Package {TransHDM}

Sure Independence Screening for High-Dimensional Mediation Analysis

Description

Step 1 of the TransHDM pipeline: dimension reduction via Sure Independence Screening (SIS). Mediators are ranked based on the product of their marginal associations with the exposure (alpha) and the outcome (beta), and the top-ranked mediators are retained for downstream de-biased lasso estimation (mediation_inference, Step 2) and joint testing (joint_test, Step 3).

The function supports transfer learning, allowing information from a source dataset to be leveraged to improve screening stability and robustness in the target dataset.

Usage

SIS(
  target_data,
  source_data = NULL,
  Y,
  D,
  M,
  X,
  topN = NULL,
  transfer = FALSE,
  verbose = TRUE,
  ncore = 1,
  dblasso_method = FALSE
)

Arguments

Value

A list of class "SIS" with the following components:

• target_SIS: A data frame containing the outcome, exposure, selected mediators, and covariates from the target dataset.

• source_SIS: A data frame containing the same variables from the source dataset if transfer = TRUE; otherwise NULL.

• M_ID_name_SIS: A character vector of selected mediator names.

• Y: The resolved outcome variable name.

• D: The resolved exposure variable name.

• M: The full mediator variable names (before screening).

• X: The resolved covariate variable names.

See Also

TransHDM, mediation_inference, joint_test

Examples

set.seed(123)

# Target data
M_target <- matrix(rnorm(200 * 50), nrow = 200)
colnames(M_target) <- paste0("M", 1:50)

target_data <- data.frame(
  Y = rnorm(200),
  D = rnorm(200),
  M_target,
  X1 = rnorm(200)
)

# Source data
M_source <- matrix(rnorm(300 * 50), nrow = 300)
colnames(M_source) <- paste0("M", 1:50)

source_data <- data.frame(
  Y = rnorm(300),
  D = rnorm(300),
  M_source,
  X1 = rnorm(300)
)

# Run SIS
result <- SIS(
  target_data = target_data,
  source_data = source_data,
  Y = "Y",
  D = "D",
  M = paste0("M", 1:50),
  X = "X1",
  transfer = TRUE,
  topN = 10
)

result$M_ID_name_SIS

TransHDM: High-Dimensional Mediation Analysis with Transfer Learning

Description

The TransHDM function performs high-dimensional mediation analysis under a transfer learning framework. It identifies and estimates indirect (mediation) effects of a high-dimensional set of mediators between an exposure and an outcome by integrating a target dataset and a source datasets.

Usage

TransHDM(
  target_data,
  source_data = NULL,
  Y,
  D,
  M,
  X,
  transfer = FALSE,
  verbose = TRUE,
  ncore = 1,
  topN = NULL,
  use_SIS = TRUE,
  dblasso_SIS = FALSE,
  p_cutoff = 0.05
)

Arguments

Details

TransHDM implements a three-step workflow that can also be executed step-by-step using the individual components:

1. Step 1 — Screening: SIS performs Sure Independence Screening to reduce dimensionality by ranking mediators based on their marginal associations.

2. Step 2 — De-biased estimation: mediation_inference fits de-biased lasso models to estimate exposure-mediator (alpha) and mediator-outcome (beta) effects.

3. Step 3 — Multiple testing: joint_test applies joint multiple testing with FDR control to identify significant mediators and computes effect summaries.

Set use_SIS = FALSE to skip Step 1 and use all mediators directly.

Value

A list with the following components:

• contributions: A data frame of identified mediators containing:

  • mediator: Mediator name

  • alpha: Estimated exposure mediator effect

  • alpha_pv: P-value for the exposure mediator effect

  • beta: Estimated mediator outcome effect

  • beta_pv: P-value for the mediator outcome effect

  • alpha_beta: Estimated indirect (mediation) effect

  • ab_pv: Joint p-value for the indirect effect

  • pa: Proportion of the total effect mediated

• effects: A data frame summarizing the total indirect effect, direct effect, total effect, and proportion mediated.

• IDE_est: A numeric vector of indirect effect estimates for all specified mediators, with non-selected mediators set to zero.

• DE_est: The estimated direct effect of the exposure on the outcome.

• all_mediators: A data frame of all SIS-screened mediators (including non-significant ones) with columns mediator, alpha, alpha_pv, beta, beta_pv, and significant (logical).

