = TRUE).
summary_table: Data frame comparing the methods, with columns method, ate (average treatment effect), se, ci_lower, ci_upper and p_value.
evalue: List with the E-value of the sensitivity analysis (evalue_point, evalue_ci).
figures: Named list of 'ggplot2' plots.
figures_catalog: Data frame cataloguing the figures.
elapsed_time: Execution time in seconds.
data_original: The input data.
verbose_text, verbose_lines: Captured console output (only if verbose = TRUE).

Examples

# Simulated confounded data: x1 drives both treatment and outcome
set.seed(1)
n <- 300
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$treat <- rbinom(n, 1, plogis(df$x1))
df$y <- 1.5 * df$treat + df$x1 + 0.5 * df$x2 + rnorm(n)

res <- causal_ml(df, treatment = "treat", outcome = "y",
                 methods = c("propensity", "meta_learners"),
                 ps_methods = c("ipw", "doubly_robust"),
                 meta_learners = "T", run_eda = FALSE,
                 seed = 1, verbose = FALSE)
res$summary_table

Cluster Profiles

Description

Computes the mean of every numeric variable within each cluster found by unsupervised_ml(), together with the overall (global) mean. Noise points (label 0, as produced by DBSCAN) are excluded.

Usage

cluster_profile(result, algorithm = NULL, original_data = NULL, verbose = TRUE)

Arguments

result

An object of class unsupervisedml, as returned by unsupervised_ml().

algorithm

Name of the clustering algorithm whose labels are used (e.g. "kmeans"). If NULL (default), the best algorithm of the evaluation (or the first one available) is used.

original_data

Optional data frame with the same rows as the data used in the analysis, used to profile variables on their original scale or variables not used in the clustering. If NULL (default), the preprocessed data before normalization are used.

verbose

Logical. If TRUE (default), the cluster sizes and means are printed to the console.

Value

Invisibly, a data frame with one row per cluster plus a final row "Global": the column Cluster holds the cluster label and the remaining columns hold the mean of each numeric variable.

Examples

set.seed(1)
X <- data.frame(a = c(rnorm(40), rnorm(40, 3)),
                b = c(rnorm(40), rnorm(40, 3)),
                c = rnorm(80))
res <- unsupervised_ml(X, methods = "clustering",
                       clustering_algorithms = c("kmeans", "hierarchical"),
                       k = 2, run_eda = FALSE, seed = 1, verbose = FALSE)
prof <- cluster_profile(res, algorithm = "kmeans", verbose = FALSE)
prof

Compare Cross-Validated Models

Description

Summarizes the cross-validated metrics of all models, adds the mean rank across metrics (rank_mean) and selects the best model according to select_metric. With verbose = TRUE it also reports the stability of the best model across folds.

Usage

compare_models(cv_results, task, select_metric = NULL, verbose = TRUE)

Arguments

cv_results

Named list of resampling results, as returned by fit_models_cv().

task

Type of task: "classification" or "regression".

select_metric

Metric used to select the best model. If NULL (default) ROC-AUC is used for classification and R-squared for regression. "rank_mean" selects the model with the best mean rank.

verbose

Logical. Print the progress and an explanation of each step in the console (default: TRUE).

Value

A list with summary (data frame with one row per model, ordered from best to worst, with column model, one column per metric and rank_mean), all_metrics (long data frame from tune::collect_metrics() with a model column), best_model, best_metric (value of the selection metric for the best model) and select_metric.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
pp <- preprocess_data(df, target = "y", task = "classification",
                      verbose = FALSE)
folds <- setup_cv(pp$train_data, target = "y", task = "classification",
                  cv_folds = 3, verbose = FALSE)
specs <- define_models(c("glm", "rf"), task = "classification",
                       verbose = FALSE)
cv_res <- fit_models_cv(specs, pp$recipe, folds, task = "classification",
                        verbose = FALSE)
cmp <- compare_models(cv_res, task = "classification", select_metric = "mcc")
cmp$best_model

Fairness Correction with Group-Specific Thresholds

Description

Post-processing correction in the spirit of equality of opportunity (Hardt, Price and Srebro, 2016). For each group of the protected variable it searches (over a grid from 0.05 to 0.95 in steps of 0.01) the classification threshold whose true positive rate (TPR) is closest to a common target TPR, defined as the mean of the TPRs obtained at each group's Youden threshold. The predicted classes are then recomputed with the group-specific thresholds.

Usage

correct_fairness_threshold(predictions, target, protected_var, verbose = TRUE)

Arguments

predictions

Data frame with one row per case containing the observed outcome (target, a factor with 2 levels), the protected variable and the probability of the positive class (.pred_<positive level>).

target

Name of the outcome variable.

protected_var

Name of the protected variable.

verbose

Logical (default TRUE). Print the thresholds and the TPR before and after the correction.

Value

A list with:

method: Character, "postprocesamiento" (post-processing).
group_thresholds: Data frame with columns group, threshold (group-specific threshold), tpr_before (TPR at the 0.5 threshold) and tpr_after (TPR at the new threshold).
target_tpr: Common target TPR.
corrected_predictions: The predictions data frame with .pred_class recomputed using the group-specific thresholds.

References

Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29.

Examples

set.seed(1)
n <- 300
sex <- sample(c("female", "male"), n, replace = TRUE)
prob <- plogis(rnorm(n) + ifelse(sex == "male", 0.5, 0))
preds <- data.frame(
  outcome = factor(ifelse(runif(n) < prob, "yes", "no"), levels = c("no", "yes")),
  .pred_yes = prob,
  sex = sex
)

corr <- correct_fairness_threshold(preds, target = "outcome",
                                   protected_var = "sex")
corr$group_thresholds
table(corr$corrected_predictions$.pred_class, preds$sex)

Feature Selection with Cross-Validation

Description

Performs feature selection inside a cross-validation loop to reduce overfitting. In each fold a random forest (ranger, 100 trees) is fitted on complete cases and the most important predictors (the top half, but at least min_features) are retained; predictors selected in at least half of the folds are returned.

Usage

cv_feature_selection(
  data,
  target,
  task,
  cv_folds = 5,
  min_features = 5,
  seed = NULL,
  verbose = TRUE
)

Arguments

data

Training data (data frame).

target

Name of the target variable.

task

Type of task: "classification" (stratified folds) or "regression".

cv_folds

Number of folds (default: 5).

min_features

Minimum number of features retained in each fold (default: 5).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print progress (default: TRUE).

Value

A list with selected_features (character vector), selection_counts (named numeric vector with the number of folds in which each predictor was selected, sorted), threshold (minimum number of folds required) and n_folds.

Examples

set.seed(1)
n <- 150
df <- as.data.frame(matrix(rnorm(n * 6), n, 6))
df$y <- df$V1 + df$V2 + rnorm(n)
fs <- cv_feature_selection(df, target = "y", task = "regression",
                           cv_folds = 3, min_features = 2, seed = 1,
                           verbose = FALSE)
fs$selected_features

Define Model Specifications

Description

Creates the parsnip model specifications of the requested models for the given task, with default hyperparameters.

Usage

define_models(models, task, verbose = TRUE)

Arguments

models

Character vector with the models. Available: "rf" (random forest), "xgboost", "svm" (RBF kernel), "nnet" (neural network), "glm" (logistic or linear regression), "glmnet" (regularized regression), "tree" (decision tree) and "nb" (naive Bayes). Some models need suggested packages (e.g. kernlab, glmnet, rpart).

task

Type of task: "classification" or "regression".

verbose

Logical. Print the progress and an explanation of each step in the console (default: TRUE).

Value

A named list of parsnip model specifications (model_spec objects), one per model.

Examples

specs <- define_models(c("glm", "rf"), task = "classification")
names(specs)

Detect Possible Data Leakage (Post-Model Checks)

Description

Inspects the results of a trained model for indicators of data leakage that can only be checked after training. Pre-model checks (correlation with the target, possible identifiers) are done by eda_leakage().

Checks performed:

test AUC or R-squared > 0.99 (suspiciously high performance);
one variable accounting for more than 80% of the total importance (dominant variable).

Usage

detect_data_leakage(model_results = NULL, importance = NULL, verbose = TRUE)

Arguments

model_results

A list with a test_metrics element (data frame with columns .metric and .estimate), such as a supervised_ml() result. Optional (default: NULL).

importance

Data frame of variable importance with columns Variable and Importance, sorted by decreasing importance (e.g. the importance element of a supervised_ml() result). Optional (default: NULL).

verbose

Logical. Print results (default: TRUE).

Value

A list with warnings (list with elements high_performance and/or dominant_variable describing each indicator), n_warnings (number of indicators) and leakage_suspected (logical).

Examples


# Inputs built by hand
metrics <- data.frame(.metric = c("roc_auc", "accuracy"),
                      .estimate = c(0.995, 0.97))
imp <- data.frame(Variable = c("a", "b", "c"), Importance = c(90, 6, 4))
detect_data_leakage(list(test_metrics = metrics), imp,
                    verbose = FALSE)$n_warnings
# From a supervised_ml() result
set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
lk <- detect_data_leakage(res, importance = res$importance, verbose = FALSE)
lk$leakage_suspected

Diversity-Based Train/Test Split for True Generalization

Description

Creates train/test splits where the test set is genuinely DIFFERENT from the training set, not just a random subset of the same distribution. This enables a fairer evaluation of model generalization.

Usage

diversity_split(
  data,
  test_prop = 0.2,
  method = "hybrid",
  n_clusters = NULL,
  exclude_cols = NULL,
  target = NULL,
  stratify = TRUE,
  diversity_strength = 0.5,
  seed = NULL,
  verbose = TRUE
)

Arguments

data

Data frame to split.

test_prop

Proportion for test set (default 0.20).

method

Method for creating diverse split:

"cluster": Assigns entire clusters to test (most different)
"dissimilarity": Selects most dissimilar observations for test
"boundary": Selects observations at distribution boundaries
"hybrid": Combines clustering with boundary selection

n_clusters

Number of clusters for the cluster-based methods. If NULL (default) it is set to round(sqrt(n / 2)), bounded between 3 and 10.

exclude_cols

Character vector of columns to exclude from the distance calculations (e.g., ID columns).

target

Name of the target variable. It is excluded from the distance calculations.

stratify

Logical (default TRUE). Reserved for keeping class proportions of target; it is passed to the cluster-based methods but class proportions are not currently enforced.

diversity_strength

Number between 0 and 1 (default 0.5) controlling how aggressively diversity is maximized. Higher values give a more different test set, but may be too extreme.

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical (default TRUE). If TRUE, prints a summary of the split.

Details

The key insight is that random sampling from a homogeneous distribution creates train and test sets that are essentially "twins" - they share the same patterns, making test performance an optimistic estimate of true generalization.

This function creates genuinely different partitions by:

Cluster method: Groups similar observations, then assigns entire clusters to test. The test set contains "types" of observations not well-represented in training.
Dissimilarity method: Selects observations that are most different from the data centroid, ensuring test covers edge cases.
Boundary method: Selects observations at the boundaries of the multivariate distribution (extreme combinations of variables).
Hybrid method: Combines clustering with dissimilarity selection within clusters.

Value

A list of class c("diversity_split", "list") containing:

train: Training data frame.
test: Test data frame.
train_idx: Sorted row indices of the training observations.
test_idx: Sorted row indices of the test observations.
diversity_metrics: List with mean_distance_ratio (mean distance of test cases to the training centroid divided by the mean within-training distance), ks_statistic (average two-sample Kolmogorov-Smirnov statistic across variables), centroid_distance (Euclidean distance between train and test centroids), random_baseline (centroid distance of one random split of the same size) and diversity_ratio (centroid_distance divided by random_baseline). Higher values mean more different sets.
cluster_info: Data frame with cluster sizes and test membership (methods "cluster" and "hybrid"), or NULL.
settings: List with n_total, n_train, n_test, method, n_clusters, diversity_strength and seed.

If fewer than two usable numeric columns are available, a warning is issued and a plain list with train, test, train_idx and test_idx from a random split is returned.

Examples

set.seed(1)
survey_data <- data.frame(
  x1 = rnorm(120), x2 = rnorm(120), x3 = rnorm(120),
  depression = factor(sample(c("yes", "no"), 120, replace = TRUE))
)

# Default (hybrid) method
result <- diversity_split(survey_data, target = "depression",
                          test_prop = 0.20, seed = 1)

# Cluster-based split (entire clusters go to test)
result <- diversity_split(
  data = survey_data,
  target = "depression",
  method = "cluster",
  n_clusters = 5,
  test_prop = 0.20,
  seed = 1,
  verbose = FALSE
)

# Check how different train and test are
result$diversity_metrics

Basic Exploratory Data Analysis

Description

Runs a basic exploratory analysis that does not require a target variable. Useful to explore a dataset before defining the model.

Usage

eda_basic(data, verbose = TRUE)

Arguments

data

Data frame with the data.

verbose

Logical. Print progress and results (default: TRUE).

Value

An object of class c("supervisedml_eda", "list") with elements structure (eda_structure()), missing (eda_missing()), numeric (eda_numeric_basic()), categorical (eda_categorical_basic()), outliers (eda_outliers()) and correlation (eda_correlation_basic()).

Examples

res <- eda_basic(iris, verbose = FALSE)
res$numeric

Categorical Variable Analysis

Description

Lists the categorical (factor or character) predictors and their number of levels.

Usage

eda_categorical(data, target, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the target variable (excluded from the analysis).

verbose

Logical. Print results (default: TRUE).

Value

A list with variables (names of the categorical predictors) and n_levels (number of distinct values of each one).

Examples

df <- data.frame(g = c("a", "b", "a"), h = factor(c("x", "y", "z")), y = 1:3)
eda_categorical(df, target = "y", verbose = FALSE)

Basic Categorical Variable Analysis

Description

Lists all categorical (factor or character) variables and their number of levels.

Usage

eda_categorical_basic(data, verbose = TRUE)

Arguments

data

Data frame.

verbose

Logical. Print results (default: TRUE).

Value

A list with variables (names of the categorical variables) and n_levels (number of distinct values of each one).

Examples

eda_categorical_basic(iris, verbose = FALSE)

Correlation Matrix

Description

Pearson correlation matrix of the numeric variables (pairwise complete observations); with verbose = TRUE it reports the strongest correlations with the target and pairs with |r| > 0.8.

Usage

eda_correlation(data, target, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the target variable.

verbose

Logical. Print results (default: TRUE).

Value

A numeric correlation matrix, or NULL if there are fewer than two numeric variables.

Examples

round(eda_correlation(mtcars[, 1:4], target = "mpg", verbose = FALSE), 2)

Basic Correlation Matrix

Description

Pearson correlation matrix of all numeric variables (pairwise complete observations); with verbose = TRUE it reports pairs with |r| > 0.7.

