Package {spfcICOMP}

Benchmark SPFC Across Covariance Estimators

Description

Fits SPFC models using several covariance estimators and compares their downstream predictive performance, runtime, shrinkage behaviour, and variable-selection output.

Usage

benchmark_spfc(
  X,
  y,
  d,
  ytype = c("auto", "continuous", "categorical"),
  methods = c("mec", "oas", "sre", "sde", "cse"),
  variable_method = "adaptive_weighted_l1",
  quantile_cut = 0.75,
  validation = c("resubstitution", "holdout", "cv"),
  test_fraction = 0.3,
  folds = 5,
  validation_seed = 123,
  verbose = TRUE,
  ...
)

Arguments

Value

A list of class spfc_benchmark.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
bench <- benchmark_spfc(
  X, y, d = 1, ytype = 'continuous',
  methods = c('oas', 'sre'),
  variable_method = 'adaptive_weighted_l1',
  quantile_cut = 0.75, validation = 'resubstitution',
  verbose = FALSE
)
bench$summary

Compute Binary Classification Metrics

Description

Computes confusion matrix, accuracy, sensitivity, specificity, precision, and F1 score for binary classification.

Usage

binary_classification_metrics(observed, predicted, positive = NULL)

Arguments

Value

A list containing the confusion matrix and classification metrics.

Examples

obs <- factor(c('A', 'A', 'B', 'B'))
pred <- factor(c('A', 'B', 'B', 'B'), levels = levels(obs))
binary_classification_metrics(obs, pred, positive = 'B')

Extract SPFC Fit from Complete Workflow

Description

Extract SPFC Fit from Complete Workflow

Usage

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

Arguments

Value

Estimated SPFC direction matrix from the fitted SPFC component.

Extract SPFC Coefficients

Description

Extracts the estimated SPFC direction matrix.

Usage

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

Arguments

Value

Numeric matrix of estimated SPFC directions.

Compare Covariance Estimators in SPFC

Description

Fits shrinkage principal fitted components using several covariance estimators and evaluates the reduced model fitted on the resulting SPFC scores.

Usage

compare_covariance_methods(
  X,
  y,
  d,
  methods = c("mec", "oas", "sre", "sde", "cse"),
  ytype = c("auto", "continuous", "categorical"),
  nslices = 5,
  poly_degree = 1,
  gamma = 0.1,
  rho = 0.5,
  centre_x = TRUE,
  scale_x = FALSE,
  classifier = c("auto", "glm", "ridge_logistic"),
  eps = 1e-08,
  verbose = TRUE
)

Arguments

Value

A list containing a summary table and full fitted objects.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
cmp <- compare_covariance_methods(
  X, y, d = 1, methods = c('oas', 'sre'),
  ytype = 'continuous', poly_degree = 2, verbose = FALSE
)
cmp$summary

Create K-fold Cross-validation Folds

Description

Create K-fold Cross-validation Folds

Usage

create_cv_folds(y, folds = 5, seed = 123, stratify = TRUE)

Arguments

Value

Integer vector of fold labels.

Examples

y <- seq_len(12)
create_cv_folds(y, folds = 3, seed = 123, stratify = FALSE)

Create an SPFC Simulation Design Grid

Description

Creates a factorial simulation design grid for SPFC simulation studies.

Usage

create_spfc_design_grid(
  response_type = c("continuous", "categorical"),
  n_values = c(50, 100),
  p_values = c(20, 50),
  d_values = c(1, 2),
  s_values = c(5),
  rho_x_values = c(0.2, 0.5, 0.8),
  snr_values = c(1, 2),
  signal_strength_values = c(0.8, 1.2),
  cov_methods = c("mec", "oas", "sre", "sde", "cse"),
  variable_methods = c("adaptive_weighted_l1", "c1f_extension"),
  nrep = 10
)

Arguments

Value

A data frame containing the full simulation design.