References

Pan L, Liu Y, Huang C, Lin R, Yu Y, Qin G. Transfer learning reveals the mediating mechanisms of cross-ethnic lipid metabolic pathways in the association between APOE gene and Alzheimer's disease. Brief Bioinform. 2025;26(5):bbaf460. doi:10.1093/bib/bbaf460

See Also

SIS, mediation_inference, joint_test

Examples

set.seed(123)

# Target data
target_data <- gen_simData_homo(n = 50, p_x = 3, p_m = 20, rho = 0.1)$data

# Source data
source_data <- gen_simData_homo(n = 100, p_x = 3, p_m = 20, rho = 0.1, source = TRUE,
transferable = TRUE)$data

# Run TransHDM
result <- TransHDM(
  target_data = target_data,
  source_data = source_data,
  Y = "Y",
  D = "D",
  M = paste0("M", 1:20),
  X = paste0("X", 1:3),
  transfer = TRUE,
  ncore = 1,
  topN = 10
)
summary(result)

Fit Debiased LASSO with Transfer Learning

Description

Fits a debiased LASSO regression model under transfer learning framework, supporting feature selection and coefficient estimation by combining target and source data.

Usage

dblasso(
  target,
  source = NULL,
  transfer = FALSE,
  level = 0.95,
  lambda = "lambda.1se"
)

Arguments

Value

A list containing:

• dbcoef.hat: Debiased LASSO coefficient vector (including intercept)

• coef.hat: Original LASSO coefficient vector

• CI: Data frame with confidence intervals (lb = lower bound, ub = upper bound)

• var.est: Variance estimates for debiased coefficients

• se.est: Standard errors for debiased coefficients

• P.value: Vector of p-values for coefficients

Simulated Dataset Generation for High-Dimensional Mediation Analysis

Description

Generates synthetic datasets mimicking high-dimensional mediation structures, optionally incorporating transferable source data under varying covariate correlation and heterogeneity levels. This function supports heterogeneous settings for data generation.

Usage

gen_simData_hetero(
  n = 100,
  p_x = 5,
  rho = 0,
  p_m = 100,
  h = 0,
  source = FALSE,
  transferable = TRUE,
  seed = NULL
)

Arguments

Details

This function generates data according to a structural equation model (SEM):

• Treatment D depends linearly on covariates X.

• Mediators M depend on D and X, with residual correlation controlled by rho.

• Outcome Y depends on D, M, and X.

• If source = TRUE, then a source dataset is simulated with potentially transferable mechanisms.

Value

A list with the following components:

• data: A data.frame of dimension n × (2 + p_m + p_x) containing outcome (Y), treatment (D), mediators (M1–Mp_m), and covariates (X1–Xp_x).

• effect: A named list of true total, direct, and indirect effects, exposure-mediator effects and mediator-outcome effects.

Examples

source_data <- gen_simData_hetero(
  n = 100, p_x = 5, rho = 0, p_m = 100, h = 0,
  source = TRUE, transferable = TRUE, seed = 123
)
source_data <- gen_simData_hetero(
  n = 100, p_x = 5, rho = 0, p_m = 100, h = 0,
  source = FALSE, transferable = TRUE, seed = 123
)

Simulated Dataset Generation for High-Dimensional Mediation Analysis

Description

Generates synthetic datasets mimicking high-dimensional mediation structures, optionally incorporating transferable source data under varying covariate correlation and heterogeneity levels. This function supports homogeneous settings for data generation.

Usage

gen_simData_homo(
  n = 100,
  p_x = 5,
  rho = 0,
  p_m = 100,
  h = 0,
  source = FALSE,
  transferable = TRUE,
  seed = NULL
)

Arguments

Details

This function generates data according to a structural equation model (SEM):

• Treatment D depends linearly on covariates X.

• Mediators M depend on D and X, with residual correlation controlled by rho.

• Outcome Y depends on D, M, and X.

• If source = TRUE, then a source dataset is simulated with potentially transferable mechanisms.

Value

A list with the following components:

• data: A data.frame of dimension n * (2 + p_m + p_x) containing outcome (Y), treatment (D), mediators (M1–Mp_m), and covariates (X1–Xp_x).

• effect: A named list of true total, direct, and indirect effects, exposure-mediator effects and mediator-outcome effects.