Usage

eda_correlation_basic(data, verbose = TRUE)

Arguments

data

Data frame.

verbose

Logical. Print results (default: TRUE).

Value

A numeric correlation matrix, or NULL if there are fewer than two numeric variables.

Examples

round(eda_correlation_basic(iris, verbose = FALSE), 2)

Basic Data Leakage Check

Description

Detects possible indicators of data leakage BEFORE training the model: numeric predictors with a very high correlation with the target (|r| > 0.95) and possible identifier variables (one unique value per row).

Usage

eda_leakage(data, target, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the target variable (factors are converted to their integer codes for the correlation).

verbose

Logical. Print results (default: TRUE).

Value

A list with warnings (list with elements high_correlation and/or possible_ids, each with the flagged variables), n_warnings (number of indicators found) and has_leakage (logical).

Examples

set.seed(1)
df <- data.frame(id = 1:50, x = rnorm(50))
df$y <- df$x + rnorm(50)
df$leak <- df$y + rnorm(50, sd = 0.01)
eda_leakage(df, target = "y", verbose = FALSE)$n_warnings

Missing Value Analysis

Description

Counts missing values by variable and in the whole dataset.

Usage

eda_missing(data, verbose = TRUE)

Arguments

data

Data frame.

verbose

Logical. Print results (default: TRUE).

Value

A list with by_variable (data frame with columns variable, n_missing and pct_missing, sorted by decreasing missingness), total (total number of missing cells) and total_pct (percentage of missing cells).

Examples

eda_missing(airquality, verbose = FALSE)$by_variable

Normality Test of the Target

Description

Shapiro-Wilk test of a numeric target (a random sample of 5000 observations is used when there are more). This is a diagnostic: most machine learning models do not assume normality.

Usage

eda_normality(data, target, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the numeric target variable.

verbose

Logical. Print results (default: TRUE).

Value

An object of class htest (see stats::shapiro.test()), or invisibly NULL if the target is not numeric.

Examples

eda_normality(mtcars, "mpg", verbose = FALSE)

Numeric Variable Analysis

Description

Descriptive statistics of the numeric predictors.

Usage

eda_numeric(data, target, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the target variable (excluded from the analysis).

verbose

Logical. Print results (default: TRUE).

Value

A list with variables (names of the numeric predictors) and stats (data frame with columns variable, mean, sd, min and max). If there are no numeric predictors only variables (empty) is returned.

Examples

eda_numeric(mtcars, target = "mpg", verbose = FALSE)$stats

Basic Numeric Variable Analysis

Description

Descriptive statistics of all numeric variables.

Usage

eda_numeric_basic(data, verbose = TRUE)

Arguments

data

Data frame.

verbose

Logical. Print results (default: TRUE).

Value

A data frame with columns variable, n, missing, mean, sd, min, median and max (rounded to 2 decimals); an empty data frame if there are no numeric variables.

Examples

eda_numeric_basic(iris, verbose = FALSE)

Outlier Detection

Description

Counts univariate outliers in every numeric variable using a robust z-score based on the median absolute deviation (MAD) or the interquartile range (IQR) rule.

Usage

eda_outliers(data, verbose = TRUE, method = c("mad", "iqr"), threshold = NULL)

Arguments

data

Data frame.

verbose

Logical. Print results (default: TRUE).

method

Detection method: "mad" (default, robust) or "iqr" (classical).

threshold

Detection threshold. For MAD, the robust z-score cutoff (used when NULL: 3.29). For IQR, the multiplier of the interquartile range (used when NULL: 3).

Value

A data frame with columns variable, n_outliers and pct (percentage of non-missing values flagged), sorted by decreasing number of outliers.

Examples

eda_outliers(mtcars, verbose = FALSE)
eda_outliers(mtcars, method = "iqr", threshold = 1.5, verbose = FALSE)

Data Structure

Description

Counts observations and variables and classifies variables as numeric or categorical.

Usage

eda_structure(data, verbose = TRUE)

Arguments

data

Data frame.

verbose

Logical. Print results (default: TRUE).

Value

A list with n_obs, n_vars, n_numeric, n_categorical and var_types (named character vector with the type of each variable).

Examples

eda_structure(iris, verbose = FALSE)

Exploratory Data Analysis

Description

Runs a complete exploratory analysis of the data: target distribution, missing values, categorical and numeric variables, outliers, correlations, multicollinearity (VIF) and a basic data leakage check. If target is NULL, a basic EDA without target is run (see eda_basic()).

Usage

eda_summary(data, target = NULL, task = "auto", verbose = TRUE)

Arguments

data

Data frame with the data.

target

Name of the target variable (optional; if NULL a basic EDA is performed).

task

Type of task: "classification", "regression" or "auto" (detected from the target).

verbose

Logical. Print progress and results (default: TRUE).

Value

An object of class c("supervisedml_eda", "list"). With a target it contains structure (see eda_structure()), target_dist, normality (regression only), missing, categorical, numeric, outliers, correlation, vif, leakage_check, task and target. Without a target, the elements returned by eda_basic().

Examples

res <- eda_summary(iris, target = "Species", verbose = FALSE)
names(res)
res_reg <- eda_summary(mtcars, target = "mpg", verbose = FALSE)
res_reg$vif

Target Distribution for Classification

Description

Frequencies and proportions of the classes of a categorical target, with the imbalance ratio.

Usage

eda_target_classification(data, target, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the target variable (coerced to factor).

verbose

Logical. Print results (default: TRUE).

Value

A list with frequencies (table), proportions (table, in percent), n_classes and imbalance_ratio (largest over smallest class frequency).

Examples

eda_target_classification(iris, "Species", verbose = FALSE)

Target Distribution for Regression

Description

Descriptive statistics of a numeric target.

Usage

eda_target_regression(data, target, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the numeric target variable.

verbose

Logical. Print results (default: TRUE).

Value

A list with mean, sd, median, min, max and skewness (a simple skewness index: (mean - median) / sd).

Examples

eda_target_regression(mtcars, "mpg", verbose = FALSE)

Variance Inflation Factor Analysis

Description

Computes the variance inflation factor (VIF) of each numeric predictor by regressing it on the remaining numeric predictors.

Usage

eda_vif(data, target, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the target variable (excluded from the analysis).

verbose

Logical. Print results (default: TRUE).

Value

A data frame with columns variable and VIF, sorted by decreasing VIF, or NULL if there are fewer than two numeric predictors, too few complete observations, or the computation fails.

Examples

eda_vif(mtcars, target = "mpg", verbose = FALSE)

Estimate N for a Target Metric Using the Curve Fit

Description

Uses the fitted power-law model to estimate the sample size required to achieve a specified target metric value.

Usage

estimate_n(object, target_metric, model = NULL, warn_extrapolation = TRUE)

Arguments

object

An object of class ml_sample_size returned by ml_sample_size().

target_metric

Target metric value.

model

For multi-model results, the model to use. If NULL (default), the first model is used.

warn_extrapolation

Logical (default TRUE). Warn if the fit is poor or the estimate extrapolates beyond the observed sample sizes.

Details

This function inverts the power-law equation to find the required n. Warnings are issued when:

The target exceeds the estimated asymptote.
The required n exceeds 2 times the largest observed sample size.
The curve fit quality is poor (pseudo-R2 < 0.80).

Value

A single number: the estimated sample size (rounded up to an integer value), or Inf if the target is beyond the estimated asymptote.

Examples

res <- ml_sample_size(model = "glm", n_grid = c(50, 100, 200, 400), reps = 5,
                      n_test = 500, sim_params = list(N_pop = 2000, p = 5),
                      seed = 1, verbose = FALSE)
a <- coef(res$curve_fit)[["a"]]
estimate_n(res, target_metric = a - 0.02)

Estimate N to Reach the Plateau (Near Maximum Performance)

Description

Computes the sample size at which performance approaches the asymptote within a specified distance (delta).

Usage

estimate_n_plateau(
  object,
  delta = 0.01,
  relative = FALSE,
  model = NULL,
  warn_extrapolation = TRUE
)

Arguments

object

An object of class ml_sample_size returned by ml_sample_size().

delta

Distance to the asymptote that defines the plateau (default 0.01).

relative

Logical (default FALSE). If TRUE, delta is interpreted as a proportion of the target metric (e.g., delta = 0.01 means 1% of the target). Requires a result computed with a target.

model

For multi-model results, the model to use. If NULL (default), the first model is used.

warn_extrapolation

Logical (default TRUE). Warn if the fit is poor or the estimate extrapolates beyond the observed sample sizes.

Details

The plateau is defined operationally as n_plateau = (b / delta)^(1 / c).

When relative = TRUE and target = 0.80, delta = 0.01 becomes 0.008 (1% of 0.80), yielding a more stringent plateau definition.

Value

A single number: the estimated sample size for the plateau (rounded up to an integer value).

Examples

result <- ml_sample_size(model = "glm", n_grid = c(50, 100, 200, 400),
                         reps = 5, target = 0.75, n_test = 500,
                         sim_params = list(N_pop = 2000, p = 5),
                         seed = 1, verbose = FALSE)

# Absolute delta (default)
estimate_n_plateau(result, delta = 0.01, warn_extrapolation = FALSE)

# Relative delta (1% of target)
estimate_n_plateau(result, delta = 0.01, relative = TRUE,
                   warn_extrapolation = FALSE)

Calibration Analysis by Probability Bins

Description

Compares the mean predicted probability of the positive class with the observed proportion of positives within ten probability bins of width 0.1. Binary classification only.

Usage

eval_calibration(predictions, target, verbose = TRUE)

Arguments

predictions

A data frame of predictions with the observed outcome column and, for classification, the .pred_class column and one ⁠.pred_<level>⁠ probability column per class (as returned by eval_predictions()).

target

Character string with the name of the outcome variable.

verbose

Logical. Print the results and their interpretation in the console (default: TRUE).

Value

A tibble with columns prob_bin (probability bin), n (number of cases), mean_pred (mean predicted probability) and mean_obs (observed proportion of positives); NULL for multiclass outcomes.

Examples

set.seed(1)
y <- factor(sample(c("no", "yes"), 100, replace = TRUE))
p <- stats::plogis(ifelse(y == "yes", 1, -1) + rnorm(100))
preds <- data.frame(y = y,
                    .pred_class = factor(ifelse(p >= 0.5, "yes", "no"),
                                         levels = levels(y)),
                    .pred_no = 1 - p, .pred_yes = p)
eval_calibration(preds, target = "y", verbose = FALSE)

Compute the Confusion Matrix

Description

Cross-tabulates predicted and observed classes and, when verbose = TRUE, explains the true/false positives and negatives.

Usage

eval_confusion(predictions, target, verbose = TRUE)

Arguments

predictions

target

Character string with the name of the outcome variable.

verbose

Logical. Print the results and their interpretation in the console (default: TRUE).

Value

An object of class conf_mat (see yardstick::conf_mat()), whose table element holds the counts (predictions in rows, truth in columns).

Examples

set.seed(1)
y <- factor(sample(c("no", "yes"), 100, replace = TRUE))
p <- stats::plogis(ifelse(y == "yes", 1, -1) + rnorm(100))
preds <- data.frame(y = y,
                    .pred_class = factor(ifelse(p >= 0.5, "yes", "no"),
                                         levels = levels(y)),
                    .pred_no = 1 - p, .pred_yes = p)
eval_confusion(preds, target = "y")

Compute Performance Metrics

Description

Computes performance metrics from a data frame of predictions. For binary classification the positive class is detected from the level names (e.g. "yes", "1", "positive"; otherwise the second level). Multiclass metrics use macro averaging and the Hand-Till ROC-AUC.

Usage

eval_metrics(predictions, target, task, verbose = TRUE)

Arguments

predictions

target

Character string with the name of the outcome variable.

task

Type of task: "classification" or "regression".

verbose

Logical. Print the results and their interpretation in the console (default: TRUE).

Value

A tibble with columns .metric, .estimator and .estimate. Classification: roc_auc, accuracy, sensitivity, specificity, f_meas, mcc, kap and f2_meas. Regression: rmse, rsq and mae.

Examples

set.seed(1)
y <- factor(sample(c("no", "yes"), 100, replace = TRUE))
p <- stats::plogis(ifelse(y == "yes", 1, -1) + rnorm(100))
preds <- data.frame(y = y,
                    .pred_class = factor(ifelse(p >= 0.5, "yes", "no"),
                                         levels = levels(y)),
                    .pred_no = 1 - p, .pred_yes = p)
eval_metrics(preds, target = "y", task = "classification")

# Regression
yr <- rnorm(100, mean = 10)
preds_r <- data.frame(yr = yr, .pred = yr + rnorm(100, sd = 0.5))
eval_metrics(preds_r, target = "yr", task = "regression", verbose = FALSE)

Compare Predicted and Observed Values

Description

Computes the correlation between observed and predicted values and the mean bias of the predictions (regression).

Usage

eval_pred_vs_obs(predictions, target, verbose = TRUE)

Arguments

predictions

A data frame of regression predictions with the observed outcome column and the .pred column (as returned by eval_predictions()).

target

Character string with the name of the outcome variable.

verbose

Logical. Print the results and their interpretation in the console (default: TRUE).

Value

A list with observed and predicted (numeric vectors), correlation (Pearson correlation) and bias (mean of predicted minus observed).

Examples

set.seed(1)
y <- rnorm(100, mean = 10)
preds <- data.frame(y = y, .pred = y + rnorm(100, sd = 0.5))
pvo <- eval_pred_vs_obs(preds, target = "y")
pvo$correlation

Generate Predictions on a Data Set

Description

Predicts the outcome of a data set with a fitted workflow and binds the predictions (and class probabilities for classification) to the data.

Usage

eval_predictions(final_fit, test_data, target, task, verbose = TRUE)

Arguments

final_fit

A fitted tidymodels workflow, e.g. the final_fit element of a supervised_ml() result.

test_data

Data frame with the observations to predict.

target

Character string with the name of the outcome variable.

task

Type of task: "classification" or "regression".

verbose

Logical. Print the results and their interpretation in the console (default: TRUE).

Value

A tibble with the columns of test_data plus .pred_class and one ⁠.pred_<level>⁠ probability column per class (classification), or .pred (regression).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
preds <- eval_predictions(res$final_fit, res$test_data, target = "y",
                          task = "classification")
head(preds)

Residual Analysis

Description

Computes the residuals (observed minus predicted), their descriptive statistics and a Shapiro-Wilk normality test (on a random subsample of 5000 residuals when there are more).

