Arthritis survey data

Description

A classification dataset on arthritis status and related demographic and behavioral covariates.

Usage

arthritis

Format

A data frame with 4,856 rows and 12 variables:

id

Participant identifier.

status

Arthritis status ("Yes" or "No").

heart_attack_relative

Whether a relative had a heart attack.

gender

Participant gender.

age

Participant age in years.

bmi

Body mass index.

diabetes

Whether the participant has diabetes.

alcohol

Whether the participant reports alcohol use.

smoke

Whether the participant smokes.

prehypertension

Whether the participant has prehypertension.

vegetarian

Whether the participant follows a vegetarian diet.

covered_health

Whether the participant has health coverage.

Details

Column names were standardized to snake_case when packaging the data.

Source

Original arthritis survey dataset distributed with the project materials.

Examples

str(funcml::arthritis)
table(funcml::arthritis$status)

Bangladesh maternal risk data

Description

A classification dataset for maternal health risk level with vital signs, diabetes history, and related clinical indicators.

Usage

bangladeshmaternalrisk

Format

A data frame with 1,205 rows and 12 variables:

age

Maternal age in years.

systolic_bp

Systolic blood pressure.

diastolic

Diastolic blood pressure.

bs

Blood sugar measurement.

body_temp

Body temperature.

bmi

Body mass index.

previous_complications

Indicator for previous pregnancy complications.

preexisting_diabetes

Indicator for preexisting diabetes.

gestational_diabetes

Indicator for gestational diabetes.

mental_health

Indicator for mental health concerns.

heart_rate

Heart rate.

risk_level

Maternal risk level outcome.

Details

Column names were standardized to snake_case when packaging the data.

Source

Mojumdar MU, Sarker D, Assaduzzaman M, et al. (2025). Maternal health risk factors dataset: Clinical parameters and insights from rural Bangladesh. Data in Brief, 59(Suppl 2), 111363. doi:10.1016/j.dib.2025.111363.

Examples

str(funcml::bangladeshmaternalrisk)
table(funcml::bangladeshmaternalrisk$risk_level)

Birth weight data

Description

A regression-oriented birth weight dataset with maternal risk factors and a derived low-birth-weight indicator.

Usage

birthweight

Format

A data frame with 189 rows and 10 variables:

age

Maternal age in years.

lwt

Maternal weight at the last menstrual period.

race

Maternal race code.

smoke

Smoking status indicator.

ptl

Number of previous premature labors.

ht

History of hypertension indicator.

ui

Presence of uterine irritability indicator.

ftv

Number of physician visits in the first trimester.

birth_weight_g

Birth weight in grams.

low_birth_weight

Low-birth-weight outcome indicator.

Source

Hosmer DW, Lemeshow S (1989). Applied Logistic Regression. Wiley. The packaged data are a lightly renamed version of the classic MASS::birthwt dataset.

Examples

str(funcml::birthweight)
summary(funcml::birthweight$birth_weight_g)

Breast cancer diagnostic data

Description

A binary classification dataset for breast cancer diagnosis using tumor morphology measurements.

Usage

breastcancerdiagnostic

Format

A data frame with 569 rows and 31 variables:

radius_mean

Mean radius.

texture_mean

Mean texture.

perimeter_mean

Mean perimeter.

area_mean

Mean area.

smoothness_mean

Mean smoothness.

compactness_mean

Mean compactness.

concavity_mean

Mean concavity.

concave_pts_mean

Mean number of concave points.

symmetry_mean

Mean symmetry.

fractal_dim_mean

Mean fractal dimension.

radius_se

Radius standard error.

texture_se

Texture standard error.

perimeter_se

Perimeter standard error.

area_se

Area standard error.

smoothness_se

Smoothness standard error.

compactness_se

Compactness standard error.

concavity_se

Concavity standard error.

concave_pts_se

Concave points standard error.

symmetry_se

Symmetry standard error.

fractal_dim_se

Fractal dimension standard error.

radius_worst

Worst radius.

texture_worst

Worst texture.

perimeter_worst

Worst perimeter.

area_worst

Worst area.

smoothness_worst

Worst smoothness.

compactness_worst

Worst compactness.

concavity_worst

Worst concavity.

concave_pts_worst

Worst number of concave points.

symmetry_worst

Worst symmetry.

fractal_dim_worst

Worst fractal dimension.

diagnosis

Diagnosis outcome ("B" = benign, "M" = malignant).