Examples

source_data <- gen_simData_homo(
  n = 100, p_x = 5, rho = 0, p_m = 100, h = 0,
  source = TRUE, transferable = TRUE, seed = 123
)
target_data <- gen_simData_homo(
  n = 100, p_x = 5, rho = 0, p_m = 100, h = 0,
  source = FALSE, transferable = TRUE, seed = 123
)

Inflammatory Mediation Benchmark Data: Ground Truth Effects

Description

Ground-truth mediation effects for the target cohort of the inflammatory mediation benchmark dataset. Maps each candidate mediator to its true exposure–mediator effect (\alpha), mediator–outcome effect (\beta), and indirect effect (\alpha \times \beta).

Usage

data(inflam_effect)

Format

A data frame with 20 rows (one per mediator) and 4 columns:

• mediator: mediator name.

• alpha: true exposure–mediator effect.

• beta: true mediator–outcome effect.

• alpha_beta: true indirect (mediation) effect.

Details

Four mediators have non-zero \alpha \times \beta and are the true active mediators. Because the mediator columns were randomly permuted during data generation, these active mediators are scattered across the candidate names — as in a real study, where the ground truth is unknown at analysis time. This table lets users benchmark TransHDM against the truth.

See Also

inflam_target, inflam_external1, inflam_external2 for the associated cohorts.

Examples

data(inflam_effect)
inflam_effect[inflam_effect$alpha_beta != 0, ]

Inflammatory Mediation Benchmark Data: Transferable External Cohort

Description

Simulated transferable external cohort for high-dimensional mediation analysis. It shares the mediator–outcome structure with the target cohort, making it suitable for transfer learning.

Usage

data(inflam_external1)

Format

A data frame with 200 rows (patients) and 25 columns, with the same structure as inflam_target:

• DSI: outcome — disease severity index.

• IB: exposure — inflammatory biomarker.

• CYT_01–SIG_04: 20 candidate mediators.

• BIS, PRS, BRS: 3 covariates.

Details

This large external cohort (n = 200) is generated under a transferable setting: its exposure–mediator (\alpha) and mediator–outcome (\beta) coefficients match those of the target for the active mediators. It is intended as the source in TransHDM with transfer = TRUE.

Note

Demonstration data only. Simulated for methodological demonstration of the TransHDM workflow.

Source

Generated with gen_simData_homo (n = 200, p_x = 3, p_m = 20, rho = 0.1, seed = 123, source = TRUE, transferable = TRUE).

See Also

inflam_target for the target cohort, inflam_external2 for the non-transferable external cohort.

Examples

data(inflam_external1)
str(inflam_external1)

Inflammatory Mediation Benchmark Data: Non-Transferable External Cohort

Description

Simulated non-transferable external cohort for high-dimensional mediation analysis. Its mediator structure differs from the target cohort, and the covariate distribution is shifted, so it should not be used for transfer.

Usage

data(inflam_external2)

Format

A data frame with 100 rows (patients) and 25 columns, with the same structure as inflam_target:

• DSI: outcome — disease severity index.

• IB: exposure — inflammatory biomarker.

• CYT_01–SIG_04: 20 candidate mediators.

• BIS, PRS, BRS: 3 covariates.

Details

This external cohort (n = 100) is generated under a non-transferable setting with covariate shift. Its exposure–mediator and mediator–outcome coefficients differ from the target, so naive transfer would degrade performance. It is used with source_detection to demonstrate detection of non-transferable sources.

Note

Demonstration data only. Simulated for methodological demonstration of the TransHDM workflow.

Source

Generated with gen_simData_hetero (n = 100, p_x = 3, p_m = 20, rho = 0.1, seed = 999, source = TRUE, transferable = FALSE).

See Also

inflam_target for the target cohort, inflam_external1 for the transferable external cohort.

Examples

data(inflam_external2)
str(inflam_external2)

Inflammatory Mediation Benchmark Data: Target Cohort

Description

Simulated target cohort for a high-dimensional mediation analysis in which an inflammatory biomarker (exposure) influences a disease severity index (outcome) through a panel of candidate molecular mediators, adjusting for clinical covariates.

Usage

data(inflam_target)

Format

A data frame with 50 rows (patients) and 25 columns:

• DSI: outcome — disease severity index.

• IB: exposure — inflammatory biomarker.

• CYT_01–SIG_04: 20 candidate mediators across five functional groups (cytokines CYT, immune markers IMM, oxidative stress OXI, metabolic MET, signalling SIG).

• BIS, PRS, BRS: 3 covariates (baseline inflammatory status, physiological reserve score, baseline risk score).