Usage

eval_residuals(predictions, target, verbose = TRUE)

Arguments

predictions

A data frame of regression predictions with the observed outcome column and the .pred column (as returned by eval_predictions()).

target

Character string with the name of the outcome variable.

verbose

Logical. Print the results and their interpretation in the console (default: TRUE).

Value

A list with residuals (numeric vector), stats (list with mean, sd, min, max and median of the residuals) and normality_test (an htest object from stats::shapiro.test()).

Examples

set.seed(1)
y <- rnorm(100, mean = 10)
preds <- data.frame(y = y, .pred = y + rnorm(100, sd = 0.5))
rs <- eval_residuals(preds, target = "y")
rs$stats

Compute the ROC Curve

Description

Computes the ROC curve and the area under the curve from classification predictions (one-vs-all curves and Hand-Till AUC for multiclass outcomes).

Usage

eval_roc(predictions, target, verbose = TRUE)

Arguments

predictions

target

Character string with the name of the outcome variable.

verbose

Logical. Print the results and their interpretation in the console (default: TRUE).

Value

A list with curve (tibble from yardstick::roc_curve() with .threshold, specificity, sensitivity and, for multiclass, .level), auc (numeric area under the curve) and event_info (list describing the positive class and event level).

Examples

set.seed(1)
y <- factor(sample(c("no", "yes"), 100, replace = TRUE))
p <- stats::plogis(ifelse(y == "yes", 1, -1) + rnorm(100))
preds <- data.frame(y = y,
                    .pred_class = factor(ifelse(p >= 0.5, "yes", "no"),
                                         levels = levels(y)),
                    .pred_no = 1 - p, .pred_yes = p)
roc <- eval_roc(preds, target = "y")
roc$auc

Evaluate the Final Model on the Test Set

Description

Fits the final workflow on the whole training set and evaluates it on the test set: predictions, performance metrics, overfitting diagnosis (cross-validation vs test, out-of-bag vs test for random forests, train vs test for other models) and, depending on the task, ROC curve, confusion matrix and calibration (classification) or predicted vs observed values and residual analysis (regression).

Usage

evaluate_model(
  tuning_result,
  train_data,
  test_data,
  target,
  task,
  modeling_result = NULL,
  best_model = NULL,
  verbose = TRUE
)

Arguments

tuning_result

A list with a final_workflow element (an unfitted tidymodels workflow), such as the result of tune_best_model() or the tuning element of a supervised_ml() result.

train_data

Data frame with the (preprocessed) training set.

test_data

Data frame with the (preprocessed) test set.

target

Character string with the name of the outcome variable.

task

Type of task: "classification" or "regression".

modeling_result

Optional result of train_models(); its cross-validation summary is used in the overfitting diagnosis (default: NULL).

best_model

Optional name of the best model, e.g. "rf" or "xgboost" (default: NULL).

verbose

Logical. Print the results and their interpretation in the console (default: TRUE).

Value

An object of class supervisedml_evaluation (a list) with: final_fit (fitted workflow), predictions (test predictions, see eval_predictions()), metrics (test metrics, see eval_metrics()), train_metrics (training metrics, NULL for random forests), overfitting (list with the overfitting diagnosis: cv_auc, test_auc, cv_gap, train vs test comparison, and oob_auc, oob_gap for random forests), task and target. Classification adds roc_data (eval_roc()), confusion (eval_confusion()) and, for binary outcomes, calibration (eval_calibration()). Regression adds pred_obs (eval_pred_vs_obs()) and residuals (eval_residuals()).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
ev <- evaluate_model(res$tuning, res$train_data, res$test_data,
                     target = "y", task = "classification",
                     verbose = FALSE)
ev$metrics

Interpret Results of supervised_ml() with a Large Language Model

Description

Sends a structured summary of a supervised_ml() result to a large language model (Groq or OpenAI chat completions API) and returns a plain-language interpretation of the analysis. A built-in diagnostic engine first checks for common problems (possible data leakage, overfitting, class imbalance, untuned models, etc.), and its findings are included in the request. An API key of the chosen provider is required; no key is bundled with the package.

Usage

explain_with_ai(
  result,
  api_key = NULL,
  language = c("es", "en"),
  model = NULL,
  provider = c("groq", "openai"),
  max_tokens = 2000L,
  verbose = TRUE
)

Arguments

result

An object of class supervisedml returned by supervised_ml().

api_key

API key of the provider. If NULL (default), the environment variable PSYML_API_KEY is used; an error is raised if it is not set.

language

Language of the interpretation: "es" (Spanish, default) or "en" (English).

model

Model identifier. If NULL (default), "llama-3.3-70b-versatile" is used for Groq and "gpt-4.1-mini" for OpenAI.

provider

API provider: "groq" (default) or "openai".

max_tokens

Maximum number of tokens of the response (default 2000).

verbose

Logical. If TRUE (default), the diagnostics and the interpretation are printed to the console.

Value

Invisibly, a list with components success (logical), text (character, the interpretation or the error message), and diagnostics (list of findings of the diagnostic engine; each finding has elements id, issue, severity, details, and suggested_code).

Examples

## Not run: 
# Requires an API key and an internet connection
set.seed(1)
df <- data.frame(x1 = rnorm(150), x2 = rnorm(150))
df$y <- factor(ifelse(df$x1 + rnorm(150) > 0, "yes", "no"))
res <- supervised_ml(df, "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
Sys.setenv(PSYML_API_KEY = "your-groq-key")
explain_with_ai(res, language = "en")
explain_with_ai(res, provider = "openai", api_key = "your-openai-key")

## End(Not run)

Export the Results of an Analysis as a JSON File

Description

Writes the result of a PsyML analysis to a structured JSON file (metadata, metrics, model information, variable importance, etc.). When the analysis was run with verbose = TRUE, the JSON also includes the complete console output (verbose_text and verbose_lines). The file can be used by generate_report_from_json(), generate_report_with_ai() and launch_report_generator().

Usage

export_verbose_json(result, file_path)

Arguments

result

Result of supervised_ml() or supervised_ml_capture().

file_path

Path of the output file (required, no default).

Value

Invisibly returns file_path.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
out <- file.path(tempdir(), "psyml_report.json")
export_verbose_json(res, out)
names(jsonlite::fromJSON(out))
unlink(out)

Export the Console Output of an Analysis as a TXT File

Description

Writes the result of a PsyML analysis to a plain text file. When the analysis was run with verbose = TRUE, supervised_ml() stores the console output, so the file contains exactly the same output shown in the console. Otherwise a summary is built from the result object.

Usage

export_verbose_txt(result, file_path)

Arguments

result

Result of supervised_ml() or supervised_ml_capture().

file_path

Path of the output file (required, no default).

Value

Invisibly returns file_path.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
out <- file.path(tempdir(), "psyml_report.txt")
export_verbose_txt(res, out)
head(readLines(out))
unlink(out)

Fit Models with Cross-Validation

Description

Fits each model specification with the preprocessing recipe on every cross-validation fold and computes the performance metrics (classification: ROC-AUC, accuracy, sensitivity, specificity, F1, balanced accuracy, PR-AUC (binary only), MCC, kappa and F2; regression: RMSE, R-squared and MAE).

Usage

fit_models_cv(
  model_specs,
  recipe,
  cv_folds,
  task,
  select_metric = NULL,
  verbose = TRUE
)

Arguments

model_specs

Named list of model specifications, as returned by define_models().

recipe

A recipe object (e.g. the recipe element of preprocess_data()).

cv_folds

An rset of folds, as returned by setup_cv().

task

Type of task: "classification" or "regression".

select_metric

Metric shown in the console for each model. If NULL (default) ROC-AUC is shown for classification and R-squared for regression.

verbose

Logical. Print the progress and an explanation of each step in the console (default: TRUE).

Value

A named list with one resampling result (resample_results object from tune::fit_resamples(), with saved predictions) per model.

Examples

set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
pp <- preprocess_data(df, target = "y", task = "classification",
                      verbose = FALSE)
folds <- setup_cv(pp$train_data, target = "y", task = "classification",
                  cv_folds = 3, verbose = FALSE)
specs <- define_models("glm", task = "classification", verbose = FALSE)
cv_res <- fit_models_cv(specs, pp$recipe, folds, task = "classification",
                        verbose = FALSE)
tune::collect_metrics(cv_res$glm)

Generate All Plots of a supervisedml Object

Description

Generates all relevant plots of a supervised_ml() result together with metadata for scientific reports. Figures are numbered (Figura 1, Figura 2, ...) and given descriptive names and file names (plot titles and catalog texts are in Spanish). Plots depend on the task and on the analyses run: variable importance, model comparison, ROC curve, confusion matrix, calibration, threshold optimization, predicted vs observed values, residuals, tuning, SHAP, fairness and decision curve analysis.

Usage

generate_all_plots(x, verbose = TRUE)

Arguments

x

An object of class supervisedml returned by supervised_ml().

verbose

Logical. Print the list of generated figures (default: TRUE).

Value

A list with plots (named list of ggplot objects), figures_catalog (named list with, for each plot, number, type, id, title, description and filename) and total_figures (number of figures).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
gp <- generate_all_plots(res, verbose = FALSE)
names(gp$plots)
gp$plots$roc_curve

Generate a Report from an Exported JSON File

Description

Builds an R Markdown report from a JSON file written by export_verbose_json() and renders it to Word, HTML or PDF. Requires the rmarkdown package and pandoc (and a LaTeX installation for PDF).

Usage

generate_report_from_json(
  json_path,
  output_path,
  format = c("docx", "html", "pdf"),
  template = c("scientific", "brief", "full")
)

Arguments

json_path

Path to the JSON file.

output_path

Path of the output file (required, no default).

format

Output format: "docx" (default), "html" or "pdf".

template

Report template: "scientific" (default), "brief" or "full".

Value

Invisibly returns output_path.

Examples


if (requireNamespace("rmarkdown", quietly = TRUE) &&
    rmarkdown::pandoc_available()) {
  set.seed(1)
  n <- 200
  df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
  df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
  res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                       tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                       verbose = FALSE)
  json_file <- file.path(tempdir(), "psyml_report.json")
  html_file <- file.path(tempdir(), "psyml_report.html")
  export_verbose_json(res, json_file)
  generate_report_from_json(json_file, html_file, format = "html")
  unlink(c(json_file, html_file))
}

Generate a Scientific Report with AI

Description

Generates a scientific report (Word document) with the OpenAI chat completions API directly from R, without opening the Shiny application. The report text is written by the language model from the JSON file and the tables of metrics and variable importance are inserted from the data. Requires an OpenAI API key, an internet connection and the packages httr2 and officer.

Usage

generate_report_with_ai(
  json_path,
  api_key = Sys.getenv("PSYML_API_KEY"),
  output_path = NULL,
  title = "Analisis de Machine Learning",
  author = NULL,
  language = c("es", "en"),
  model = "gpt-4.1-mini",
  custom_instructions = NULL
)

Arguments

json_path

Path to the JSON file exported with export_verbose_json().

api_key

OpenAI API key. Defaults to the environment variable PSYML_API_KEY; an error is raised if it is empty.

output_path

Path of the output .docx file (required; an error is raised if NULL, the default).

title

Report title (default "Analisis de Machine Learning").

author

Author name (optional, default NULL).

language

Language of the report: "es" (Spanish, default) or "en" (English).

model

OpenAI model to use (default "gpt-4.1-mini").

custom_instructions

Optional character string with additional instructions for the language model (e.g., journal style or sections to emphasize). They are appended to the request with high priority. NULL (default) adds nothing.

Value

Invisibly returns output_path.

Examples

## Not run: 
# Requires an OpenAI API key and an internet connection
set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml_capture(df, target = "y", models = "glm", cv_folds = 3,
                             tune_best = FALSE, run_shap = FALSE,
                             n_cores = 1)
json_file <- file.path(tempdir(), "my_analysis.json")
export_verbose_json(res, json_file)

Sys.setenv(PSYML_API_KEY = "your-openai-key")
generate_report_with_ai(
  json_path = json_file,
  output_path = file.path(tempdir(), "my_report.docx"),
  title = "Machine Learning Analysis",
  author = "Jane Doe",
  language = "en"
)

## End(Not run)

Get the Catalog of Figures

Description

Returns the catalog of the figures generated by supervised_ml(), with their numbers, titles, descriptions and suggested file names (texts in Spanish).

Usage

get_figures_catalog(x)

Arguments

x

An object of class supervisedml returned by supervised_ml().

Value

A data frame with one row per figure, ordered by number, and columns key (name of the plot in x$figures), number, id, title, description and filename; NULL (with a message) when x has no plots or no catalog.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
get_figures_catalog(res)

Variable Importance of a Fitted Workflow

Description

Extracts variable importance from a fitted workflow. With method = "auto", a model-specific measure is used when available: impurity importance for ranger and rpart, gain for xgboost, absolute coefficients for glmnet, and absolute z/t statistics for glm/lm. For other engines (for example nnet or kernlab), or with method = "permutation", permutation importance is computed on data: the drop in performance (AUC for binary outcomes, accuracy for multiclass outcomes, RMSE for numeric outcomes) after shuffling each predictor.

Usage

get_importance(
  final_fit,
  data = NULL,
  target = NULL,
  method = c("auto", "native", "permutation"),
  nsim = 5,
  verbose = TRUE
)

Arguments

final_fit

A fitted workflow (for example result$final_fit from supervised_ml()).

data

Optional data frame with the predictors and the outcome, used for permutation importance. If NULL, only model-specific importance can be returned.

target

Name of the outcome variable in data.

method

One of "auto" (model-specific if available, otherwise permutation), "native", or "permutation".

nsim

Number of permutations per predictor.

verbose

Logical. If TRUE, the top 10 variables are printed.

Value

A data frame with columns Variable, Importance, and Rank, sorted by decreasing importance. The attribute "method" records the importance measure used. An empty data frame is returned when importance cannot be computed.

Examples

set.seed(1)
df <- data.frame(x1 = rnorm(100), x2 = rnorm(100))
df$y <- 2 * df$x1 + rnorm(100)
wf <- workflows::workflow() |>
  workflows::add_formula(y ~ .) |>
  workflows::add_model(parsnip::linear_reg() |> parsnip::set_engine("lm"))
fit <- parsnip::fit(wf, data = df)
get_importance(fit, verbose = FALSE)
get_importance(fit, data = df, target = "y", method = "permutation",
               verbose = FALSE)

Model Interpretability Analysis

Description

Computes variable importance and Shapley (SHAP) values for a fitted workflow returned by evaluate_model().