Details

Column names were standardized to snake_case when packaging the data.

Source

Breast Cancer Wisconsin Diagnostic Dataset from the UCI Machine Learning Repository, packaged in dslabs::brca.

Examples

str(funcml::breastcancerdiagnostic)
table(funcml::breastcancerdiagnostic$diagnosis)

Wisconsin breast cancer data

Description

A binary classification dataset for breast cancer diagnosis from cytology measurements.

Usage

breastcancerwisconsin

Format

A data frame with 699 rows and 10 variables:

clump_thickness

Clump thickness score.

uniformity_cell_size

Uniformity of cell size score.

uniformity_cell_shape

Uniformity of cell shape score.

marginal_adhesion

Marginal adhesion score.

single_epithelial_cell_size

Single epithelial cell size score.

bare_nuclei

Bare nuclei score.

bland_chromatin

Bland chromatin score.

normal_nucleoli

Normal nucleoli score.

mitoses

Mitoses score.

class

Diagnostic class (2 = benign, 4 = malignant).

Source

Wisconsin Breast Cancer Database from University of Wisconsin Hospitals, distributed through the UCI Machine Learning Repository.

Examples

str(funcml::breastcancerwisconsin)
table(funcml::breastcancerwisconsin$class)

Cancer remission data

Description

A binary classification dataset on cancer remission status using leukemia index and treatment group indicators.

Usage

cancerremission

Format

A data frame with 27 rows and 3 variables:

li

Leukemia index measurement.

m

Treatment group indicator.

remission

Remission outcome indicator (0 = no remission, 1 = remission).

Details

Column names were standardized to snake_case when packaging the data.

Source

Davison AC, Hinkley DV (1997). Bootstrap Methods and Their Application. Cambridge University Press. The packaged data are from boot::remission.

Examples

str(funcml::cancerremission)
table(funcml::cancerremission$remission)

CD4 follow-up data

Description

A regression dataset relating baseline CD4 counts to one-year follow-up CD4 measurements in HIV-positive patients.

Usage

cd4counts

Format

A data frame with 20 rows and 2 variables:

baseline

Baseline CD4 count.

oneyear

One-year follow-up CD4 count.

Source

Davison AC, Hinkley DV (1997). Bootstrap Methods and Their Application. Cambridge University Press. The packaged data are from boot::cd4.

Examples

str(funcml::cd4counts)
summary(funcml::cd4counts$oneyear)

Youth tobacco survey data

Description

A classification-oriented survey dataset on smoking exposure, tobacco use, and tobacco-related environments among youth respondents.

Usage

cigsmoke

Format

A data frame with 3,915 rows and 27 variables:

final_wgt

Survey final weight.

age

Age group.

gender

Gender.

income

Personal spending money category.

parent_work

Parental work status.

father_education

Father's education level.

mother_education

Mother's education level.

living_env

Living environment.

age_first_cig

Age at first cigarette.

cigar_use

Cigar use indicator.

noncig_use

Non-cigarette tobacco use indicator.

smokeless_use

Smokeless tobacco use indicator.

parent_smoke

Parental smoking exposure.

friends_smoke

Friends' smoking exposure.

home_shs

Secondhand smoke exposure at home.

outside_shs

Secondhand smoke exposure outside the home.

indoor_ban

Indoor smoking ban indicator.

outdoor_ban

Outdoor smoking ban indicator.

antitobacco_media

Exposure to antitobacco media.

antitobacco_school

Exposure to school antitobacco education.

tobacco_media

Exposure to tobacco media.

offer_freetobacco

Whether free tobacco was offered.

own_items

Ownership of tobacco-branded items.

knowledge_harm

Knowledge that tobacco is harmful.

e_cig

Electronic cigarette use indicator.

stratum

Survey stratum identifier.

psu

Primary sampling unit identifier.

Details

Column names were standardized to snake_case when packaging the data.

Source

Morocco Global Youth Tobacco Survey public-use survey data.

References

Kim N, Loh WY, McCarthy DE (2021). Machine learning models of tobacco susceptibility and current use among adolescents from 97 countries in the Global Youth Tobacco Survey, 2013-2017. PLOS Global Public Health, 1(12), e0000060. doi:10.1371/journal.pgph.0000060.