Examples

create_spfc_design_grid(
  response_type = 'continuous', n_values = 30, p_values = 8,
  d_values = 1, s_values = 3, rho_x_values = 0.3,
  snr_values = 2, cov_methods = 'oas',
  variable_methods = 'adaptive_weighted_l1', nrep = 1
)

Create Train-test Split

Description

Create Train-test Split

Usage

create_train_test_split(y, test_fraction = 0.3, seed = 123, stratify = TRUE)

Arguments

Value

A list with train and test indices.

Examples

y <- seq_len(20)
create_train_test_split(
  y, test_fraction = 0.25, seed = 123, stratify = FALSE
)

Convex Sum Covariance Estimator

Description

Computes a convex combination of the sample covariance matrix and a spherical target matrix.

Usage

cse(x, rho = 0.5)

Arguments

Value

A list with covariance matrix covx, shrinkage intensity rho, and method label.

Examples

set.seed(1)
X <- matrix(rnorm(120), nrow = 20, ncol = 6)
est <- cse(X, rho = 0.5)
dim(est$covx)

Estimate a Regularised Covariance Matrix

Description

Unified interface for covariance estimators used in shrinkage principal fitted components regression.

Usage

estimate_covariance(
  X,
  y = NULL,
  ytype = c("auto", "continuous", "categorical"),
  method = c("mec", "oas", "sre", "sde", "cse"),
  nslices = 5,
  gamma = 0.1,
  rho = 0.5,
  eps = 1e-08
)

Arguments

Value

A list containing the covariance estimate, shrinkage intensity, and method diagnostics.

Examples

set.seed(1)
X <- matrix(rnorm(120), nrow = 20, ncol = 6)
est <- estimate_covariance(X, method = 'oas')
c(method = est$method, rho = est$rho)

Evaluate a Reduced Model

Description

Evaluates a model fitted on reduced SPFC scores.

Usage

evaluate_reduced_model(object, Z, y, threshold = 0.5, positive = NULL)

Arguments

Value

A list of evaluation metrics.

Examples

set.seed(1)
Z <- matrix(rnorm(40), nrow = 20, ncol = 2)
y <- Z[, 1] - 0.5 * Z[, 2] + rnorm(20, sd = 0.4)
mod <- fit_reduced_model(Z, y, ytype = 'continuous')
evaluate_reduced_model(mod, Z, y)

Evaluate SPFC with K-fold Cross-validation

Description

Evaluate SPFC with K-fold Cross-validation

Usage

evaluate_spfc_cv(
  X,
  y,
  d,
  ytype = c("auto", "continuous", "categorical"),
  cov_method = c("mec", "oas", "sre", "sde", "cse"),
  folds = 5,
  seed = 123,
  nslices = 5,
  poly_degree = 1,
  variable_method = c("adaptive_weighted_l1", "c1f_extension"),
  selection_rule = c("c1f", "quantile", "fixed"),
  threshold = NULL,
  quantile_cut = 0.75,
  centre_x = TRUE,
  scale_x = FALSE,
  ...
)

Arguments

Value

A list containing fold-level and aggregate validation results.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
cv <- evaluate_spfc_cv(
  X, y, d = 1, ytype = 'continuous', cov_method = 'oas',
  folds = 3, seed = 123, poly_degree = 2,
  selection_rule = 'quantile', quantile_cut = 0.75
)
cv$aggregate

Evaluate SPFC with Train-test Validation

Description

Evaluate SPFC with Train-test Validation

Usage

evaluate_spfc_train_test(
  X,
  y,
  train_idx,
  test_idx,
  d,
  ytype = c("auto", "continuous", "categorical"),
  cov_method = c("mec", "oas", "sre", "sde", "cse"),
  nslices = 5,
  poly_degree = 1,
  variable_method = c("adaptive_weighted_l1", "c1f_extension"),
  selection_rule = c("c1f", "quantile", "fixed"),
  threshold = NULL,
  quantile_cut = 0.75,
  centre_x = TRUE,
  scale_x = FALSE,
  ...
)

Arguments

Value

A list containing fit, reduced model, predictions, and test metrics.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
split <- create_train_test_split(
  y, test_fraction = 0.25, seed = 123, stratify = FALSE
)
ans <- evaluate_spfc_train_test(
  X, y, train_idx = split$train, test_idx = split$test,
  d = 1, ytype = 'continuous', cov_method = 'oas',
  poly_degree = 2, selection_rule = 'quantile',
  quantile_cut = 0.75
)
ans$evaluation

Fit Reduced Downstream Model

Description

Fits a downstream predictive model using the reduced SPFC score matrix.