Usage

interpret_model(
  evaluation_result,
  train_data,
  test_data,
  target,
  n_shap = 100,
  verbose = TRUE
)

Arguments

evaluation_result

Result of evaluate_model(); it must contain the element final_fit (a fitted workflow).

train_data

Training data frame (used as background data for SHAP).

test_data

Test data frame (used to compute importance and SHAP).

target

Name of the outcome variable (character).

n_shap

Number of test observations used to compute SHAP values.

verbose

Logical. If TRUE, progress is printed to the console.

Value

A list of class supervisedml_interpret with elements importance (data frame returned by get_importance()) and shap (list returned by calculate_shap(), or NULL if SHAP values could not be computed).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
idx <- sample(n, 150)
rec <- recipes::recipe(y ~ ., data = df[idx, ])
wf <- workflows::workflow() |>
  workflows::add_recipe(rec) |>
  workflows::add_model(parsnip::logistic_reg() |> parsnip::set_engine("glm"))
fit <- parsnip::fit(wf, data = df[idx, ])
res <- interpret_model(list(final_fit = fit), df[idx, ], df[-idx, ], "y",
                       n_shap = 20, verbose = FALSE)
res$importance

Launch the Shiny Report Generator

Description

Opens the Shiny application that generates scientific reports from JSON files exported with export_verbose_json(). Requires the shiny package.

Usage

launch_report_generator(launch.browser = TRUE)

Arguments

launch.browser

Logical (default TRUE). Open the app in the web browser.

Value

Called for its side effect (runs the Shiny app); returns the value returned by shiny::runApp() when the app is closed.

Examples

if (interactive()) {
  launch_report_generator()
}

Launch the Integrated PsyML Shiny App

Description

Opens the integrated Shiny application that combines the machine learning pipeline, the sample size estimation and the AI report generator. Requires the shiny package.

Usage

launch_supervisedml_app(launch.browser = TRUE)

Arguments

launch.browser

Logical (default TRUE). Open the app in the web browser.

Value

Called for its side effect (runs the Shiny app); returns the value returned by shiny::runApp() when the app is closed.

Examples

if (interactive()) {
  launch_supervisedml_app()
}

Sample Size Estimation for Machine Learning via Monte Carlo Simulation

Description

Estimates the minimum sample size needed to achieve a target performance in machine learning models using Monte Carlo simulation and learning curves. It plays a role similar to G*Power, but for machine learning.

Usage

ml_sample_size(
  data = NULL,
  formula = NULL,
  task = c("classification", "regression"),
  model = c("rf"),
  metric = NULL,
  n_grid = c(50, 100, 150, 200, 300, 400, 600, 800, 1000, 1500, 2000),
  reps = 50,
  target = NULL,
  target_dir = c("higher_better", "lower_better"),
  prob_min = 0.8,
  sd_max = NULL,
  n_test = 5000,
  n_outer_splits = 1,
  bootstrap_ci = FALSE,
  n_boot = 1000,
  sampling = c("stratified", "none", "up", "down"),
  positive = "1",
  threshold = 0.5,
  sim_params = list(),
  model_params = list(),
  seed = NULL,
  verbose = TRUE
)

Arguments

data

Data frame with real data. If NULL (default), a synthetic population is simulated using sim_params.

formula

Model formula (e.g., y ~ .). Required when data is supplied.

task

Task type: "classification" (default) or "regression".

model

Model(s) to use: "rf" (random forest, default), "xgboost", "svm" or "glm". Can be a vector to compare several models.

metric

Evaluation metric (yardstick naming convention). If NULL (default), "roc_auc" is used for classification and "rsq" for regression. Classification: "roc_auc", "accuracy", "f_meas", "mcc", "kap", "bal_accuracy". Regression: "rsq", "rmse", "mae". The aliases auc, f1, kappa, balanced_accuracy and r2 are accepted.

n_grid

Numeric vector with the sample sizes to evaluate (default c(50, 100, 150, 200, 300, 400, 600, 800, 1000, 1500, 2000)). Values larger than the available training pool are dropped.

reps

Number of Monte Carlo repetitions per sample size (default 50). Recommendations: R = 50 for exploratory analysis (SE about 0.057), R = 100 for standard use (SE about 0.040), R >= 200 for publication (SE about 0.028).

target

Target metric value (e.g., 0.80 for AUC >= 0.80). If NULL (default), no sample size is recommended and only the learning curve is estimated.

target_dir

Direction of the target: "higher_better" or "lower_better". If not supplied, it is set automatically ("lower_better" for rmse and mae, "higher_better" otherwise).

prob_min

Minimum proportion of repetitions that must meet the target (default 0.80). This is the probability-of-success criterion p_k.

sd_max

Optional maximum SD of the metric (stability criterion). NULL (default) disables the criterion.

n_test

Size of the fixed test set (default 5000). If it is at least 50% of the available data, it is reduced to 20% of the data with a warning.

n_outer_splits

Number of independent test partitions used to assess the robustness of n* (default 1). It is set to 5 automatically when the data have fewer than 1000 rows and it is left at 1.

bootstrap_ci

Logical (default FALSE). If TRUE and target is given, a bootstrap 95% CI for n* is computed (single partition only).

n_boot

Number of bootstrap resamples (default 1000).

sampling

Sampling type used to draw training samples in classification: "stratified" (default), "none", "up" or "down".

positive

Label of the positive class (default "1").

threshold

Classification threshold (default 0.5).

sim_params

List with simulation parameters, used when data is NULL:

N_pop: Population size to simulate (default 30000).
p: Number of predictors (default 20).
prevalence: Positive class prevalence for binary outcomes (default 0.20).
signal: Signal strength (default 2.5). It determines the maximum achievable AUC: signal = 1.5 gives AUC about 0.70-0.75, signal = 2.5 about 0.85-0.90 and signal = 3.5 about 0.92-0.95.
noise_sd: Noise SD for regression (default 1.0).
n_classes: Number of classes for classification (default 2). With 3 or more classes a multinomial logit (softmax) simulation is used.
class_probs: Numeric vector of class probabilities for multiclass outcomes (default: equal probabilities 1/K). Ignored for binary outcomes.

model_params

List with model hyperparameters:

num.trees: Number of trees for random forest (default 500).
mtry: Number of variables considered at each split.
min.node.size: Minimum node size (default 5).
max.depth: Maximum depth (default 0, no limit).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical (default TRUE). If TRUE, progress and results are reported with message().

Details

The function implements the methodology described in Ventura-Leon (2026) for sample size estimation in machine learning. Key features:

Decision criteria: n* is the smallest n where: (1) mean performance >= target, (2) P(metric >= target) >= prob_min, (3) SD <= sd_max (optional).

SE for p_k: The standard error of the success proportion is SE(p_k) = sqrt(p_k * (1 - p_k) / R). Use R >= 100 for reliable estimates.

Multiple partitions: When N < 1000, using n_outer_splits > 1 is recommended to assess variability in n* estimates.

The power-law learning curve is metric(n) = a - b n^{-c} (or a + b n^{-c} when lower values are better), where a is the asymptote. See estimate_n() and estimate_n_plateau() to invert the fitted curve, and plot_learning_curve() and plot_distribution() to display the results.

Value

An object of class c("ml_sample_size", "list") with:

raw: Data frame with one row per repetition (model, n, rep, metric_value and, if target is given, meets_target).
summary: Data frame by model and sample size with mean, sd, se, p_meet (proportion of repetitions meeting the target) and se_p_meet (its binomial standard error).
recommend_n: Recommended sample size n* (a number, or a named list with one value per model); NA if no evaluated size meets the criteria. With several partitions it is the median n* across partitions.
recommend_n_ci: Bootstrap 95% CI for n* (list with ci_lower, ci_upper, ci_median, n_valid), or NULL.
curve_fit: Fitted power-law nls model (or a list of them per model), NULL if the fit failed.
curve_diagnostics: Fit diagnostics (pseudo_r2, rmse_fit, residuals, fit_quality).
outer_splits_results: Summary of n* across partitions (median, mean, sd, cv, range, n_valid, all_values) when n_outer_splits > 1, otherwise NULL.
settings: List with the configuration used.

References

Ventura-Leon, J. (2026). Sample Size Estimation for Machine Learning via Monte Carlo Simulation: Learning Curves and Power Laws.

Examples

# Simulated population (classification), small settings for speed
result <- ml_sample_size(
  task = "classification",
  model = "glm",
  metric = "roc_auc",
  n_grid = c(50, 100, 200, 400),
  reps = 5,
  target = 0.75,
  n_test = 500,
  sim_params = list(N_pop = 2000, p = 5, prevalence = 0.30),
  seed = 1,
  verbose = FALSE
)
result

# View recommendation
result$recommend_n

# Plot learning curve
plot_learning_curve(result)

# Small real dataset (N < 1000): several partitions and stability check
set.seed(2)
df <- data.frame(x1 = rnorm(400), x2 = rnorm(400))
df$y <- factor(ifelse(df$x1 + rnorm(400) > 0, "1", "0"))
res_real <- ml_sample_size(
  data = df,
  formula = y ~ .,
  task = "classification",
  model = "glm",
  n_grid = c(40, 80),
  reps = 3,
  target = 0.75,
  n_test = 100,
  n_outer_splits = 2,
  seed = 1,
  verbose = FALSE
)
res_real$outer_splits_results

Optimize the Classification Threshold

Description

Finds the optimal probability threshold for binary classification by evaluating thresholds from 0.01 to 0.99 (step 0.01) under one of several criteria: Youden's J, F1, a user-defined misclassification cost, or balanced accuracy. The positive class is detected from the levels of the target (recognized labels such as "yes", "1" or "positive"; otherwise the second level).

Usage

optimize_threshold(
  predictions,
  target,
  method = "youden",
  cost_fp = 1,
  cost_fn = 1,
  verbose = TRUE
)

Arguments

predictions

Data frame of predictions with the observed target (a factor with two levels) and a probability column named ⁠.pred_<positive class>⁠, such as the predictions element of a supervised_ml() result.

target

Name of the target variable.

method

Optimization criterion: "youden" (default; maximizes sensitivity + specificity - 1), "f1" (maximizes F1), "cost" (minimizes cost_fp * FP + cost_fn * FN) or "balanced" (maximizes balanced accuracy). Unknown values fall back to "youden".

cost_fp

Cost of a false positive (used when method = "cost"; default: 1).

cost_fn

Cost of a false negative (used when method = "cost"; default: 1).

verbose

Logical. Print results (default: TRUE).

Value

A list with optimal_threshold (numeric), optimal_metrics (one-row data frame with the metrics at the optimal threshold), all_results (data frame with columns threshold, sensitivity, specificity, precision, f1, youden_j, balanced_acc, total_cost, tp, tn, fp and fn for every threshold) and method.

Examples

set.seed(1)
n <- 200
x <- rnorm(n)
y <- factor(ifelse(x + rnorm(n) > 0.8, "yes", "no"), levels = c("no", "yes"))
preds <- data.frame(y = y, .pred_yes = plogis(1.5 * x - 1))
thr <- optimize_threshold(preds, target = "y", method = "f1", verbose = FALSE)
thr$optimal_threshold

Plot a causalml Object

Description

Returns the figures stored in a causalml object.

Usage

## S3 method for class 'causalml'
plot(x, type = "all", ...)

Arguments

x

An object of class causalml, as returned by causal_ml().

type

Plot type: "all" (default) or one of "treatment", "outcome", "ps", "overlap", "love", "cate", "heterogeneity", "importance", "ate_forest", "meta", "evalue" and "balance". The internal name of a figure (see names(x$figures)) is also accepted.

...

Further arguments (not used).

Value

For type = "all", the named list of 'ggplot2' objects in x$figures; otherwise the requested 'ggplot2' object (printed when called at top level). If no figure is available or the type is not found, a message is issued and NULL is returned invisibly.

Examples

set.seed(1)
n <- 300
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$treat <- rbinom(n, 1, plogis(df$x1))
df$y <- 1.5 * df$treat + df$x1 + 0.5 * df$x2 + rnorm(n)
res <- causal_ml(df, treatment = "treat", outcome = "y",
                 methods = c("propensity", "meta_learners"),
                 ps_methods = c("ipw", "doubly_robust"),
                 meta_learners = "T", run_eda = FALSE,
                 seed = 1, verbose = FALSE)
plot(res, type = "ate_forest")

Plot a supervisedml Object

Description

Returns the plots of a supervised_ml() result: a combined summary panel (default), the list of all plots, or a single plot.

Usage

## S3 method for class 'supervisedml'
plot(x, type = "panel", top_n = 15, ...)

Arguments

x

An object of class supervisedml returned by supervised_ml().

type

Type of plot: "panel" (default; four-plot summary panel, requires patchwork), "all" (list with all plots) or a single plot: "importance", "metrics" (or "comparison"), "roc", "confusion", "calibration", "calibration_curve", "threshold", "pred_vs_obs", "residuals", "residual_diagnostics", "tuning", "shap" (or "shap_beeswarm"), "shap_bar", "fairness" or "dca". Availability depends on the task and on the analyses run.

top_n

Number of variables shown in the importance plot when it has to be regenerated (default: 15).

...

Additional arguments (not used).

Value

A ggplot (or patchwork) object, a named list of ggplot objects when type = "all", or NULL (with a message) when the requested plot is not available. The plot is drawn when the returned object is printed.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
plot(res, type = "roc")
plot(res, type = "confusion")
names(plot(res, type = "all"))
if (requireNamespace("patchwork", quietly = TRUE)) plot(res)

Plot an unsupervisedml Object

Description

Displays the figures stored in an unsupervisedml object.

Usage

## S3 method for class 'unsupervisedml'
plot(x, which = NULL, ...)

Arguments

x

An object of class unsupervisedml, as returned by unsupervised_ml().

which

Figures to display: a numeric vector of indices, a character vector of figure names (see names(x$figures)), or "all". If NULL (default), the catalogue of available figures is listed with message().

...

Further arguments (not used).

Value

Invisibly returns x.

Examples

set.seed(1)
X <- data.frame(a = c(rnorm(40), rnorm(40, 3)),
                b = c(rnorm(40), rnorm(40, 3)),
                c = rnorm(80))
res <- unsupervised_ml(X,
                       clustering_algorithms = c("kmeans", "hierarchical"),
                       k_range = 2:4, reduction_methods = "pca",
                       anomaly_methods = "mahalanobis",
                       run_eda = FALSE, seed = 1, verbose = FALSE)
plot(res)
plot(res, which = 1)

Plot a Summary Panel of All Results

Description

Generic function that combines the main plots of a result into a single panel. The supervisedml method arranges four plots with patchwork: variable importance, model comparison and, for classification, confusion matrix and ROC curve or, for regression, predicted vs observed values and residuals.

Usage

plot_all(x, ...)