Examples

str(funcml::cigsmoke)
table(funcml::cigsmoke$e_cig)

Methods for learner comparison results.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_compare objects.

Usage

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

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

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

Arguments

Value

print() and summary() return the input object or results table invisibly. plot() returns a ggplot2 object.

Examples

cmp <- compare_learners(
  data = mtcars,
  formula = mpg ~ wt + hp,
  models = c("glm", "rpart"),
  resampling = cv(3, seed = 1),
  metrics = c("rmse", "mae")
)
print(cmp)
summary(cmp)
plot(cmp)

Compare multiple learners with optional tuning.

Description

Compare multiple learners with optional tuning.

Usage

compare_learners(
  data,
  formula,
  models,
  specs = NULL,
  resampling = cv(5),
  metrics = NULL,
  type = NULL,
  conf_level = 0.95,
  seed = NULL,
  ncores = NULL,
  tune = FALSE,
  grids = NULL,
  metric = NULL,
  ...
)

Arguments

Value

A funcml_compare object.

Examples

cmp <- compare_learners(
  data = mtcars,
  formula = mpg ~ wt + hp,
  models = c("glm", "rpart"),
  resampling = cv(3, seed = 1),
  metrics = c("rmse", "mae")
)
cmp$results

Resampling specification generator.

Description

Resampling specification generator.

Usage

cv(
  v = 5,
  repeats = 1,
  strata = TRUE,
  seed = NULL,
  method = c("vfold", "holdout", "group_vfold", "time"),
  prop = 0.8,
  group = NULL,
  time = NULL,
  initial = NULL,
  assess = NULL,
  skip = 0,
  cumulative = TRUE
)

Arguments

Value

A funcml_cv object containing fold indices and parameters.

Examples

cv(v = 3, repeats = 2, seed = 1)

Doctor visits data

Description

A regression dataset on annual doctor visit counts and related health, demographic, and insurance covariates.

Usage

doctorvisits

Format

A data frame with 5,190 rows and 12 variables:

visits

Number of doctor visits.

gender

Recorded gender.

age

Age in years scaled to decades.

income

Income measure scaled by household composition.

illness

Number of illnesses in the previous two weeks.

reduced

Number of days with reduced activity.

health

Self-rated health score.

private

Private insurance indicator.

freepoor

Free care indicator for low-income patients.

freerepat

Free care indicator for pensioners or veterans.

nchronic

Indicator for no chronic condition.

lchronic

Indicator for limiting chronic condition.

Source

Cameron AC, Trivedi PK (1998). Regression Analysis of Count Data. Cambridge University Press. The packaged data are from AER::DoctorVisits.

Examples

str(funcml::doctorvisits)
summary(funcml::doctorvisits$visits)

Causal effect estimation via plug-in g-computation.

Description

Causal effect estimation via plug-in g-computation.

Usage

estimate(
  data,
  formula,
  model = NULL,
  treatment = NULL,
  estimand = c("ATE", "ATT", "CATE", "IATE"),
  newdata = NULL,
  treatment_level = NULL,
  control_level = NULL,
  spec = NULL,
  type = NULL,
  interval = c("normal", "bootstrap"),
  conf_level = 0.95,
  n_boot = 200,
  seed = NULL,
  fit = NULL,
  ...
)

Arguments

Value

A funcml_estimand object.

Examples

causal_data <- mtcars
causal_data$am <- factor(causal_data$am, labels = c("auto", "manual"))
ate <- estimate(
  data = causal_data,
  formula = mpg ~ am + wt + hp,
  model = "glm",
  treatment = "am",
  estimand = "ATE"
)
ate$estimate

Methods for causal estimand results.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_estimand objects.

Usage

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

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

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

Arguments

Value

print() and summary() return the input object or summary table invisibly. plot() returns a ggplot2 object.

Examples

causal_data <- mtcars
causal_data$am <- factor(causal_data$am, labels = c("auto", "manual"))
ate <- estimate(
  data = causal_data,
  formula = mpg ~ am + wt + hp,
  model = "glm",
  treatment = "am",
  estimand = "ATE"
)
print(ate)
summary(ate)
plot(ate)

Cross-validated evaluation.

Description

Cross-validated evaluation.

Usage

evaluate(
  data,
  formula,
  model = NULL,
  spec = NULL,
  resampling = cv(5),
  metrics = NULL,
  type = NULL,
  conf_level = 0.95,
  seed = NULL,
  fit = NULL,
  ncores = NULL,
  ...
)

Arguments

Value

A funcml_eval object.