Usage

fit_reduced_model(
  Z,
  y,
  ytype = c("auto", "continuous", "categorical"),
  classifier = c("auto", "glm", "ridge_logistic"),
  standardize = TRUE
)

Arguments

Value

A list containing the fitted downstream model and metadata.

Examples

set.seed(1)
Z <- matrix(rnorm(40), nrow = 20, ncol = 2)
y <- Z[, 1] - 0.5 * Z[, 2] + rnorm(20, sd = 0.4)
fit_reduced_model(Z, y, ytype = 'continuous')

Extract Reduced Scores from Complete Workflow

Description

Extract Reduced Scores from Complete Workflow

Usage

## S3 method for class 'spfc'
fitted(object, ...)

Arguments

Value

Reduced SPFC score matrix.

Fitted SPFC Scores

Description

Extracts the reduced SPFC score matrix.

Usage

## S3 method for class 'spfc_fit'
fitted(object, ...)

Arguments

Value

Numeric matrix of reduced SPFC scores.

Maximum Entropy Covariance Estimator

Description

Computes the Maximum Entropy Covariance estimator for sufficient dimension reduction. The response is first sliced. Slice-specific covariance matrices are computed, the entropy-maximising slice covariance is selected, and an entropy-guided covariance estimate is stabilised by a convex shrinkage step.

Usage

mec(
  x,
  y,
  ytype = c("auto", "continuous", "categorical"),
  nslices = 5,
  eps = 1e-08
)

Arguments

Value

A list with covariance matrix covx, shrinkage intensity rho, method label, response type, number of slices used, and entropy values.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
est <- mec(X, y, ytype = 'continuous', nslices = 5)
c(method = est$method, rho = est$rho)

Oracle Approximating Shrinkage-Type Covariance Estimator

Description

Computes an OAS-type stabilised covariance estimator with an additional convex shrinkage step.

Usage

oas(x)

Arguments

Value

A list with covariance matrix covx, shrinkage intensity rho, and method label.

Examples

set.seed(1)
X <- matrix(rnorm(120), nrow = 20, ncol = 6)
est <- oas(X)
c(method = est$method, rho = est$rho)

Plot Dimension-Selection Criteria

Description

Plots information criteria returned by spfc_select_dimension().

Usage

plot_dimension_selection(
  dsel,
  criterion = c("AIC", "BIC", "CAIC", "ICOMP_IFIM", "ICOMP_MISSPEC", "CICOMP")
)

Arguments

Value

Invisibly returns the plotted data.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
dsel <- spfc_select_dimension(
  X, y, d_grid = 1:2, cov_method = 'oas',
  ytype = 'continuous', poly_degree = 2, verbose = FALSE
)
plot_dimension_selection(dsel, criterion = 'ICOMP_IFIM')

Plot SPFC Simulation Metric by Covariance Method

Description

Produces a base R boxplot for a selected simulation metric across covariance estimators.

Usage

plot_metric_by_covariance(results, metric, main = NULL, ylab = NULL)

Arguments

Value

Invisibly returns the formula used for plotting.

Examples

results <- data.frame(
  cov_method = rep(c('oas', 'sre'), each = 2),
  rmse = c(0.50, 0.55, 0.62, 0.59),
  runtime_sec = c(0.01, 0.012, 0.008, 0.009),
  subspace_distance = c(0.20, 0.22, 0.28, 0.25)
)
plot_metric_by_covariance(results, metric = 'rmse')

Plot RMSE by Covariance Method

Description

Plot RMSE by Covariance Method

Usage

plot_rmse_by_covariance(results)

Arguments

Value

Invisibly returns the plotting formula.