## S3 method for class 'supervisedml'
plot_all(x, ...)

Arguments

x

An object with a plot_all() method, such as a supervisedml object returned by supervised_ml().

...

Additional arguments passed to methods (not used by the supervisedml method).

Value

A patchwork object (drawn when printed).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
if (requireNamespace("patchwork", quietly = TRUE)) plot_all(res)

Plot All Graphs for Sample Size Results

Description

Displays the learning curve (plot_learning_curve()) and the metric distribution (plot_distribution()) side by side (using patchwork when it is installed; otherwise the plots are printed one after the other).

Usage

## S3 method for class 'ml_sample_size'
plot_all(x, ...)

Arguments

x

An object of class ml_sample_size (single model).

...

Additional arguments (not used).

Value

Invisibly returns a list with the ggplot objects learning_curve and distribution.

Examples

result <- ml_sample_size(model = "glm", n_grid = c(50, 100, 200), reps = 5,
                         target = 0.75, n_test = 500,
                         sim_params = list(N_pop = 2000, p = 5),
                         seed = 1, verbose = FALSE)
plot_all(result)

Plot the Calibration Curve

Description

Reliability diagram comparing the mean predicted probability with the observed proportion of the positive class per probability bin, before (red) and after (blue) calibration.

Usage

plot_calibration_curve(calibration_result, n_bins = 10)

Arguments

calibration_result

A list returned by calibrate_probabilities().

n_bins

Number of probability bins (default: 10).

Value

A ggplot object.

Examples

set.seed(1)
x <- rnorm(300)
y <- factor(ifelse(x + rnorm(300) > 0, "yes", "no"), levels = c("no", "yes"))
preds <- data.frame(y = y, .pred_yes = plogis(3 * x))
cal <- calibrate_probabilities(preds, target = "y", verbose = FALSE)
plot_calibration_curve(cal)

Plot the Metric Distribution by Sample Size

Description

Shows boxplots of the Monte Carlo distribution of the metric for each sample size of an ml_sample_size() result, with the target as a dashed line.

Usage

plot_distribution(x, ...)

Arguments

x

An object of class ml_sample_size.

...

Additional arguments (not used).

Value

If ggplot2 is available (it is a dependency), the plot is printed and the ggplot object is returned invisibly. Otherwise a base R boxplot is drawn and x is returned invisibly.

Examples

result <- ml_sample_size(model = "glm", n_grid = c(50, 100, 200), reps = 5,
                         target = 0.75, n_test = 500,
                         sim_params = list(N_pop = 2000, p = 5),
                         seed = 1, verbose = FALSE)
plot_distribution(result)

Plot Variable Importance

Description

Horizontal bar chart of the most important variables.

Usage

plot_importance(interpret_result, top_n = 15)

Arguments

interpret_result

A list with an importance element (data frame with columns Variable and Importance, sorted by decreasing importance), such as the result of interpret_model().

top_n

Maximum number of variables shown (default: 15).

Value

A ggplot object, or NULL (with a message) when no importance data are available.

Examples

set.seed(1)
df <- data.frame(x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100))
df$y <- 2 * df$x1 - df$x2 + rnorm(100)
wf <- workflows::workflow() |>
  workflows::add_formula(y ~ .) |>
  workflows::add_model(parsnip::linear_reg() |> parsnip::set_engine("lm"))
fit <- parsnip::fit(wf, data = df)
interp <- list(importance = get_importance(fit, verbose = FALSE))
plot_importance(interp)

Plot the Learning Curve of a Sample Size Analysis

Description

Plots the learning curve of an ml_sample_size() result, showing how model performance changes with sample size, together with the fitted power-law curve, its confidence band, the target and the recommended n*.

Usage

plot_learning_curve(
  x,
  show_target = TRUE,
  show_fit = TRUE,
  show_ci = TRUE,
  ci_level = 0.95,
  title = NULL,
  ...
)

Arguments

x

An object of class ml_sample_size.

show_target

Logical (default TRUE). Draw a horizontal line at the target.

show_fit

Logical (default TRUE). Draw the fitted power-law curve.

show_ci

Logical (default TRUE). Draw the confidence band of the mean.

ci_level

Confidence level of the band (default 0.95).

title

Plot title. If NULL (default), it is built automatically.

...

Additional arguments (not used).

Value

If ggplot2 is available (it is a dependency), the plot is printed and the ggplot object is returned invisibly. Otherwise a base R plot is drawn and x is returned invisibly.

Examples

result <- ml_sample_size(
  task = "classification",
  model = "glm",
  metric = "accuracy",
  n_grid = c(50, 100, 200, 400),
  reps = 5,
  target = 0.75,
  n_test = 500,
  sim_params = list(N_pop = 2000, p = 5),
  seed = 1,
  verbose = FALSE
)
plot_learning_curve(result)

Plot the Profile of Performance Metrics

Description

Draws a point-and-line plot with the profile of all cross-validated performance metrics of each model. Each model is a line connecting its values across metrics, which makes it easy to compare strengths and weaknesses.

Usage

plot_metrics_profile(
  x,
  metrics = NULL,
  highlight = NULL,
  size_point = 3.5,
  size_line = 0.9,
  show_values = TRUE
)

Arguments

x

A supervisedml object (its cv_summary element is used) or a data frame with a model column and numeric metric columns.

metrics

Optional character vector with the metrics to include. If NULL (default), all numeric columns are used.

highlight

Optional name of the model to highlight. If NULL (default) and x is a supervisedml object, the best model is highlighted.

size_point

Numeric. Size of the points (default: 3.5).

size_line

Numeric. Width of the lines (default: 0.9).

show_values

Logical. Show the numeric values above the points (default: TRUE).

Value

A ggplot object.

Examples


cv <- data.frame(model = c("glm", "rf", "xgboost"),
                 roc_auc = c(0.81, 0.86, 0.84),
                 accuracy = c(0.74, 0.79, 0.78),
                 mcc = c(0.48, 0.57, 0.55))
plot_metrics_profile(cv, highlight = "rf")
plot_metrics_profile(cv, metrics = c("roc_auc", "mcc"), show_values = FALSE)

set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
plot_metrics_profile(res)

Residual Diagnostic Plots

Description

Residuals versus predictions, normal Q-Q plot, histogram of residuals and scale-location plot.

Usage

plot_residual_diagnostics(residual_analysis)

Arguments

residual_analysis

A list returned by advanced_residual_analysis().

Value

If the patchwork package is installed, a patchwork object combining the four plots; otherwise a list of four ggplot objects (residuals_vs_fitted, qq_plot, histogram, scale_location).

Examples

set.seed(1)
obs <- rnorm(150, 10, 2)
preds <- data.frame(y = obs, .pred = obs + rnorm(150))
ra <- advanced_residual_analysis(preds, target = "y", verbose = FALSE)
p <- plot_residual_diagnostics(ra)

Plot SHAP Feature Importance

Description

Bar chart of the mean absolute SHAP value of the most important variables, drawn with shapviz when it is installed (otherwise with ggplot2).

Usage

plot_shap_bar(interpret_result, top_n = 15)

Arguments

interpret_result

A list with a shap element as returned by calculate_shap(), such as the result of interpret_model() or the interpretation element of a supervised_ml() result.

top_n

Maximum number of variables shown (default: 15).

Value

A ggplot object, or NULL (with a message) when no SHAP values are available.

Examples

if (requireNamespace("kernelshap", quietly = TRUE)) {
  set.seed(1)
  df <- data.frame(x1 = rnorm(120), x2 = rnorm(120), x3 = rnorm(120))
  df$y <- df$x1 - df$x2 + rnorm(120)
  wf <- workflows::workflow() |>
    workflows::add_formula(y ~ .) |>
    workflows::add_model(parsnip::linear_reg() |> parsnip::set_engine("lm"))
  fit <- parsnip::fit(wf, data = df[1:100, ])
  sh <- calculate_shap(fit, df[1:100, ], df[101:120, ], "y",
                       n_samples = 20, bg_n = 30, verbose = FALSE)
  interp <- list(shap = sh)
  plot_shap_bar(interp)
}

Plot SHAP Dependence for One Variable

Description

Scatter plot of the SHAP values of one variable against its observed values in the explained sample, with a loess smoother.

Usage

plot_shap_dependence(interpret_result, variable, test_data)

Arguments

interpret_result

A list with a shap element as returned by calculate_shap(), such as the result of interpret_model() or the interpretation element of a supervised_ml() result.

variable

Character string with the name of the variable to plot.

test_data

Not used; the explained sample stored in interpret_result$shap$test_sample is plotted. Kept for backward compatibility.

Value

A ggplot object, or NULL (with a message) when no SHAP values are available or variable is not among them.

Examples

if (requireNamespace("kernelshap", quietly = TRUE)) {
  set.seed(1)
  df <- data.frame(x1 = rnorm(120), x2 = rnorm(120), x3 = rnorm(120))
  df$y <- df$x1 - df$x2 + rnorm(120)
  wf <- workflows::workflow() |>
    workflows::add_formula(y ~ .) |>
    workflows::add_model(parsnip::linear_reg() |> parsnip::set_engine("lm"))
  fit <- parsnip::fit(wf, data = df[1:100, ])
  sh <- calculate_shap(fit, df[1:100, ], df[101:120, ], "y",
                       n_samples = 20, bg_n = 30, verbose = FALSE)
  interp <- list(shap = sh)
  plot_shap_dependence(interp, variable = "x1", test_data = df[101:120, ])
}

Plot a SHAP Summary (Beeswarm)

Description

Beeswarm plot of the SHAP values of the most important variables, colored by the feature value, drawn with shapviz when it is installed. Otherwise a boxplot of the SHAP values per variable is drawn.

Usage

plot_shap_summary(interpret_result, top_n = 15)

Arguments

interpret_result

A list with a shap element as returned by calculate_shap(), such as the result of interpret_model() or the interpretation element of a supervised_ml() result.

top_n

Maximum number of variables shown (default: 15).

Value

A ggplot object, or NULL (with a message) when no SHAP values are available.

Examples

if (requireNamespace("kernelshap", quietly = TRUE)) {
  set.seed(1)
  df <- data.frame(x1 = rnorm(120), x2 = rnorm(120), x3 = rnorm(120))
  df$y <- df$x1 - df$x2 + rnorm(120)
  wf <- workflows::workflow() |>
    workflows::add_formula(y ~ .) |>
    workflows::add_model(parsnip::linear_reg() |> parsnip::set_engine("lm"))
  fit <- parsnip::fit(wf, data = df[1:100, ])
  sh <- calculate_shap(fit, df[1:100, ], df[101:120, ], "y",
                       n_samples = 20, bg_n = 30, verbose = FALSE)
  interp <- list(shap = sh)
  plot_shap_summary(interp)
}

Plot the Threshold Optimization

Description

Plots sensitivity, specificity, F1 and precision as a function of the threshold, marking the optimal threshold and 0.5.

Usage

plot_threshold_optimization(threshold_result)

Arguments

threshold_result

A list returned by optimize_threshold().

Value

A ggplot object.

Examples

set.seed(1)
x <- rnorm(200)
y <- factor(ifelse(x + rnorm(200) > 0.8, "yes", "no"), levels = c("no", "yes"))
preds <- data.frame(y = y, .pred_yes = plogis(1.5 * x - 1))
thr <- optimize_threshold(preds, target = "y", verbose = FALSE)
plot_threshold_optimization(thr)

Plot the Hyperparameter Tuning Results

Description

Plots the cross-validated performance of the evaluated hyperparameter combinations with tune::autoplot().

Usage

plot_tuning(tune_result, metric = NULL)

Arguments

tune_result

Result of tune_best_model() (or the tuning element of a supervised_ml() result).

metric

Optional character vector with the metrics to plot. If NULL (default) all computed metrics are shown.

Value

A ggplot object, or NULL (with a message) when no tuning was performed.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
plot_tuning(res$tuning)  # NULL: no tuning was performed

res_rf <- supervised_ml(df, target = "y", models = "rf", cv_folds = 3,
                        tune_grid = 3, run_shap = FALSE, n_cores = 1,
                        verbose = FALSE)
plot_tuning(res_rf$tuning, metric = "roc_auc")

Predict Conditional Average Treatment Effects from a causalml Object

Description

Returns conditional average treatment effect (CATE) estimates, either for the original data or for new observations.

Usage

## S3 method for class 'causalml'
predict(object, new_data = NULL, method = "best", ...)

Arguments

object

An object of class causalml, as returned by causal_ml().

new_data

A data frame with new observations containing the covariates used in the analysis. If NULL (default), the CATE estimates of the original data are returned (from the causal forest or, if not available, the X-, T- or S-learner).

method

Method used for prediction on new data: "best" (default; causal forest if available, otherwise T-learner), "causal_forest" or "T".

...

Further arguments (not used).

Value

A numeric vector of CATE estimates, one per observation.

Examples

set.seed(1)
n <- 300
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$treat <- rbinom(n, 1, plogis(df$x1))
df$y <- 1.5 * df$treat + df$x1 + 0.5 * df$x2 + rnorm(n)
res <- causal_ml(df, treatment = "treat", outcome = "y",
                 methods = c("propensity", "meta_learners"),
                 ps_methods = c("ipw", "doubly_robust"),
                 meta_learners = "T", run_eda = FALSE,
                 seed = 1, verbose = FALSE)
head(predict(res))
# New data must be on the same (normalized) scale as the covariates
predict(res, new_data = res$preprocessed$data[1:5, ], method = "T")

Predict with a supervisedml Model

Description

Generates predictions for new data with the final model of a supervised_ml() result. When the model was fitted with fairness_correction = "postprocesamiento" and new_data contains the protected variable, class predictions use the group-specific thresholds (observations of unknown groups use 0.5).

Usage

## S3 method for class 'supervisedml'
predict(object, new_data, type = "class", ...)

Arguments

object

An object of class supervisedml.

new_data

A data frame with the same predictors used for training.

type

Type of prediction: "class" (default; predicted class for classification, numeric prediction for regression) or "prob" (class probabilities, classification only).

...

Additional arguments (not used).

Value

A tibble with one row per row of new_data: column .pred_class (factor) for class predictions, .pred (numeric) for regression, or one ⁠.pred_<level>⁠ column per class with the probabilities when type = "prob".

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
new_df <- data.frame(x1 = rnorm(5), x2 = rnorm(5), x3 = rnorm(5))
predict(res, new_df)
predict(res, new_df, type = "prob")

Assign New Observations to Clusters

Description

Assigns new observations to the clusters of an unsupervisedml object. The new data are normalized with the parameters of the original analysis and assigned to the nearest centroid (k-means and hierarchical), with the fitted mixture model (GMM, requires 'mclust') or with the posterior class probabilities (LCA, requires 'poLCA').