Examples

eval_obj <- evaluate(
  data = mtcars,
  formula = mpg ~ wt + hp,
  model = "glm",
  resampling = cv(3, seed = 1),
  metrics = c("rmse", "mae")
)
eval_obj$summary

Methods for cross-validation results.

Description

These methods provide the standard print() and summary() interfaces for funcml_eval objects, plus a plot() method for fold-level diagnostics.

Usage

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

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

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

Arguments

Value

print() and summary() return the input object or summary table invisibly. plot() returns a ggplot2 object.

Examples

eval_obj <- evaluate(
  data = mtcars,
  formula = mpg ~ wt + hp,
  model = "glm",
  resampling = cv(3, seed = 1),
  metrics = c("rmse", "mae")
)
print(eval_obj)
summary(eval_obj)
plot(eval_obj)

Fit a model using the funcml interface.

Description

Registered learner ids currently include: regression and classification: glm, rpart, glmnet, ranger, nnet, e1071_svm, randomForest, gbm, kknn, ctree, cforest, lightgbm, xgboost, stacking, superlearner; regression plus binary classification: gam, bart; classification only: C50, naivebayes, fda, lda, qda; binary classification only: adaboost; regression plus binary classification: earth; regression only: pls.

Usage

fit(
  formula,
  data,
  model,
  spec = NULL,
  seed = NULL,
  na_action = stats::na.fail,
  ...
)

Arguments

Details

The learner engine packages are installed with funcml, so the advertised registry is intended to be available after a standard installation.

Value

An object of class funcml_fit.

Examples

fit_obj <- fit(mpg ~ wt + hp, data = mtcars, model = "glm")
predict(fit_obj, newdata = mtcars[1:3, , drop = FALSE])

Methods for fitted funcml models.

Description

These methods provide the standard print(), summary(), predict(), and coef() interfaces for funcml_fit objects.

Usage

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

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

## S3 method for class 'funcml_fit'
predict(
  object,
  newdata,
  type = NULL,
  class_level = NULL,
  pos_level = NULL,
  na_action = object$na_action,
  ...
)

## S3 method for class 'funcml_fit'
coef(object, ...)

Arguments

Value

print() and summary() return the input object invisibly. predict() returns predictions in the requested format. coef() returns a named numeric coefficient vector when available.

Examples

fit_obj <- fit(mpg ~ wt + hp, data = mtcars, model = "glm")
print(fit_obj)
summary(fit_obj)
predict(fit_obj, newdata = mtcars[1:3, , drop = FALSE])
coef(fit_obj)

Grouped cross-validation.

Description

Grouped cross-validation.

Usage

group_cv(v = 5, group, repeats = 1, seed = NULL)

Arguments

Value

A funcml_cv object.

Examples

group_cv(v = 3, group = rep(letters[1:3], each = 4), seed = 1)

Haberman survival data

Description

A binary classification dataset on breast cancer survival after surgery.

Usage

haberman

Format

A data frame with 306 rows and 4 variables:

age

Age of patient at operation time in years.

operation_year

Year of operation minus 1900.

positive_axillary_nodes

Number of positive axillary nodes detected.

survival_status

Survival status (1 = survived 5 years or longer, 2 = died within 5 years).

Source

Haberman's Survival Data from the University of Chicago's Billings Hospital study, distributed through the UCI Machine Learning Repository.

Examples

str(funcml::haberman)
table(funcml::haberman$survival_status)

Heart disease patient data

Description

A binary classification dataset on heart disease status using demographic and clinical risk factors.

Usage

heartdisease

Format

A data frame with 303 rows and 9 variables:

age

Age in years.

sex

Recorded sex.

chest_pain

Chest pain type.

bp

Resting blood pressure.

cholesterol

Serum cholesterol measurement.

blood_sugar

High fasting blood sugar indicator.

maximum_hr

Maximum heart rate achieved.

exercise_induced_angina

Exercise-induced angina indicator.

heart_disease

Heart disease outcome ("Yes" or "No").

Details

Column names were standardized to snake_case when packaging the data.

Source

CardioDataSets package dataset CardioDataSets::heartdisease_tbl_df.

Examples

str(funcml::heartdisease)
table(funcml::heartdisease$heart_disease)

Heart failure data

Description

A binary classification dataset on heart failure mortality using demographic, laboratory, and clinical covariates.