Examples

results <- data.frame(
  cov_method = rep(c('oas', 'sre'), each = 2),
  rmse = c(0.50, 0.55, 0.62, 0.59),
  runtime_sec = c(0.01, 0.012, 0.008, 0.009),
  subspace_distance = c(0.20, 0.22, 0.28, 0.25)
)
plot_rmse_by_covariance(results)

Plot Runtime by Covariance Method

Description

Plot Runtime by Covariance Method

Usage

plot_runtime_by_covariance(results)

Arguments

Value

Invisibly returns the plotting formula.

Examples

results <- data.frame(
  cov_method = rep(c('oas', 'sre'), each = 2),
  rmse = c(0.50, 0.55, 0.62, 0.59),
  runtime_sec = c(0.01, 0.012, 0.008, 0.009),
  subspace_distance = c(0.20, 0.22, 0.28, 0.25)
)
plot_runtime_by_covariance(results)

Plot SPFC Reduced Scores

Description

Produces a reduced-score plot from an object returned by spfc_fit().

Usage

plot_spfc_scores(fit, y = NULL, dims = c(1, 2), main = NULL)

Arguments

Value

Invisibly returns the plotted data.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
fit <- spfc_fit(X, y, d = 1, ytype = 'continuous',
                cov_method = 'oas', poly_degree = 2)
plot_spfc_scores(fit, y = y, dims = 1)

Plot Subspace Distance by Covariance Method

Description

Plot Subspace Distance by Covariance Method

Usage

plot_subspace_distance_by_covariance(results)

Arguments

Value

Invisibly returns the plotting formula.

Examples

results <- data.frame(
  cov_method = rep(c('oas', 'sre'), each = 2),
  rmse = c(0.50, 0.55, 0.62, 0.59),
  runtime_sec = c(0.01, 0.012, 0.008, 0.009),
  subspace_distance = c(0.20, 0.22, 0.28, 0.25)
)
plot_subspace_distance_by_covariance(results)

Predict from Ridge Logistic Regression

Description

Predict from Ridge Logistic Regression

Usage

## S3 method for class 'ridge_logistic_fit'
predict(object, newdata, type = c("class", "response"), ...)

Arguments

Value

Predicted classes or probabilities.

Predict SPFC Scores from Complete Workflow

Description

Predict SPFC Scores from Complete Workflow

Usage

## S3 method for class 'spfc'
predict(object, newdata, ...)

Arguments

Value

Numeric matrix of predicted SPFC scores.

Predict SPFC Scores for New Data

Description

Projects new predictor observations into the estimated SPFC reduced space.

Usage

## S3 method for class 'spfc_fit'
predict(object, newdata, ...)

Arguments

Value

Numeric matrix of predicted SPFC scores.

Predict from a Reduced Model Fit

Description

Generates fitted values, probabilities, or predicted classes from an object returned by fit_reduced_model().

Usage

predict_reduced_model(
  object,
  Z,
  type = c("response", "class", "link"),
  threshold = 0.5
)

Arguments

Value

Numeric predictions or factor class labels.

Examples

set.seed(1)
Z <- matrix(rnorm(40), nrow = 20, ncol = 2)
y <- Z[, 1] - 0.5 * Z[, 2] + rnorm(20, sd = 0.4)
mod <- fit_reduced_model(Z, y, ytype = 'continuous')
head(predict_reduced_model(mod, Z, type = 'response'))

Print Complete SPFC Workflow

Description

Print Complete SPFC Workflow

Usage

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

Arguments

Value

Invisibly returns x.

Print SPFC Benchmark

Description

Print SPFC Benchmark

Usage

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

Arguments

Value

Invisibly returns x.

Print an SPFC Fit

Description

Print an SPFC Fit

Usage

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

Arguments

Value

Invisibly returns x.

Print Summary of Complete SPFC Workflow

Description

Print Summary of Complete SPFC Workflow

Usage

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

Arguments

Value

Invisibly returns x.

Print Summary of SPFC Benchmark

Description

Print Summary of SPFC Benchmark

Usage

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

Arguments

Value

Invisibly returns x.

Print Summary of an SPFC Fit

Description

Print Summary of an SPFC Fit

Usage

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

Arguments

Value

Invisibly returns x.