Usage

## S3 method for class 'unsupervisedml'
predict(object, newdata, algorithm = NULL, ...)

Arguments

object

An object of class unsupervisedml, as returned by unsupervised_ml().

newdata

A data frame with the new observations; it must contain the variables used in the analysis (names(object$preprocessed$data_clean)), on their original scale.

algorithm

Algorithm used for the assignment: "kmeans", "hierarchical", "gmm" or "lca". If NULL (default), the best algorithm of the evaluation (or the first one available) is used.

...

Further arguments (not used).

Value

An integer vector with the cluster label of each row of newdata.

Examples

set.seed(1)
X <- data.frame(a = c(rnorm(40), rnorm(40, 3)),
                b = c(rnorm(40), rnorm(40, 3)),
                c = rnorm(80))
res <- unsupervised_ml(X, methods = "clustering",
                       clustering_algorithms = c("kmeans", "hierarchical"),
                       k = 2, run_eda = FALSE, seed = 1, verbose = FALSE)
new_obs <- data.frame(a = c(0, 3), b = c(0, 3), c = c(0, 0))
predict(res, new_obs, algorithm = "kmeans")

Feature Selection with Boruta

Description

Runs the Boruta all-relevant feature selection algorithm (maximum 100 runs) on complete cases and keeps the confirmed and tentative variables. If the Boruta package is not installed, the step is skipped.

Usage

prep_feature_selection(data, target, seed = NULL, verbose = TRUE)

Arguments

data

Data frame (usually the training data).

target

Name of the target variable.

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print results (default: TRUE).

Value

A list with selected (names of the confirmed and tentative variables), boruta (the Boruta object) and importance (data frame from Boruta::attStats() with a Variable column). If Boruta is not available, selected and boruta are NULL.

Examples

if (requireNamespace("Boruta", quietly = TRUE)) {
  fs <- prep_feature_selection(iris, "Species", seed = 1, verbose = FALSE)
  fs$selected
}

Create the Preprocessing Recipe

Description

Defines a recipes recipe with, in this order: imputation, winsorization of outliers, removal of highly correlated and high-VIF numeric predictors, pooling of rare categories, dummy encoding, removal of zero-variance predictors, normalization, PCA and class balancing (each step optional). Thresholds and winsorization limits are computed on data.

Usage

prep_recipe(
  data,
  target,
  task,
  impute = TRUE,
  impute_method = c("knn", "median", "mean"),
  normalize = FALSE,
  normalize_method = c("zscore", "minmax"),
  use_pca = FALSE,
  pca_threshold = 0.95,
  balance_classes = FALSE,
  balance_method = c("smote", "adasyn", "rose", "up", "down"),
  treat_outliers = TRUE,
  outlier_percentile = 0.05,
  remove_high_cor = TRUE,
  cor_threshold = 0.9,
  remove_high_vif = TRUE,
  vif_threshold = 5,
  group_rare = TRUE,
  other_threshold = 0.05,
  remove_zv = TRUE,
  verbose = TRUE
)

Arguments

data

Data frame (usually the training data).

target

Name of the target variable.

task

Type of task: "classification" or "regression".

impute

Logical. Impute missing values (default: TRUE).

impute_method

Imputation method for numeric predictors: "knn" (default), "median" or "mean". Nominal predictors are imputed with the mode.

normalize

Logical. Normalize numeric predictors (default: FALSE).

normalize_method

Normalization method: "zscore" (default) or "minmax" (rescaling between 0 and 1).

use_pca

Logical. Add a PCA step (default: FALSE).

pca_threshold

Proportion of variance retained by the PCA step (default: 0.95).

balance_classes

Logical. Balance classes for classification (default: FALSE; requires the themis package).

balance_method

Balancing method: "smote" (default), "adasyn", "rose", "up" or "down".

treat_outliers

Logical. Winsorize numeric predictors with outliers (1.5 x IQR rule) (default: TRUE).

outlier_percentile

Winsorization percentile (default: 0.05, i.e. 5th and 95th percentiles).

remove_high_cor

Logical. Remove highly correlated predictors (default: TRUE).

cor_threshold

Absolute correlation threshold (default: 0.90).

remove_high_vif

Logical. Iteratively remove predictors with high VIF (default: TRUE).

vif_threshold

VIF threshold (default: 5).

group_rare

Logical. Pool rare categories (default: TRUE).

other_threshold

Minimum category proportion for pooling (default: 0.05).

remove_zv

Logical. Remove zero-variance predictors (default: TRUE).

verbose

Logical. Print the configured steps and treatments (default: TRUE).

Value

An untrained recipe object (class recipe) with an attribute "treatments_applied": a list with elements outliers, correlation and/or vif describing the variables treated or removed.

Examples

set.seed(1)
df <- data.frame(x1 = rnorm(80), x2 = rnorm(80))
df$x3 <- df$x1 + rnorm(80, sd = 0.05)
df$y <- df$x1 + df$x2 + rnorm(80)
rec <- prep_recipe(df, target = "y", task = "regression", verbose = FALSE)
attr(rec, "treatments_applied")$correlation$vars_removed

Train/Test Split

Description

Stratified train/test split with rsample::initial_split() (strata by class for classification and by quartiles for regression).

Usage

prep_split(data, target, task, test_split = 0.2, seed = NULL, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the target variable (used for stratification).

task

Type of task (kept for compatibility; stratification is always applied).

test_split

Proportion of observations assigned to the test set (default: 0.20).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print results (default: TRUE).

Value

A list with train and test (data frames) and split (the rsample split object).

Examples

sp <- prep_split(iris, "Species", task = "classification", seed = 1,
                 verbose = FALSE)
table(sp$test$Species)

Prepare the Target Variable

Description

Converts character columns to factors and coerces the target to a factor (classification) or to numeric (regression).

Usage

prep_target(data, target, task, verbose = TRUE)

Arguments

data

Data frame.

target

Name of the target variable.

task

Type of task: "classification" or "regression".

verbose

Logical. Print results (default: TRUE).

Value

The data frame with character columns converted to factors and the target coerced to the type required by task.

Examples

df <- data.frame(x = 1:6, y = c("a", "b", "a", "b", "a", "b"))
str(prep_target(df, "y", task = "classification", verbose = FALSE))

Preprocessing Pipeline

Description

Runs the complete preprocessing stage: preparation of the target, stratified train/test split, optional feature selection with Boruta and definition of a recipes preprocessing recipe. The recipe is only defined here (not trained); it is estimated later inside the resampling to avoid data leakage.

Usage

preprocess_data(
  data,
  target,
  task = "auto",
  test_split = 0.2,
  feature_selection = FALSE,
  balance_classes = FALSE,
  balance_method = c("smote", "adasyn", "rose", "up", "down"),
  impute = TRUE,
  impute_method = c("knn", "median", "mean"),
  normalize = FALSE,
  normalize_method = c("zscore", "minmax"),
  use_pca = FALSE,
  pca_threshold = 0.95,
  treat_outliers = TRUE,
  outlier_percentile = 0.05,
  remove_high_cor = TRUE,
  cor_threshold = 0.9,
  remove_high_vif = TRUE,
  vif_threshold = 5,
  group_rare = TRUE,
  other_threshold = 0.05,
  remove_zv = TRUE,
  seed = NULL,
  verbose = TRUE
)

Arguments

data

Data frame with the data.

target

Name of the target variable.

task

Type of task: "classification", "regression" or "auto" (default; detected from the target).

test_split

Proportion of observations assigned to the test set (default: 0.20).

feature_selection

Logical. Use Boruta for feature selection (default: FALSE; requires the Boruta package).

balance_classes

Logical. Balance the classes of a classification target (default: FALSE; requires the themis package).

balance_method

Balancing method: "smote" (default), "adasyn", "rose", "up" (random upsampling) or "down" (random downsampling).

impute

Logical. Impute missing values (default: TRUE).

impute_method

Imputation method for numeric predictors: "knn" (default), "median" or "mean". Nominal predictors are imputed with the mode.

normalize

Logical. Normalize numeric predictors (default: FALSE).

normalize_method

Normalization method: "zscore" (default) or "minmax" (rescaling between 0 and 1).

use_pca

Logical. Add a principal component analysis step (default: FALSE).

pca_threshold

Proportion of variance retained by the PCA step (default: 0.95).

treat_outliers

Logical. Treat outliers by winsorization (default: TRUE).

outlier_percentile

Percentile used for winsorization (default: 0.05, i.e. values are limited to the 5th and 95th percentiles).

remove_high_cor

Logical. Remove predictors with high correlation (default: TRUE).

cor_threshold

Absolute correlation threshold for removal (default: 0.90).

remove_high_vif

Logical. Remove predictors with high VIF (default: TRUE).

vif_threshold

VIF threshold for removal (default: 5).

group_rare

Logical. Pool rare categories with recipes::step_other() (default: TRUE).

other_threshold

Minimum proportion below which a category is pooled into "other" (default: 0.05).

remove_zv

Logical. Remove zero-variance predictors (default: TRUE).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print progress (default: TRUE).

Value

An object of class c("supervisedml_preprocess", "list") with elements data (data with the prepared target), train_data and test_data (data frames), split (the rsample split object), selected_features (character vector, or NULL without feature selection), boruta_result (only with feature selection), recipe (an untrained recipes::recipe object, see prep_recipe()), task and target.

Examples

set.seed(1)
n <- 120
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n),
                 g = sample(c("a", "b", "c"), n, TRUE))
df$y <- factor(ifelse(df$x1 + rnorm(n) > 0, "yes", "no"))
pp <- preprocess_data(df, target = "y", seed = 1, verbose = FALSE)
dim(pp$train_data)
pp$recipe

Print a Block-Based Split

Description

Prints the sizes of the training and test sets and the distribution of test cases across blocks.

Usage

## S3 method for class 'block_split'
print(x, ...)

Arguments

x

A block_split object returned by block_split().

...

Additional arguments (ignored).

Value

Invisibly returns x.

Examples

res <- block_split(iris, num_blocks = 3, test_prop = 0.2, seed = 1)
print(res)

Print a causalml Object

Description

Prints the configuration of the analysis, the average treatment effect estimated by each method and the E-value.

Usage

## S3 method for class 'causalml'
print(x, ...)

Arguments

x

An object of class causalml, as returned by causal_ml().

...

Further arguments (not used).

Value

Invisibly returns x.

Examples

set.seed(1)
n <- 300
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$treat <- rbinom(n, 1, plogis(df$x1))
df$y <- 1.5 * df$treat + df$x1 + 0.5 * df$x2 + rnorm(n)
res <- causal_ml(df, treatment = "treat", outcome = "y",
                 methods = c("propensity", "meta_learners"),
                 ps_methods = c("ipw", "doubly_robust"),
                 meta_learners = "T", run_eda = FALSE,
                 seed = 1, verbose = FALSE)
print(res)

Print a Diversity-Based Split

Description

Prints the sizes of the training and test sets and the diversity metrics.

Usage

## S3 method for class 'diversity_split'
print(x, ...)

Arguments

x

A diversity_split object returned by diversity_split().

...

Additional arguments (ignored).

Value

Invisibly returns x.

Examples

res <- diversity_split(iris, target = "Species", method = "dissimilarity",
                       seed = 1, verbose = FALSE)
print(res)

Print ML Sample Size Results

Description

Prints the settings, the recommended sample size n* (with its bootstrap CI and curve-fit quality when available) and the summary by sample size of an ml_sample_size() result.

Usage

## S3 method for class 'ml_sample_size'
print(x, ...)

Arguments

x

An object of class ml_sample_size.

...

Additional arguments (ignored).

Value

Invisibly returns x.

Examples

res <- ml_sample_size(model = "glm", n_grid = c(50, 100, 200), reps = 3,
                      target = 0.75, n_test = 300,
                      sim_params = list(N_pop = 1500, p = 5),
                      seed = 1, verbose = FALSE)
print(res)

Print a supervisedml Object

Description

Prints a console report of a supervised_ml() result: configuration, best model and tuned parameters, test metrics, top five variables and, when available, fairness and decision curve results (console text is in Spanish).

Usage

## S3 method for class 'supervisedml'
print(x, ...)

Arguments

x

An object of class supervisedml.

...

Additional arguments (not used).

Value

Invisibly returns x. Called for its side effect of printing.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
print(res)

Print an unsupervisedml Object

Description

Prints a short overview of the analysis: optimal k, clustering algorithms and best algorithm, dimensionality reduction and anomaly counts.

Usage

## S3 method for class 'unsupervisedml'
print(x, ...)

Arguments

x

An object of class unsupervisedml, as returned by unsupervised_ml().

...

Further arguments (not used).

Value

Invisibly returns x.

Examples

set.seed(1)
X <- data.frame(a = c(rnorm(40), rnorm(40, 3)),
                b = c(rnorm(40), rnorm(40, 3)),
                c = rnorm(80))
res <- unsupervised_ml(X,
                       clustering_algorithms = c("kmeans", "hierarchical"),
                       k_range = 2:4, reduction_methods = "pca",
                       anomaly_methods = "mahalanobis",
                       run_eda = FALSE, seed = 1, verbose = FALSE)
print(res)

Print the Catalog of Figures

Description

Prints the catalog of figures returned by get_figures_catalog(): id, title, description, file name and how to access each figure (console text in Spanish).

Usage

print_figures_catalog(x)

Arguments

x

An object of class supervisedml returned by supervised_ml().

Value

Invisibly returns NULL. Called for its side effect of printing.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
print_figures_catalog(res)

Fairness-Oriented Stratified Resampling

Description

Pre-processing correction for fairness. Every combination of outcome class and protected group is upsampled with replacement until it has as many rows as the largest combination, so that all outcome-by-group cells are balanced in the training data.

Usage

resample_fairness(
  train_data,
  target,
  protected_var,
  seed = NULL,
  verbose = TRUE
)

Arguments

train_data

Training data frame.

target

Name of the outcome variable.

protected_var

Name of the protected variable.

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical (default TRUE). Print the size of each combination before and after resampling.

Value

A data frame with the same columns as train_data, containing the original rows plus the upsampled rows (grouped by outcome-by-group combination, with reset row names).

Examples

set.seed(1)
train <- data.frame(
  x = rnorm(100),
  outcome = factor(sample(c("no", "yes"), 100, replace = TRUE, prob = c(0.7, 0.3))),
  sex = sample(c("female", "male"), 100, replace = TRUE, prob = c(0.8, 0.2))
)
balanced <- resample_fairness(train, target = "outcome",
                              protected_var = "sex", seed = 1)
table(balanced$outcome, balanced$sex)

Nested Cross-Validation

Description

Runs nested cross-validation to obtain a less biased estimate of the performance of a model whose hyperparameters are tuned: an inner cross-validation tunes the model by grid search and an outer cross-validation evaluates the tuned model on data not used for tuning.