Usage

heartfailure

Format

A data frame with 299 rows and 13 variables:

age

Age in years.

anaemia

Anaemia indicator.

creatinine_phosphokinase

Creatinine phosphokinase level.

diabetes

Diabetes indicator.

ejection_fraction

Ejection fraction percentage.

high_blood_pressure

High blood pressure indicator.

platelets

Platelet count.

serum_creatinine

Serum creatinine level.

serum_sodium

Serum sodium level.

sex

Sex indicator.

smoking

Smoking indicator.

time

Follow-up time.

death_event

Death event outcome indicator (0 = no event, 1 = death).

Details

Column names were standardized to snake_case when packaging the data.

Source

CardioDataSets package dataset CardioDataSets::cardiac_failure_df.

Examples

str(funcml::heartfailure)
table(funcml::heartfailure$death_event)

Plain holdout resampling.

Description

Plain holdout resampling.

Usage

holdout(prop = 0.8, strata = TRUE, seed = NULL)

Arguments

Value

A funcml_cv object.

Examples

holdout(prop = 0.75, seed = 1)

Infant mortality data

Description

A regression dataset on infant mortality with country-level income, region, and oil-export status covariates.

Usage

infantmortality

Format

A data frame with 105 rows and 5 variables:

country

Country name.

income

Per-capita income.

infant

Infant mortality rate.

region

Geographic region.

oil

Oil-exporting country indicator.

Details

Column names were standardized to snake_case when packaging the data.

Source

Fox J, Weisberg S (2019). An R Companion to Applied Regression. Sage. The packaged data are from carData::Leinhardt.

Examples

str(funcml::infantmortality)
summary(funcml::infantmortality$infant)

Model-agnostic interpretation (global + local).

Description

Implements native permutation VI, PDP/ICE/ALE, SHAP approximations, local surrogate explanations, interaction strength, and global surrogate models.

Usage

interpret(
  fit,
  data,
  formula = fit$formula,
  method = c("vip", "permute", "pdp", "ice", "ale", "local", "lime", "shap",
    "local_model", "interaction", "surrogate", "profile", "ceteris_paribus",
    "calibration"),
  features = NULL,
  type = NULL,
  metric = NULL,
  importance_type = c("permute", "model", "auto"),
  compare = c("difference", "ratio"),
  keep = TRUE,
  k = NULL,
  gower_power = NULL,
  class_level = NULL,
  pos_level = NULL,
  newdata = NULL,
  nsim = NULL,
  nsamples = NULL,
  grid = NULL,
  seed = NULL,
  bins = 10,
  strategy = c("quantile", "uniform"),
  ...
)

Arguments

Value

An interpretation object whose class depends on method. Returned objects contain computed explanation values and metadata used for printing, summarizing, and plotting.

Examples

fit_obj <- fit(
  mpg ~ wt + hp + disp,
  data = mtcars,
  model = "rpart",
  spec = list(cp = 0.01, minsplit = 5)
)
vi <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "permute",
  features = c("wt", "hp"),
  nsim = 2,
  metric = "rmse"
)
vi$result$scores

ALE result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_ale objects returned by interpret(method = "ale").

Usage

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

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns the ALE curve table invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
ale <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "ale",
  features = c("wt", "hp"),
  nsamples = 20
)
print(ale)
summary(ale)
plot(ale)

Calibration result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_calibration objects returned by interpret(method = "calibration").

Usage

## S3 method for class 'funcml_calibration'
plot(x, style = c("curve", "histogram"), ...)

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns a list with calibration curve and summary diagnostics invisibly.

Examples

fit_obj <- fit(mpg > 20 ~ wt + hp + disp, data = mtcars, model = "glm")
cal <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "calibration"
)
print(cal)
summary(cal)
plot(cal)

ICE result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_ice objects returned by interpret(method = "ice") or interpret(method = "ceteris_paribus").

Usage

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

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns the ICE curve table invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
ice <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "ice",
  features = c("wt", "hp"),
  nsamples = 20
)
print(ice)
summary(ice)
plot(ice)

Interaction result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_interaction objects returned by interpret(method = "interaction").