Rank Covariance Methods from Simulation Summaries

Description

Ranks covariance estimators according to a chosen performance metric.

Usage

rank_covariance_methods(summary, metric, smaller_is_better = TRUE)

Arguments

Value

Ranked data frame.

Examples

summary <- data.frame(
  cov_method = c('oas', 'sre'),
  mean_rmse = c(0.50, 0.60)
)
rank_covariance_methods(summary, metric = 'mean_rmse')

Run an SPFC Simulation Design

Description

Runs an SPFC simulation design grid sequentially.

Usage

run_spfc_design(
  design,
  base_seed = 123,
  d_grid = 1:5,
  criteria = c("AIC", "BIC", "CAIC", "ICOMP_IFIM", "CICOMP"),
  selection_rule = "c1f",
  nslices = 5,
  poly_degree = 2,
  gamma = 0.1,
  rho = 0.5,
  save_every = NULL,
  save_path = "spfc_simulation_partial.rds",
  verbose = TRUE
)

Arguments

Value

A data frame of simulation results.

Examples

design <- create_spfc_design_grid(
  response_type = 'continuous', n_values = 30, p_values = 8,
  d_values = 1, s_values = 3, rho_x_values = 0.3,
  snr_values = 2, cov_methods = 'oas',
  variable_methods = 'adaptive_weighted_l1', nrep = 1
)
ans <- run_spfc_design(
  design, base_seed = 123, d_grid = 1, criteria = 'AIC',
  selection_rule = 'c1f', poly_degree = 2, verbose = FALSE
)
ans[, c('cov_method', 'criterion', 'fitted_d', 'rmse')]

Run a Small SPFC Simulation Study

Description

Run a Small SPFC Simulation Study

Usage

run_spfc_simulation(
  response_type = c("continuous", "categorical"),
  nrep = 10,
  n = 100,
  p = 50,
  d = 1,
  s = 5,
  rho_x = 0.5,
  snr = 1,
  signal_strength = 1,
  cov_methods = c("mec", "oas", "sre", "sde", "cse"),
  variable_methods = c("adaptive_weighted_l1", "c1f_extension"),
  seed = 123
)

Arguments

Value

A data frame of simulation results.

Examples

ans <- run_spfc_simulation(
  response_type = 'continuous', nrep = 1, n = 30, p = 8,
  d = 1, s = 3, rho_x = 0.3, snr = 2,
  cov_methods = c('oas', 'sre'),
  variable_methods = 'adaptive_weighted_l1', seed = 123
)
ans[, c('cov_method', 'rmse', 'subspace_distance')]

Score Information Criteria for a Reduced Model

Description

Computes AIC, BIC, CAIC, ICOMP(IFIM), ICOMP(Misspec), and CICOMP for a model fitted on reduced SPFC scores.

Usage

score_information_criteria(reduced_model, n, eps = 1e-10)

Arguments

Value

A one-row data frame of information criteria.

Examples

set.seed(1)
Z <- matrix(rnorm(40), nrow = 20, ncol = 2)
y <- Z[, 1] - 0.5 * Z[, 2] + rnorm(20, sd = 0.4)
mod <- fit_reduced_model(Z, y, ytype = 'continuous')
score_information_criteria(mod, n = length(y))

Stipulated Diagonal Covariance Estimator

Description

Computes a diagonal covariance estimator by retaining only the diagonal elements of the sample covariance matrix.

Usage

sde(x)

Arguments

Value

A list with covariance matrix covx, shrinkage intensity rho, and method label.

Examples

set.seed(1)
X <- matrix(rnorm(120), nrow = 20, ncol = 6)
est <- sde(X)
c(method = est$method, rho = est$rho)

Simulate Binary High-Dimensional Classification Data

Description

Simulate Binary High-Dimensional Classification Data

Usage

simulate_spfc_binary(
  n = 100,
  p = 50,
  d = 1,
  s = 5,
  rho_x = 0.5,
  signal_strength = 1,
  seed = NULL
)

Arguments

Value

A list containing X, y, true directions, active set, and covariance.