Details

Mediation model:

IB \rightarrow Molecular\ Mediators \rightarrow DSI

This is the small-sample target cohort (n = 50) that benefits from transfer learning using the larger external cohorts. The mediator columns have been randomly permuted, so the identity of the truly active mediators is hidden; the ground truth is stored in inflam_effect.

Note

Demonstration data only. Simulated for methodological demonstration of the TransHDM workflow, not for drawing biological conclusions.

Source

Generated with gen_simData_homo (n = 50, p_x = 3, p_m = 20, rho = 0.1, seed = 123). See gen_data.R in the package source.

See Also

inflam_external1, inflam_external2 for the external cohorts, and inflam_effect for the ground truth.

Examples

data(inflam_target)
str(inflam_target)

Joint Multiple Testing for Mediation Analysis

Description

Step 3 (final step) of the TransHDM pipeline: joint multiple testing on the alpha and beta p-values obtained from mediation_inference (Step 2). It estimates the null proportion, controls the false discovery rate via HDMT::fdr_est, identifies significant mediators, and summarises the direct, indirect, and total effects into a "TransHDM" object.

Together with SIS (Step 1) and mediation_inference (Step 2), this completes the three-step TransHDM workflow.

Usage

joint_test(inference_result, p_cutoff = 0.05, verbose = TRUE)

Arguments

Value

An object of class "TransHDM" — a list with:

• contributions: Data frame of identified mediators with columns mediator, alpha, alpha_pv, beta, beta_pv, alpha_beta, ab_pv, pa.

• effects: Data frame summarising indirect, direct, total effects and proportion mediated (pe).

• IDE_est: Named numeric vector of indirect effect estimates for all mediators (non-selected set to zero).

• DE_est: Estimated direct effect.

• all_mediators: Data frame of all SIS-screened mediators with columns mediator, alpha, alpha_pv, beta, beta_pv, significant (logical).

See Also

TransHDM, mediation_inference, SIS

Examples

set.seed(123)
target_data <- gen_simData_homo(n = 50, p_x = 3, p_m = 20, rho = 0.1)$data
source_data <- gen_simData_homo(n = 100, p_x = 3, p_m = 20, rho = 0.1,
  source = TRUE, transferable = TRUE)$data
M_vars <- paste0("M", 1:20)
X_vars <- paste0("X", 1:3)

# Full pipeline
sis <- SIS(target_data, source_data, Y = "Y", D = "D", M = M_vars,
  X = X_vars, transfer = TRUE, topN = 10)
infer <- mediation_inference(sis, M = M_vars, D = "D", X = X_vars,
  Y = "Y", transfer = TRUE)
result <- joint_test(infer)
plot(result)

K-Fold Cross-Validation Data Splitting

Description

Splits input data into k folds for cross-validation, generating training and test sets for each fold. Particularly useful for mediator selection stability assessment in high-dimensional mediation analysis.

Usage

kfold_split(data, kfold = 3)

Arguments

Value

A list containing two elements:

• train_set: List of length kfold, each element is a training subset

• test_set: List of length kfold, each element is the corresponding test subset

Fit LASSO Regression with Transfer Learning

Description

Fits a LASSO (Least Absolute Shrinkage and Selection Operator) regression model under a transfer learning framework. Supports feature selection and coefficient estimation by combining target data and source data.

Usage

lasso(target, source = NULL, transfer = FALSE, lambda = "lambda.1se")

Arguments

Value

A numeric vector coef containing LASSO coefficient estimates (including intercept).

Mediation Inference via De-biased Lasso

Description

Step 2 of the TransHDM pipeline: de-biased lasso estimation following screening (SIS, Step 1). It consists of two sub-steps:

• Step 2.1 — Outcome model (beta): estimates mediator-outcome effects by regressing the outcome Y on all retained mediators, the exposure D, and covariates X.

• Step 2.2 — Mediator models (alpha): estimates exposure-mediator effects by regressing each mediator on D and X.

Both steps support transfer learning. Results are passed to joint_test (Step 3) for multiple testing.

Usage

mediation_inference(
  screen_result = NULL,
  target_data = NULL,
  source_data = NULL,
  Y = NULL,
  D = NULL,
  M = NULL,
  X = NULL,
  transfer = FALSE,
  verbose = TRUE,
  ncore = 1
)

Arguments

Value

An object of class "mediation_inference" — a list with:

• DE_est: Estimated direct effect of D on Y.