Usage

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

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns the interaction summary table invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
interaction_obj <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "interaction",
  features = c("wt", "hp"),
  nsamples = 20,
  grid_size = 5
)
print(interaction_obj)
summary(interaction_obj)
plot(interaction_obj)

Local surrogate result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_local objects returned by interpret(method = "local") or interpret(method = "lime").

Usage

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

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns the local explanation payload invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
local_obj <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "local",
  newdata = mtcars[1, , drop = FALSE],
  k = 2
)
print(local_obj)
summary(local_obj)
plot(local_obj)

Local surrogate result methods for local_model and lime.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_iml_local_model objects returned by interpret(method = "local_model") or interpret(method = "lime").

Usage

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

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

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns the local model explanation payload invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
local_model <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "local_model",
  newdata = mtcars[1, , drop = FALSE],
  k = 2
)
print(local_model)
summary(local_model)
plot(local_model)

Partial dependence result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_pdp objects returned by interpret(method = "pdp").

Usage

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

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns the partial dependence table invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
pdp <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "pdp",
  features = c("wt", "hp"),
  nsamples = 20
)
print(pdp)
summary(pdp)
plot(pdp)

Permutation importance result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_permute objects returned by interpret(method = "permute").

Usage

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

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns the permutation importance table invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
perm <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "permute",
  features = c("wt", "hp"),
  nsim = 1,
  metric = "rmse"
)
print(perm)
summary(perm)
plot(perm)

SHAP result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_shap objects returned by interpret(method = "shap").

Usage

## S3 method for class 'funcml_shap'
plot(
  x,
  kind = c("auto", "waterfall", "force", "summary", "beeswarm", "importance", "bar",
    "dependence", "dependence2d", "interaction"),
  ...
)

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

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

Arguments

Value

plot() returns a visualization object (typically a ggplot2 object) from shapviz. print() returns the input object invisibly. summary() returns the SHAP contribution table invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
shap <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "shap",
  newdata = mtcars[1, , drop = FALSE],
  nsim = 1
)
print(shap)
summary(shap)
plot(shap)

Global surrogate result methods.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_surrogate objects returned by interpret(method = "surrogate").

Usage

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

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

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

Arguments

Value

plot() returns a ggplot2 object. print() returns the input object invisibly. summary() returns the surrogate model summary object invisibly.

Examples

fit_obj <- fit(mpg ~ wt + hp + disp, data = mtcars, model = "rpart",
  spec = list(cp = 0.01, minsplit = 5))
surrogate <- interpret(
  fit = fit_obj,
  data = mtcars,
  method = "surrogate"
)
print(surrogate)
summary(surrogate)
plot(surrogate)

Ketamine pain management data

Description

A regression dataset on ketamine dosing, treatment characteristics, cost, quality-adjusted life years, and administration mode.

Usage

ketapain

Format

A data frame with 184 rows and 11 variables:

patient_id

Patient identifier.

sexe

Recorded sex.

age

Patient age in years.

av_dose

Average dose.

level_dose

Dose level category.

cum_dose

Cumulative dose.

cum_days

Cumulative treatment days.

perfusion

Perfusion duration.

cost

Treatment cost.

qaly

Quality-adjusted life years.

mode

Administration mode.

Details

Column names were standardized to snake_case when packaging the data.

Source

Original ketamine pain management dataset distributed with the project materials.

Examples

str(funcml::ketapain)
summary(funcml::ketapain$qaly)

Available learners.

Description

learners() returns the registry keys accepted by fit(). Task support is: regression and classification: glm, rpart, glmnet, ranger, nnet, e1071_svm, randomForest, gbm, kknn, ctree, cforest, lightgbm, xgboost, stacking, superlearner; regression plus binary classification: gam, bart, earth; classification only: C50, naivebayes, fda, lda, qda; binary classification only: adaboost; regression only: pls.

Usage

learners()

Details

The learner engine packages are installed with funcml, so the advertised registry is intended to be available after a standard installation.

Value

Character vector of learner ids.

Examples

learners()

List available interpretability methods.

Description

Returns a compact catalog of interpret() entry points and whether each method has a corresponding plot() method.

Usage

list_interpretability_methods(has_plot = NULL, columns = NULL)

Arguments

Value

Data frame of interpretability methods.

Examples

list_interpretability_methods()
subset(list_interpretability_methods(), has_plot)

Learner inventory table with capabilities.

Description

list_learners() returns a compact learner registry in the style of a catalog table. By default it focuses on the most user-visible columns: learner id, generic fit/predict/tune entry points, and availability in the current session.