Examples

sim <- simulate_spfc_binary(
  n = 30, p = 8, d = 1, s = 3, rho_x = 0.3,
  signal_strength = 1, seed = 123
)
table(sim$y)

Simulate Continuous High-Dimensional PFC Data

Description

Generates data directly from a principal fitted components inverse model, so the nominal structural dimension is identifiable by construction. For d = 1, the inverse mean uses f(Y) = Y. For d = 2, it uses the two population-orthonormal basis functions Y and (Y^2 - 1)/sqrt(2) with Y ~ N(0,1). The inverse-mean loading matrix is constructed so that the true sufficient reduction subspace is the sparse matrix returned as B_true, even when the residual predictor covariance is correlated.

Usage

simulate_spfc_continuous(
  n = 100,
  p = 50,
  d = 1,
  s = 5,
  rho_x = 0.5,
  snr = 1,
  n_test = n,
  seed = NULL
)

Arguments

Details

An independent test sample is generated from the same inverse model for honest out-of-sample prediction assessment.

Value

A list containing training and test data, true PFC quantities, sparse reduction directions, active set, and residual covariance.

Examples

sim <- simulate_spfc_continuous(
  n = 30, p = 8, d = 1, s = 3, rho_x = 0.3,
  snr = 2, n_test = 10, seed = 123
)
c(train_n = length(sim$y), test_n = length(sim$y_test))

Fit a Complete SPFC Workflow

Description

Fits a complete Shrinkage Principal Fitted Components workflow, including optional structural dimension selection, SPFC fitting, reduced model fitting, evaluation, and variable selection.

Usage

spfc(
  X,
  y,
  d = NULL,
  d_grid = 1:3,
  ytype = c("auto", "continuous", "categorical"),
  cov_method = c("mec", "oas", "sre", "sde", "cse"),
  selector = c("AIC", "BIC", "CAIC", "ICOMP_IFIM", "ICOMP_MISSPEC", "CICOMP"),
  nslices = 5,
  poly_degree = 1,
  variable_method = c("adaptive_weighted_l1", "c1f_extension"),
  selection_rule = c("c1f", "quantile", "fixed"),
  threshold = NULL,
  quantile_cut = 0.75,
  validation = c("resubstitution", "holdout", "cv"),
  test_fraction = 0.3,
  folds = 5,
  validation_seed = 123,
  centre_x = TRUE,
  scale_x = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Value

A list of class spfc containing the complete workflow.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
obj <- spfc(
  X, y, d = 1, ytype = 'continuous', cov_method = 'oas',
  poly_degree = 2, selection_rule = 'quantile',
  quantile_cut = 0.75, validation = 'resubstitution',
  verbose = FALSE
)
obj

Fit Shrinkage Principal Fitted Components

Description

Fits a shrinkage principal fitted components model by replacing the unstable covariance object in the PFC reduction step with a selected shrinkage covariance estimator.

Usage

spfc_fit(
  X,
  y,
  d,
  fy = NULL,
  ytype = c("auto", "continuous", "categorical"),
  cov_method = c("mec", "oas", "sre", "sde", "cse"),
  nslices = 5,
  poly_degree = 1,
  gamma = 0.1,
  rho = 0.5,
  centre_x = TRUE,
  scale_x = FALSE,
  eps = 1e-08
)

Arguments

Value

A spfc_fit object containing reduced scores, directions, fitted covariance objects, response basis, preprocessing metadata, and diagnostics.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
fit <- spfc_fit(
  X, y, d = 1, ytype = 'continuous',
  cov_method = 'oas', poly_degree = 2
)
coef(fit)

Select Structural Dimension for SPFC

Description

Fits SPFC models across candidate dimensions and scores classical and information-complexity-based criteria.