Usage

run_nested_cv(
  data,
  target,
  task,
  recipe,
  model_name = "rf",
  outer_folds = 5,
  inner_folds = 5,
  grid_size = 10,
  seed = NULL,
  verbose = TRUE
)

Arguments

data

Training data (data frame).

target

Name of the target variable.

task

Type of task: "classification" or "regression".

recipe

A recipes recipe for the preprocessing (e.g. from prep_recipe() or recipes::recipe()).

model_name

Model to tune: "rf" (default, random forest with 500 trees) or "xgboost".

outer_folds

Number of outer folds (default: 5).

inner_folds

Number of inner folds (default: 5).

grid_size

Size of the tuning grid (default: 10).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print progress (default: TRUE).

Value

A list with results (data frame with one row per outer fold: fold and roc_auc and accuracy for classification, or rmse and rsq for regression), summary (list with the mean and SD of those metrics), task, model and config (numbers of outer and inner folds).

Examples


set.seed(1)
n <- 100
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- df$x1 + 0.5 * df$x2 + rnorm(n)
rec <- recipes::recipe(y ~ ., data = df)
ncv <- run_nested_cv(df, target = "y", task = "regression", recipe = rec,
                     outer_folds = 2, inner_folds = 2, grid_size = 2,
                     seed = 1, verbose = FALSE)
ncv$results

Save All Plots to PNG Files

Description

Saves every plot of a supervised_ml() result as a PNG file with a descriptive name (e.g. Figura_1_Importancia_Variables.png) plus the combined summary panel (Figura_0_Panel_Resumen.png, only when patchwork is installed). The directory is created if needed.

Usage

save_all_plots(
  x,
  path,
  prefix = NULL,
  width = 8,
  height = 6,
  dpi = 300,
  verbose = TRUE
)

Arguments

x

An object of class supervisedml returned by supervised_ml().

path

Directory where the files are written. Required; there is no default.

prefix

Optional character prefix added to every file name (default: NULL).

width

Width of each figure in inches (default: 8).

height

Height of each figure in inches (default: 6).

dpi

Resolution in dots per inch (default: 300).

verbose

Logical. Print the catalog of saved figures (default: TRUE).

Value

Invisibly, a named list with one element per saved file, each a list with filename, full_path and catalog (the figure metadata, see generate_all_plots()).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
out_dir <- file.path(tempdir(), "psyml_figures")
saved <- save_all_plots(res, path = out_dir, dpi = 72, verbose = FALSE)
list.files(out_dir)
unlink(out_dir, recursive = TRUE)

Save All Figures of an Unsupervised Analysis

Description

Saves every ggplot2 figure stored in an unsupervisedml object as a PNG file in output_dir. Nothing is written unless a directory is supplied.

Usage

save_unsupervised_plots(
  result,
  output_dir,
  width = 10,
  height = 7,
  dpi = 300,
  verbose = TRUE
)

Arguments

result

Object of class unsupervisedml.

output_dir

Directory where the PNG files are written (required; for example tempdir()). It is created if it does not exist.

width

Width in inches (default: 10).

height

Height in inches (default: 7).

dpi

Resolution in dots per inch (default: 300).

verbose

Logical. If TRUE, the output directory and the number of saved files are reported with message().

Value

Invisibly, a character vector with the paths of the saved files.

Examples


set.seed(1)
X <- data.frame(a = c(rnorm(40), rnorm(40, 3)), b = c(rnorm(40), rnorm(40, 3)))
res <- unsupervised_ml(X, methods = "clustering", clustering_algorithms = "kmeans",
                       k = 2, run_eda = FALSE,
                       verbose = FALSE)
out <- file.path(tempdir(), "psyml_unsup_figs")
files <- save_unsupervised_plots(res, output_dir = out, dpi = 72)
unlink(out, recursive = TRUE)

Set Up Cross-Validation Folds

Description

Creates k-fold cross-validation folds of the training data, stratified by the outcome in classification.

Usage

setup_cv(train_data, target, task, cv_folds = 10, seed = NULL, verbose = TRUE)

Arguments

train_data

Data frame with the training data.

target

Character string with the name of the outcome variable.

task

Type of task: "classification" or "regression".

cv_folds

Number of folds (default: 10).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print the progress and an explanation of each step in the console (default: TRUE).

Value

An rset object (tibble with columns splits and id) created by rsample::vfold_cv().

Examples

set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
folds <- setup_cv(df, target = "y", task = "classification",
                  cv_folds = 3, seed = 1)
folds

Summarize a causalml Object

Description

Prints the comparison table of the methods and the sensitivity analysis (E-value).

Usage

## S3 method for class 'causalml'
summary(object, ...)

Arguments

object

An object of class causalml, as returned by causal_ml().

...

Further arguments (not used).

Value

Invisibly returns object.

Examples

set.seed(1)
n <- 300
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$treat <- rbinom(n, 1, plogis(df$x1))
df$y <- 1.5 * df$treat + df$x1 + 0.5 * df$x2 + rnorm(n)
res <- causal_ml(df, treatment = "treat", outcome = "y",
                 methods = c("propensity", "meta_learners"),
                 ps_methods = c("ipw", "doubly_robust"),
                 meta_learners = "T", run_eda = FALSE,
                 seed = 1, verbose = FALSE)
summary(res)

Summarize a supervisedml Object

Description

Prints the cross-validation comparison of all trained models, the test metrics and the tuned hyperparameters of a supervised_ml() result (console text is in Spanish).

Usage

## S3 method for class 'supervisedml'
summary(object, ...)

Arguments

object

An object of class supervisedml.

...

Additional arguments (not used).

Value

Invisibly returns object. Called for its side effect of printing.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
summary(res)

Summarize an unsupervisedml Object

Description

Prints a detailed summary: data dimensions, comparison of the clustering algorithms, best algorithm and PCA explained variance.

Usage

## S3 method for class 'unsupervisedml'
summary(object, ...)

Arguments

object

An object of class unsupervisedml, as returned by unsupervised_ml().

...

Further arguments (not used).

Value

Invisibly returns object.

Examples

set.seed(1)
X <- data.frame(a = c(rnorm(40), rnorm(40, 3)),
                b = c(rnorm(40), rnorm(40, 3)),
                c = rnorm(80))
res <- unsupervised_ml(X,
                       clustering_algorithms = c("kmeans", "hierarchical"),
                       k_range = 2:4, reduction_methods = "pca",
                       anomaly_methods = "mahalanobis",
                       run_eda = FALSE, seed = 1, verbose = FALSE)
summary(res)

Supervised Machine Learning for Classification and Regression

Description

Main function of the package. It runs a complete supervised machine learning pipeline: exploratory data analysis, preprocessing, training of several models with cross-validation, hyperparameter tuning of the best model, evaluation on a held-out test set, interpretability (permutation importance and SHAP values) and optional advanced diagnostics (data leakage, threshold optimization, interactions, calibration, nested cross-validation, fairness and decision curve analysis).

Usage

supervised_ml(
  data,
  target,
  task = c("auto", "classification", "regression"),
  models = c("rf", "xgboost", "svm", "nnet", "glm", "tree"),
  test_split = 0.2,
  cv_folds = 10,
  select_metric = NULL,
  tune_best = TRUE,
  tune_method = c("random", "grid", "bayes", "racing"),
  tune_grid = 20,
  tune_iter = 30,
  feature_engineering = FALSE,
  custom_features = NULL,
  feature_selection = FALSE,
  balance_classes = FALSE,
  balance_method = c("smote", "adasyn", "rose", "up", "down"),
  impute = TRUE,
  impute_method = c("knn", "median", "mean"),
  normalize = TRUE,
  normalize_method = c("zscore", "minmax"),
  use_pca = FALSE,
  pca_threshold = 0.95,
  treat_outliers = TRUE,
  outlier_percentile = 0.05,
  remove_high_cor = TRUE,
  cor_threshold = 0.9,
  remove_high_vif = TRUE,
  vif_threshold = 5,
  run_eda = TRUE,
  run_shap = TRUE,
  n_shap = 100,
  optimize_threshold = TRUE,
  threshold_method = "youden",
  calibrate_probs = FALSE,
  calibration_method = "platt",
  check_leakage = TRUE,
  nested_cv = FALSE,
  analyze_interactions = TRUE,
  protected_var = NULL,
  run_fairness = FALSE,
  fairness_threshold = 0.8,
  fairness_correction = c("none", "postprocesamiento", "preprocesamiento"),
  run_dca = FALSE,
  dca_thresholds = seq(0.01, 0.99, by = 0.01),
  n_cores = 2,
  seed = NULL,
  verbose = TRUE
)

Arguments

data

A data frame with the predictors and the outcome.

target

Character string with the name of the outcome variable.

task

Type of task: "auto" (default; detected from the outcome), "classification" or "regression".

models

Character vector with the models to train (default: c("rf", "xgboost", "svm", "nnet", "glm", "tree")). Available: "rf" (random forest), "xgboost", "svm" (RBF kernel), "nnet" (neural network), "glm" (logistic or linear regression), "glmnet" (regularized regression), "tree" (decision tree) and "nb" (naive Bayes). Some models need suggested packages (e.g. kernlab, glmnet, rpart).

test_split

Proportion of the data held out as test set (default: 0.20).

cv_folds

Number of cross-validation folds (default: 10).

select_metric

Metric used to select the best model. If NULL (default) it is chosen automatically: ROC-AUC for classification and R-squared for regression. Classification: "roc_auc", "mcc", "f_meas" (F1), "f2_meas" (F2), "accuracy", "sensitivity", "specificity", "bal_accuracy", "pr_auc", "kap", "rank_mean". Regression: "rsq", "rmse", "mae", "rank_mean".

tune_best

Logical. Tune the hyperparameters of the best model (default: TRUE).

tune_method

Hyperparameter search method: "random" (random search, default), "grid" (grid search), "bayes" (Bayesian optimization) or "racing" (racing with ANOVA).

tune_grid

Number of hyperparameter combinations evaluated during tuning (default: 20).

tune_iter

Number of iterations for Bayesian optimization (default: 30).

feature_engineering

Logical. Apply automatic feature engineering: detection of suspicious zeros, log-transformation of skewed variables, missing-value indicators and interactions between the top variables (default: FALSE).

custom_features

Optional named list to create domain variables. Each element name is the new variable and its value is an R expression given as a character string, e.g. list(family = "SibSp + Parch", fare_pp = "Fare / (SibSp + Parch + 1)") (default: NULL).

feature_selection

Logical. Perform variable selection with cross-validation (default: FALSE).

balance_classes

Logical. For classification, balance the classes of the training set (default: FALSE).

balance_method

Balancing method: "smote" (default), "adasyn", "rose", "up" or "down".

impute

Logical. Impute missing values (default: TRUE).

impute_method

Imputation method: "knn" (default), "median" or "mean".

normalize

Logical. Normalize numeric predictors (default: TRUE).

normalize_method

Normalization method: "zscore" (default, standardization) or "minmax" (rescaling between 0 and 1).

use_pca

Logical. Apply principal component analysis after normalization (default: FALSE).

pca_threshold

Proportion of variance retained by PCA (default: 0.95).

treat_outliers

Logical. Treat outliers by winsorization (default: TRUE).

outlier_percentile

Percentile used for winsorization (default: 0.05).

remove_high_cor

Logical. Remove highly correlated predictors (default: TRUE).

cor_threshold

Correlation threshold for removal (default: 0.90).

remove_high_vif

Logical. Remove predictors with a high variance inflation factor (default: TRUE).

vif_threshold

VIF threshold for removal (default: 5).

run_eda

Logical. Run the exploratory data analysis (default: TRUE).

run_shap

Logical. Compute variable importance and SHAP values (default: TRUE).

n_shap

Number of observations used to compute SHAP values (default: 100).

optimize_threshold

Logical. Optimize the decision threshold in binary classification (default: TRUE).

threshold_method

Threshold optimization criterion: "youden" (default), "f1" or "balanced".

calibrate_probs

Logical. Calibrate predicted probabilities in binary classification (default: FALSE).

calibration_method

Calibration method: "platt" (default) or "isotonic".

check_leakage

Logical. Check for possible data leakage after fitting (default: TRUE).

nested_cv

Logical. Run nested cross-validation for a less biased performance estimate; only used when the best model is "rf" or "xgboost" (default: FALSE).

analyze_interactions

Logical. Analyze interactions and non-linear effects with splines (default: TRUE).

protected_var

Optional name of a protected variable used in the fairness analysis, e.g. "sex", "race" or "age_group" (default: NULL).

run_fairness

Logical. Run the fairness analysis (default: FALSE). Requires protected_var. Computes demographic parity, equalized odds, predictive parity and the disparate impact ratio (four-fifths rule). Classification only.

fairness_threshold

Threshold of the four-fifths rule for the disparate impact ratio (default: 0.8).

fairness_correction

Bias mitigation strategy for classification: "none" (default, no correction), "postprocesamiento" (post-processing: group-specific decision thresholds, stored in group_thresholds and used by predict.supervisedml()) or "preprocesamiento" (pre-processing: the training set is resampled by protected group and outcome before fitting). Any value other than "none" requires protected_var and switches run_fairness on; it is ignored for regression.

run_dca

Logical. Run decision curve analysis of clinical utility, classification only: net benefit compared with treating all or none (default: FALSE).

dca_thresholds

Numeric vector of probability thresholds for the decision curve analysis (default: seq(0.01, 0.99, by = 0.01)).

n_cores

Number of cores for parallel processing (default: 2). Use 1 for sequential processing. If NULL, all available cores minus one are used. Parallel processing requires the packages doParallel and foreach; if they are not installed, the analysis runs sequentially.

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print the progress and the interpretation of each step in the console (default: TRUE).

Details

The data are split into a training and a test set (test_split). All models listed in models are fitted with cv_folds-fold cross-validation on the training set and compared with select_metric. The best model is optionally tuned (tune_best), refitted on the whole training set and evaluated on the test set. For binary classification the decision threshold is chosen with out-of-fold training predictions and evaluated on the test set. When verbose = TRUE the console output (in Spanish) is shown in real time and also stored in the returned object (verbose_text, verbose_lines).