Usage

list_learners(
  has_fit = NULL,
  has_predict = NULL,
  has_tune = NULL,
  available = NULL,
  columns = NULL,
  regression = NULL,
  classification = NULL,
  prob = NULL,
  multiclass = NULL,
  importance = NULL,
  tune = NULL
)

Arguments

Details

Additional capability metadata remains available through ⁠columns =⁠.

Value

Data frame with learner metadata and capability columns.

Examples

list_learners()
list_learners(has_tune = TRUE)
list_tunable_learners()
list_learners(classification = TRUE, prob = TRUE,
              columns = c("learner", "has_tune", "supports_prob", "engine_package"))

List available metrics used in scoring and resampling summaries.

Description

List available metrics used in scoring and resampling summaries.

Usage

list_metrics(direction = NULL, columns = NULL)

Arguments

Value

Data frame of metric metadata.

Examples

list_metrics()
list_metrics(direction = "minimize")

Shortcut for learners with tuning support.

Description

Shortcut for learners with tuning support.

Usage

list_tunable_learners(...)

Arguments

Value

Data frame with the same columns as list_learners().

Examples

list_tunable_learners()

Mammography calcification data

Description

A binary classification dataset for detection of mammographic microcalcifications.

Usage

mammography

Format

A data frame with 11,183 rows and 7 variables:

attr1

Numeric imaging-derived predictor 1.

attr2

Numeric imaging-derived predictor 2.

attr3

Numeric imaging-derived predictor 3.

attr4

Numeric imaging-derived predictor 4.

attr5

Numeric imaging-derived predictor 5.

attr6

Numeric imaging-derived predictor 6.

class

Calcification class (⁠\"-1\"⁠ or ⁠\"1\"⁠).

Source

Woods K, Doss C, Bowyer K, Solka J, Priebe C, Kegelmeyer P (1993). Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography.

Examples

str(funcml::mammography)
table(funcml::mammography$class)

Regression and classification metrics.

Description

Base R implementations used across evaluation and interpretation utilities.

Usage

rmse(truth, pred)

mae(truth, pred)

mse(truth, pred)

rsq(truth, pred)

medae(truth, pred)

mape(truth, pred)

logloss(truth, prob_matrix)

brier(truth, prob_matrix)

accuracy(truth, pred_class)

precision(truth, pred_class)

recall(truth, pred_class)

specificity(truth, pred_class)

f1(truth, pred_class)

balanced_accuracy(truth, pred_class)

auc(truth, prob, average = c("macro", "weighted"))

auc_weighted(truth, prob)

calibration_curve(
  truth,
  prob,
  bins = 10,
  strategy = c("quantile", "uniform"),
  positive = NULL
)

ece(
  truth,
  prob,
  bins = 10,
  strategy = c("quantile", "uniform"),
  positive = NULL
)

mce(
  truth,
  prob,
  bins = 10,
  strategy = c("quantile", "uniform"),
  positive = NULL
)

Arguments

Value

Numeric scalar metric.

Examples

truth_reg <- c(3, 5, 2.5, 7)
pred_reg <- c(2.8, 4.9, 2.7, 6.8)
rmse(truth_reg, pred_reg)
mae(truth_reg, pred_reg)
mse(truth_reg, pred_reg)
rsq(truth_reg, pred_reg)
medae(truth_reg, pred_reg)
mape(truth_reg, pred_reg)

truth_cls <- factor(c("no", "yes", "yes", "no"), levels = c("no", "yes"))
pred_cls <- factor(c("no", "yes", "no", "no"), levels = levels(truth_cls))
prob_cls <- cbind(
  no = c(0.8, 0.2, 0.6, 0.7),
  yes = c(0.2, 0.8, 0.4, 0.3)
)
logloss(truth_cls, prob_cls)
brier(truth_cls, prob_cls)
accuracy(truth_cls, pred_cls)
precision(truth_cls, pred_cls)
recall(truth_cls, pred_cls)
specificity(truth_cls, pred_cls)
f1(truth_cls, pred_cls)
balanced_accuracy(truth_cls, pred_cls)
auc(truth_cls, prob_cls[, "yes"])

truth_multi <- factor(c("a", "b", "c", "a", "b", "c"), levels = c("a", "b", "c"))
prob_multi <- rbind(
  c(0.90, 0.05, 0.05),
  c(0.05, 0.90, 0.05),
  c(0.05, 0.05, 0.90),
  c(0.85, 0.10, 0.05),
  c(0.10, 0.80, 0.10),
  c(0.05, 0.10, 0.85)
)
colnames(prob_multi) <- levels(truth_multi)
auc(truth_multi, prob_multi)
auc_weighted(truth_multi, prob_multi)
calibration_curve(truth_cls, prob_cls[, "yes"])
ece(truth_cls, prob_cls[, "yes"])
mce(truth_cls, prob_cls[, "yes"])