Usage

spfc_select_dimension(
  X,
  y,
  d_grid = 1:3,
  cov_method = c("mec", "oas", "sre", "sde", "cse"),
  ytype = c("auto", "continuous", "categorical"),
  nslices = 5,
  poly_degree = 1,
  gamma = 0.1,
  rho = 0.5,
  centre_x = TRUE,
  scale_x = FALSE,
  classifier = c("auto", "glm"),
  eps = 1e-08,
  verbose = TRUE
)

Arguments

Value

A list containing the criterion table and selected dimensions by criterion.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
dsel <- spfc_select_dimension(
  X, y, d_grid = 1:2, cov_method = 'oas',
  ytype = 'continuous', poly_degree = 2, verbose = FALSE
)
dsel$selected

Select Variables from SPFC Directions

Description

Selects variables using either the thesis-safe adaptive weighted L1 route or the experimental C1F-informed extension route.

Usage

spfc_select_variables(
  fit,
  method = c("adaptive_weighted_l1", "c1f_extension"),
  selection_rule = c("c1f", "quantile", "fixed"),
  reduced_model = NULL,
  threshold = NULL,
  quantile_cut = 0.75,
  gamma = 1,
  c1f_calibration = c("icomp_hd_floor", "robust_universal", "raw"),
  eps = 1e-10
)

Arguments

Value

A data frame containing variable indices, importance scores, weights, and selected status.

Examples

set.seed(1)
X <- matrix(rnorm(180), nrow = 30, ncol = 6)
y <- X[, 1] - 0.7 * X[, 2] + rnorm(30, sd = 0.5)
fit <- spfc_fit(X, y, d = 1, ytype = 'continuous',
                cov_method = 'oas', poly_degree = 2)
reduced_model <- fit_reduced_model(
  fit$Z, y, ytype = 'continuous'
)
sel <- spfc_select_variables(
  fit, method = 'c1f_extension', selection_rule = 'c1f',
  reduced_model = reduced_model
)
sel[, c('variable', 'shrunk_importance', 'selected')]

Stipulated Ridge Covariance Estimator

Description

Computes a ridge-stabilised covariance estimator by adding a positive constant to the diagonal of the sample covariance matrix.

Usage

sre(x, gamma = 0.1)

Arguments

Value

A list with covariance matrix covx, ridge intensity rho, and method label.

Examples

set.seed(1)
X <- matrix(rnorm(120), nrow = 20, ncol = 6)
est <- sre(X, gamma = 0.1)
c(method = est$method, rho = est$rho)

Compute Subspace Distance

Description

Computes a sine-angle distance between two subspaces.

Usage

subspace_distance(B_true, B_hat)

Arguments

Value

Numeric subspace distance.

Examples

B1 <- matrix(c(1, 0, 0), ncol = 1)
B2 <- matrix(c(0.9, 0.1, 0), ncol = 1)
subspace_distance(B1, B2)

Summarise SPFC Simulation Results

Description

Aggregates Monte Carlo simulation results by covariance estimator, variable-selection method, and design factors.

Usage

summarise_spfc_simulation(results)

Arguments

Value

A summary data frame.

Examples

results <- run_spfc_simulation(
  response_type = 'continuous', nrep = 1, n = 30, p = 8,
  d = 1, s = 3, rho_x = 0.3, snr = 2,
  cov_methods = c('oas', 'sre'),
  variable_methods = 'adaptive_weighted_l1', seed = 123
)
summarise_spfc_simulation(results)

Summarise Complete SPFC Workflow

Description

Summarise Complete SPFC Workflow

Usage

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

Arguments

Value

A list of class summary.spfc.

Summarise SPFC Benchmark

Description

Summarise SPFC Benchmark

Usage

## S3 method for class 'spfc_benchmark'
summary(object, metric = NULL, smaller_is_better = NULL, ...)

Arguments

Value

A list of class summary.spfc_benchmark.

Summarise an SPFC Fit

Description

Summarise an SPFC Fit

Usage

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

Arguments

Value

A list containing key SPFC summary components.

Compute Variable-Selection Performance

Description

Compute Variable-Selection Performance

Usage

variable_selection_metrics(selected, active_set, p)

Arguments

Value

A list of precision, recall, F1, false positives, and false negatives.

Examples

variable_selection_metrics(
  selected = c(1, 2, 5), active_set = c(1, 2, 3), p = 8
)