• alpha_SIS_est: Named numeric vector of alpha estimates (exposure-mediator effects) for all mediators in M. Non-selected mediators are set to zero.

• P_alpha_SIS: Named numeric vector of p-values for alpha.

• beta_SIS_est: Named numeric vector of beta estimates (mediator-outcome effects) for all mediators in M. Non-selected mediators are set to zero.

• P_beta_SIS: Named numeric vector of p-values for beta.

• M: The full mediator name vector.

• M_SIS: Mediators retained after screening.

• D: The exposure variable name.

See Also

TransHDM, joint_test, SIS

Examples

set.seed(123)
target_data <- gen_simData_homo(n = 50, p_x = 3, p_m = 20, rho = 0.1)$data
source_data <- gen_simData_homo(n = 100, p_x = 3, p_m = 20, rho = 0.1,
  source = TRUE, transferable = TRUE)$data
M_vars <- paste0("M", 1:20)
X_vars <- paste0("X", 1:3)

# With screening (SIS)
sis <- SIS(target_data, source_data, Y = "Y", D = "D", M = M_vars,
  X = X_vars, transfer = TRUE, topN = 10)
infer <- mediation_inference(sis, transfer = TRUE)

# Without screening (use all mediators directly)
infer_ns <- mediation_inference(
  target_data = target_data, source_data = source_data,
  Y = "Y", D = "D", M = M_vars, X = X_vars,
  transfer = TRUE)

Estimate Null Proportion in Joint Mediation Analysis

Description

Estimates the proportion of null hypotheses in dual-stage mediation analysis using empirical p-value distributions. This implements the statistical framework for estimating the proportion of non-mediating variables.

Usage

null_estimation(input_pvalues, lambda = 0.5)

Arguments

Value

A list containing estimated null proportions:

• alpha00: Proportion of mediators with null effects in both exposure-mediator and mediator-outcome paths

• alpha10: Proportion with non-null exposure-mediator but null mediator-outcome effects

• alpha01: Proportion with null exposure-mediator but non-null mediator-outcome effects

• alpha1: Marginal null proportion in exposure-mediator path

• alpha2: Marginal null proportion in mediator-outcome path

Visualize TransHDM Mediation Analysis Results

Description

S3 plot method for objects of class "TransHDM". Produces a bar chart of the overall effects (indirect, direct, total, proportion mediated), individual mediator effects, or p-values.

Usage

## S3 method for class 'TransHDM'
plot(
  x,
  type = c("overall", "mediator", "pvalue", "alpha_beta"),
  fill = "white",
  color = "black",
  linewidth = 0.5,
  size.text = 3,
  show.values = TRUE,
  digits = 3,
  base_size = 12,
  label = c("significant", "all", "none"),
  ...
)

Arguments

Value

A ggplot object. The plot is printed automatically when called interactively.

Examples

set.seed(123)
target_data <- gen_simData_homo(n = 50, p_x = 3, p_m = 20, rho = 0.1)$data
source_data <- gen_simData_homo(n = 100, p_x = 3, p_m = 20, rho = 0.1,
  source = TRUE, transferable = TRUE)$data
result <- TransHDM(
  target_data = target_data, source_data = source_data,
  Y = "Y", D = "D", M = paste0("M", 1:20), X = paste0("X", 1:3),
  transfer = TRUE, ncore = 1, topN = 10
)
plot(result)
plot(result, type = "mediator")
plot(result, type = "pvalue")
plot(result, type = "alpha_beta")

Visualize Source Detection Results

Description

S3 plot method for objects of class "source_detection". Produces a bar chart of the T-index for each source dataset, with bars extending from a solid baseline at zero and a horizontal reference line at the transferability threshold. Sources whose T-index falls on or below the threshold are considered transferable.

Usage

## S3 method for class 'source_detection'
plot(
  x,
  fill_transferable = "white",
  fill_nontransferable = "white",
  color = "black",
  linewidth = 0.5,
  size.text = 3,
  show.values = TRUE,
  digits = 3,
  base_size = 12,
  threshold.linetype = "dashed",
  threshold.color = "gray30",
  ...
)

Arguments

Value

A ggplot object.