Value

An object of class supervisedml (a list). Main elements:

task: Type of task: "classification" or "regression".
target: Name of the outcome variable.
best_model: Name of the selected model.
best_params: Tuned hyperparameters of the best model (NULL if not tuned).
final_fit: Final fitted model (a tidymodels workflow).
test_metrics: Tibble with the metrics on the test set (.metric, .estimator, .estimate).
predictions: Tibble with the predictions on the test set.
cv_summary: Cross-validation summary of all trained models.
importance: Data frame with variable importance (Variable, Importance).
train_data, test_data: Preprocessed training and test sets.
eda, preprocessing, modeling, tuning, evaluation: Detailed results of each step.
interpretation: Importance and SHAP results (if run_shap = TRUE).
threshold_optimization, optimal_threshold: Threshold optimization (binary classification).
leakage_check, interactions_analysis, calibration, residual_analysis, nested_cv: Advanced analyses, when run.
fairness_analysis, fairness_correction, group_thresholds: Fairness results (if requested).
dca: Decision curve analysis (if run_dca = TRUE).
plots, figures: Lists of ggplot2 figures.
figures_catalog: Catalog of figures with metadata for reports.
verbose_text, verbose_lines: Captured console output (only when verbose = TRUE).
metadata: List with analysis information (sample sizes, models, folds, metric, seed, elapsed time, date).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "glm", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
print(res)
summary(res)

# Predictions for new observations
new_df <- data.frame(x1 = rnorm(5), x2 = rnorm(5), x3 = rnorm(5))
predict(res, new_df)
predict(res, new_df, type = "prob")

plot(res, type = "roc")

# Two models, tuning and SHAP values (slower)
res2 <- supervised_ml(df, target = "y", models = c("glm", "rf"),
                      cv_folds = 3, tune_grid = 3, n_shap = 20,
                      n_cores = 1, seed = 123, verbose = FALSE)
res2$best_model
plot(res2, type = "shap_bar")
if (requireNamespace("patchwork", quietly = TRUE)) plot(res2)

Run supervised_ml() and Capture Its Console Output

Description

Runs supervised_ml() with verbose = TRUE, capturing all the console output so that it can be exported with export_verbose_txt() or export_verbose_json(). The captured output is not shown in the console.

Usage

supervised_ml_capture(...)

Arguments

...

Arguments passed to supervised_ml() (do not pass verbose, it is always set to TRUE).

Value

The supervisedml object returned by supervised_ml() with three additional elements: verbose_text (the captured output as a single string), verbose_lines (character vector, one element per line) and verbose_json (list with the output parsed into sections).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml_capture(df, target = "y", models = "glm", cv_folds = 3,
                             tune_best = FALSE, run_shap = FALSE,
                             n_cores = 1)
cat(head(res$verbose_lines, 10), sep = "\n")

Run and Export a Complete Analysis

Description

Convenience function that runs supervised_ml() through supervised_ml_capture() and exports both a TXT and a JSON file to output_dir. The file names are <prefix>_<timestamp>.txt and <prefix>_<timestamp>.json.

Usage

supervised_ml_export(..., output_dir, prefix = "supervisedml")

Arguments

...

Arguments passed to supervised_ml() (do not pass verbose).

output_dir

Output directory (required, no default). It is created if it does not exist.

prefix

Prefix of the file names (default "supervisedml").

Value

Invisibly returns the result of supervised_ml_capture() (a supervisedml object with the captured console output).

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
out_dir <- file.path(tempdir(), "psyml_export")
res <- supervised_ml_export(df, target = "y", models = "glm", cv_folds = 3,
                            tune_best = FALSE, run_shap = FALSE,
                            n_cores = 1, output_dir = out_dir)
list.files(out_dir)
unlink(out_dir, recursive = TRUE)

Train and Compare Models with Cross-Validation

Description

Modeling step of the pipeline: creates the cross-validation folds (setup_cv()), defines the model specifications (define_models()), fits every model with cross-validation (fit_models_cv()) and compares them to select the best one (compare_models()).

Usage

train_models(
  preprocess_result,
  models = c("rf", "xgboost", "glm"),
  cv_folds = 10,
  select_metric = NULL,
  seed = NULL,
  verbose = TRUE
)

Arguments

preprocess_result

Result of preprocess_data(). It must contain task, target, train_data and recipe.

models

Character vector with the models to train (default: c("rf", "xgboost", "glm")). Available: "rf" (random forest), "xgboost", "svm" (RBF kernel), "nnet" (neural network), "glm" (logistic or linear regression), "glmnet" (regularized regression), "tree" (decision tree) and "nb" (naive Bayes). Some models need suggested packages (e.g. kernlab, glmnet, rpart).

cv_folds

Number of cross-validation folds (default: 10).

select_metric

Metric used to select the best model. If NULL (default) ROC-AUC is used for classification and R-squared for regression. See supervised_ml() for the available metrics.

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print the progress and an explanation of each step in the console (default: TRUE).

Value

An object of class supervisedml_modeling (a list) with cv_folds (the rset of folds), model_specs (named list of parsnip specifications), cv_results (named list of resampling results), comparison (result of compare_models()), best_model (name of the best model), task, target, recipe and train_data.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
pp <- preprocess_data(df, target = "y", task = "classification",
                      verbose = FALSE)
mod <- train_models(pp, models = c("glm", "rf"), cv_folds = 3,
                    verbose = FALSE)
mod$best_model
mod$comparison$summary

Tune the Hyperparameters of the Best Model

Description

Tunes the hyperparameters of the best model selected by train_models() with cross-validation on the training folds, and returns the finalized workflow with the best combination. Tunable models are "rf", "xgboost", "svm", "nnet", "glmnet" and "tree"; for other models (e.g. "glm") tuning is skipped and the untuned workflow is returned.

Usage

tune_best_model(
  modeling_result,
  method = "random",
  grid_size = 20,
  iter = 30,
  select_metric = NULL,
  seed = NULL,
  verbose = TRUE
)

Arguments

modeling_result

Result of train_models() (or the modeling element of a supervised_ml() result). It must contain best_model, task, recipe and cv_folds.

method

Search method: "random" (random search, default), "grid" (grid search), "bayes" (Bayesian optimization) or "racing" (racing with ANOVA; requires finetune, otherwise random search is used with a warning).

grid_size

Number of hyperparameter combinations evaluated with the grid, random and racing methods (default: 20).

iter

Maximum number of iterations of Bayesian optimization (default: 30).

select_metric

Metric used to select the best combination. If NULL (default) ROC-AUC is used for classification and R-squared for regression (also when "rank_mean" is given).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical. Print the progress and the interpretation of the best hyperparameters (default: TRUE).

Value

A list (class supervisedml_tuning when tuning was performed) with original_model (name of the model), tuned (logical), best_params (named list with the best hyperparameter values, NULL if not tuned), final_workflow (finalized, unfitted tidymodels workflow) and, when tuned, tune_results (the tune_results object), task and method.

Examples


set.seed(1)
n <- 200
df <- data.frame(x1 = rnorm(n), x2 = rnorm(n), x3 = rnorm(n))
df$y <- factor(ifelse(df$x1 + 0.5 * df$x2 + rnorm(n) > 0, "yes", "no"))
res <- supervised_ml(df, target = "y", models = "rf", cv_folds = 3,
                     tune_best = FALSE, run_shap = FALSE, n_cores = 1,
                     verbose = FALSE)
tuned <- tune_best_model(res$modeling, method = "random", grid_size = 3,
                         select_metric = "roc_auc", seed = 1,
                         verbose = FALSE)
tuned$best_params

Automated Unsupervised Learning Pipeline

Description

Runs a complete unsupervised learning pipeline: exploratory data analysis, preprocessing, dimensionality reduction, clustering, cluster evaluation, anomaly detection and visualization.

Usage

unsupervised_ml(
  data,
  methods = c("clustering", "reduction", "anomaly"),
  clustering_algorithms = c("kmeans", "hierarchical", "dbscan", "gmm"),
  k = NULL,
  k_range = 2:10,
  distance = c("euclidean", "manhattan", "gower"),
  hclust_method = c("ward.D2", "complete", "average", "single"),
  dbscan_eps = NULL,
  dbscan_minPts = 5,
  gmm_type = "VVV",
  lca_nclass = NULL,
  lca_nrep = 10,
  reduction_methods = c("pca", "tsne", "umap"),
  n_components = 2,
  pca_threshold = 0.95,
  tsne_perplexity = 30,
  umap_n_neighbors = 15,
  umap_min_dist = 0.1,
  anomaly_methods = c("isolation_forest", "lof", "mahalanobis"),
  contamination = 0.05,
  lof_k = 20,
  exclude_cols = NULL,
  impute = TRUE,
  impute_method = c("median", "knn", "mean"),
  normalize = TRUE,
  normalize_method = c("zscore", "minmax"),
  treat_outliers = FALSE,
  outlier_percentile = 0.05,
  remove_high_cor = TRUE,
  cor_threshold = 0.9,
  run_eda = TRUE,
  seed = NULL,
  verbose = TRUE
)

Arguments

data

A data frame with the data.

methods

Character vector with the analyses to run: any of "clustering", "reduction" and "anomaly" (default: all three).

clustering_algorithms

Clustering algorithms: any of "kmeans", "hierarchical", "dbscan", "gmm" and "lca" (default: all except "lca"). LCA (latent class analysis) requires categorical or binary data.

k

Number of clusters. If NULL (default), the optimal number is searched automatically.

k_range

Range of k values evaluated when k = NULL (default: 2:10).

distance

Distance metric: "euclidean" (default), "manhattan" or "gower".

hclust_method

Linkage method for hierarchical clustering: "ward.D2" (default), "complete", "average" or "single".

dbscan_eps

Epsilon radius for DBSCAN. If NULL (default), it is computed automatically.

dbscan_minPts

Minimum number of points for DBSCAN (default: 5).

gmm_type

Covariance model for GMM, passed to 'mclust' (default: "VVV").

lca_nclass

Number of latent classes for LCA. If NULL (default), the number of classes is searched automatically.

lca_nrep

Number of LCA repetitions to avoid local maxima (default: 10).

reduction_methods

Dimensionality reduction methods: any of "pca", "tsne" and "umap" (default: all three).

n_components

Number of target dimensions for t-SNE and UMAP (default: 2).

pca_threshold

Cumulative proportion of variance retained by PCA (default: 0.95).

tsne_perplexity

Perplexity for t-SNE (default: 30).

umap_n_neighbors

Number of neighbours for UMAP (default: 15).

umap_min_dist

Minimum distance for UMAP (default: 0.1).

anomaly_methods

Anomaly detection methods: any of "isolation_forest", "lof" and "mahalanobis" (default: all three).

contamination

Expected proportion of anomalies (default: 0.05).

lof_k

Number of neighbours for LOF (default: 20).

exclude_cols

Character vector of columns to exclude from the analysis (IDs, text, etc.).

impute

Logical; impute missing values (default: TRUE).

impute_method

Imputation method: "median" (default), "knn" or "mean".

normalize

Logical; normalize the numeric variables (default: TRUE).

normalize_method

Normalization method: "zscore" (default) or "minmax".

treat_outliers

Logical; winsorize outliers before the analysis (default: FALSE).

outlier_percentile

Percentile used for winsorization (default: 0.05).

remove_high_cor

Logical; remove highly correlated variables (default: TRUE).

cor_threshold

Correlation threshold used by remove_high_cor (default: 0.90).

run_eda

Logical; run the exploratory data analysis (default: TRUE).

seed

Optional integer seed. If NULL (default) the random number generator state is not modified.

verbose

Logical; print progress to the console (default: TRUE).

Details

Only numeric columns are analysed. Several algorithms rely on suggested packages and are skipped (with a note in the verbose output) when they are not installed: 'dbscan' (DBSCAN and LOF), 'mclust' (GMM), 'poLCA' (LCA), 'Rtsne' (t-SNE), 'umap' (UMAP), 'isotree' (isolation forest), 'cluster' (silhouette, gap statistic and Gower distance), 'ggdendro' (dendrogram) and 'VIM' (kNN imputation).

Value

An object of class unsupervisedml (a list) with the components:

call: The matched call of the internal pipeline.
params: List with the main analysis parameters.
eda: Exploratory analysis: structure, missing values, descriptive statistics, outliers, correlations and normality (only if run_eda = TRUE).
preprocessed: List with the preprocessed data (data_clean), the data before normalization (data_before_norm), the normalization parameters (norm_params), the removed columns (removed_cols) and the number of variables before and after preprocessing.
reduction: Dimensionality reduction results (pca, tsne, umap).
clustering: Clustering results: optimal_k, k_analysis and results, a list with one element per algorithm containing the cluster labels and the fitted model.
evaluation: Cluster evaluation: comparison (data frame with the silhouette, Calinski-Harabasz and Davies-Bouldin indices of each algorithm) and best_algorithm.
anomalies: Anomaly detection results per method (results) and the majority consensus.
figures: Named list of 'ggplot2' plots.
elapsed_time: Execution time in seconds.
data_original: The input data.
verbose_text, verbose_lines: Captured console output (only if verbose = TRUE).

Examples

# Two well separated groups
set.seed(1)
X <- data.frame(a = c(rnorm(40), rnorm(40, 3)),
                b = c(rnorm(40), rnorm(40, 3)),
                c = rnorm(80))
res <- unsupervised_ml(X,
                       clustering_algorithms = c("kmeans", "hierarchical"),
                       k_range = 2:4, reduction_methods = "pca",
                       anomaly_methods = "mahalanobis",
                       run_eda = FALSE, seed = 1, verbose = FALSE)
res$evaluation$comparison

Package {PsyML}

PsyML: Machine Learning Workflows for Supervised, Unsupervised, and Causal Analysis

Description

Supervised learning (predicting a known outcome)

Unsupervised learning (finding structure without an outcome)

Causal inference (estimating the effect of a treatment)

Tools shared by the three approaches

Author(s)

See Also

Advanced Residual Analysis

Description

Usage

Arguments

Value

See Also

Examples

Clinical Utility Analysis (Decision Curve Analysis)

Description

Usage

Arguments

Value

References

See Also

Examples

Fairness Analysis of a Classification Model

Description

Usage

Arguments

Value

See Also

Examples

Analyze Interactions and Nonlinear Effects

Description

Usage

Arguments

Value

See Also

Examples

Domain Feature Engineering

Description

Usage

Arguments

Value

See Also

Examples

Apply a Classification Threshold

Description

Usage

Arguments

Value

See Also

Examples

Automatic Feature Engineering

Description

Usage

Arguments

Value

See Also

Examples

Block-Based Train/Test Split for Enhanced Generalization

Description

Usage

Arguments

Details

Value

References

See Also

Examples

SHAP Values of a Fitted Workflow

Description

Usage

Arguments

Value

See Also

Examples

Calibrate Predicted Probabilities

Description

Usage

Arguments

Value