Thyroid function data

Description

A multiclass classification dataset on thyroid functional state using five laboratory test measurements.

Usage

newthyroid

Format

A data frame with 215 rows and 6 variables:

t3_resin_uptake

T3-resin uptake percentage.

total_serum_thyroxin

Total serum thyroxin measurement.

total_serum_triiodothyronine

Total serum triiodothyronine measurement.

basal_tsh

Basal thyroid-stimulating hormone measurement.

max_abs_tsh_diff

Maximum absolute TSH difference after thyrotropin- releasing hormone injection.

class

Thyroid class (1 = normal, 2 = hyperthyroid, 3 = hypothyroid).

Source

Thyroid gland data distributed through the UCI Machine Learning Repository.

Examples

str(funcml::newthyroid)
table(funcml::newthyroid$class)

Pima diabetes data

Description

A diabetes classification dataset with clinical measurements and a predefined train/test split column.

Usage

pimadiabetes

Format

A data frame with 532 rows and 9 variables:

npreg

Number of pregnancies.

glu

Plasma glucose concentration.

bp

Diastolic blood pressure.

skin

Triceps skin fold thickness.

bmi

Body mass index.

ped

Diabetes pedigree function.

age

Age in years.

diabetes

Diabetes outcome ("Yes" or "No").

split

Suggested split indicator ("train" or "test").

Source

National Institute of Diabetes and Digestive and Kidney Diseases Pima Indians Diabetes Database.

References

Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Annual Symposium on Computer Application in Medical Care, 261-265.

Examples

str(funcml::pimadiabetes)
table(funcml::pimadiabetes$split)

FuncML plotting theme.

Description

A custom ggplot2 theme used across funcml plots. It keeps a clean light background, restrained grid lines, and high-contrast labels so package figures remain consistent and publication-friendly.

Usage

theme_funcml(base_size = 11)

Arguments

Value

A ggplot2 theme object.

Examples

ggplot2::ggplot(mtcars, ggplot2::aes(wt, mpg)) +
  ggplot2::geom_point() +
  theme_funcml()

Time-aware rolling resampling.

Description

Time-aware rolling resampling.

Usage

time_cv(
  initial,
  assess = 1,
  time = NULL,
  skip = 0,
  cumulative = TRUE,
  seed = NULL
)

Arguments

Value

A funcml_cv object.

Examples

time_cv(initial = 8, assess = 2, skip = 1)

Hyperparameter tuning via grid or random search.

Description

Hyperparameter tuning via grid or random search.

Usage

tune(
  data,
  formula,
  model,
  grid,
  resampling = cv(5),
  metric = NULL,
  type = NULL,
  search = c("grid", "random"),
  n_evals = NULL,
  outer_resampling = NULL,
  seed = NULL,
  ncores = NULL,
  ...
)

Arguments

Value

A funcml_tune object.

Examples

tune_obj <- tune(
  data = mtcars,
  formula = mpg ~ wt + hp,
  model = "rpart",
  grid = expand.grid(cp = c(0.001, 0.01), minsplit = c(5, 10)),
  resampling = cv(3, seed = 1),
  metric = "rmse"
)
tune_obj$best

Methods for tuning results.

Description

These methods provide the standard print(), summary(), and plot() interfaces for funcml_tune objects.

Usage

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

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

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

Arguments

Value

print() and summary() return the input object or results table invisibly. plot() returns a ggplot2 object.

Examples

tune_obj <- tune(
  data = mtcars,
  formula = mpg ~ wt + hp,
  model = "rpart",
  grid = expand.grid(cp = c(0.001, 0.01), minsplit = c(5, 10)),
  resampling = cv(3, seed = 1),
  metric = "rmse"
)
print(tune_obj)
summary(tune_obj)
plot(tune_obj)