See Also

source_detection

Examples

set.seed(123)
target_data <- data.frame(
  Y  = rnorm(200), D  = rnorm(200),
  M1 = rnorm(200), M2 = rnorm(200), X1 = rnorm(200)
)
source1 <- data.frame(
  Y  = rnorm(300), D  = rnorm(300),
  M1 = rnorm(300), M2 = rnorm(300), X1 = rnorm(300)
)
source2 <- data.frame(
  Y  = rnorm(250), D  = rnorm(250),
  M1 = rnorm(250), M2 = rnorm(250), X1 = rnorm(250)
)
result <- source_detection(
  target_data = target_data,
  source_data = list(source1, source2),
  Y = "Y", D = "D", M = c("M1", "M2"), X = "X1",
  kfold = 5, verbose = FALSE
)
plot(result)

Print method for SIS objects

Description

Print method for SIS objects

Usage

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

Arguments

Value

Invisibly returns x.

Print method for TransHDM objects

Description

Print method for TransHDM objects

Usage

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

Arguments

Value

Invisibly returns x.

Print method for dblasso objects

Description

Print method for dblasso objects

Usage

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

Arguments

Value

Invisibly returns x.

Print method for lasso objects

Description

Print method for lasso objects

Usage

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

Arguments

Value

Invisibly returns x.

Print method for mediation_inference objects

Description

Print method for mediation_inference objects

Usage

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

Arguments

Value

Invisibly returns x.

Print method for source_detection objects

Description

Print method for source_detection objects

Usage

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

Arguments

Value

Invisibly returns x.

Print method for summary.dblasso objects

Description

Displays the coefficient table from a debiased Lasso model summary. Supports fixed decimal places and scientific notation formatting.

Usage

## S3 method for class 'summary.dblasso'
print(x, digits = 3, fixed = FALSE, ...)

Arguments

Value

Invisibly returns x.

Detect Transferable Source Data via Cross-Validation

Description

Determines whether external source datasets can be effectively transferred to the target data by comparing predictive performance using LASSO regression under a transfer learning framework.

Usage

source_detection(
  target_data,
  source_data = NULL,
  Y,
  D,
  M,
  X,
  kfold = 5,
  t = 0.02,
  verbose = TRUE
)

Arguments

Value

A list containing:

• transfer.source.id: Indices of source datasets deemed transferable

• source.loss: Mean validation loss for each source dataset

• target.valid.loss: Mean validation loss using target-only model

• T_index: Difference between source loss and target-only loss for each source

• threshold: Calculated transferability threshold

• loss.cv: Full k-fold cross-validation loss matrix

Examples

## Reproducible example
set.seed(123)

# Generate synthetic target data
target_data <- data.frame(
  Y  = rnorm(200),
  D  = rnorm(200),
  M1 = rnorm(200),
  M2 = rnorm(200),
  X1 = rnorm(200)
)

# Generate synthetic source data
source1 <- data.frame(
  Y  = rnorm(300),
  D  = rnorm(300),
  M1 = rnorm(300),
  M2 = rnorm(300),
  X1 = rnorm(300)
)

source2 <- data.frame(
  Y  = rnorm(250),
  D  = rnorm(250),
  M1 = rnorm(250),
  M2 = rnorm(250),
  X1 = rnorm(250)
)

# Run source detection
result <- source_detection(
  target_data = target_data,
  source_data = list(source1, source2),
  Y = "Y",
  D = "D",
  M = c("M1", "M2"),
  X = "X1",
  kfold = 5,
  t = 0.05,
  verbose = FALSE
)

# Get Summary
summary(result)

# Transferable source indices
result$transfer.source.id

# Compare validation losses
data.frame(
  Source = c(paste0("Source", seq_along(result$source.loss)), "Target"),
  Loss   = c(result$source.loss, result$target.valid.loss)
)

Summary of TransHDM Mediation Analysis

Description

Summary of TransHDM Mediation Analysis

Usage

## S3 method for class 'TransHDM'
summary(object, top = 10, digits = 4, ...)

Arguments

Value

An object of class "summary.TransHDM".

Summary of Debiased Lasso Inference

Description

Summary of Debiased Lasso Inference

Usage

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

Arguments

Value

An object of class "summary.dblasso".

Summary of Lasso Regression

Description

Summary of Lasso Regression

Usage

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

Arguments

Value

An object of class "summary.lasso".

Summary of Mediation Inference

Description

Summary of Mediation Inference

Usage

## S3 method for class 'mediation_inference'
summary(object, top = 10, digits = 4, ...)

Arguments

Value

An object of class "summary.mediation_inference".

Summary of Source Detection Results

Description

Summary of Source Detection Results

Usage

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

Arguments

Value

An object of class "summary.source_detection".