Package 'probmed'

Re-export extract_mediation from medfit

Description

Generic function to extract mediation structure from fitted models. This function is provided by the medfit package and re-exported here for convenience.

Arguments

object	Fitted model object
...	Additional arguments passed to methods

Value

A medfit::MediationData object

See Also

extract_mediation for full documentation

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Plot Bootstrap Distribution

Description

Plot Bootstrap Distribution

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Compute P_med: Probabilistic Effect Size for Mediation

Description

Compute $P_{med}$, a scale-free probabilistic effect size for mediation analysis, along with the traditional Indirect Effect ($a \times b$). Provides point estimates and bootstrap confidence intervals.

$P_{med}$ represents $P(Y(x, M(x)) > Y(x, M(x^*))) + \tfrac12 P(Y(x, M(x)) = Y(x, M(x^*)))$: the probability that the outcome under treatment with the mediator at its treated level exceeds the outcome under treatment with the mediator at its control level (a one-half correction is added for ties). Both potential outcomes hold treatment at $x$, so the direct effect cancels and $P_{med}$ reflects mediation only; values $> 0.5$ indicate positive mediation.

Usage

pmed(object, ...)

Arguments

object	Either: A formula for the outcome model (most common) A medfit::MediationData object from extract_mediation()
...	Additional arguments passed to methods (see method documentation)

Value

PmedResult object containing:

• estimate: P_med point estimate

• ci_lower, ci_upper: Confidence interval bounds

• ie_estimate: Indirect Effect point estimate

• ie_ci_lower, ie_ci_upper: IE confidence interval

• boot_estimates: Bootstrap distribution (if applicable)

• method: Inference method used

Methods

Available inference methods via method argument:

• "parametric_bootstrap" (default): Fast, assumes normality

• "nonparametric_bootstrap": Robust to assumptions, slower

• "plugin": Point estimate only, no CI

See Also

extract_mediation for extracting from fitted models

Examples

# Basic example with formula interface
set.seed(123)
n <- 200
data <- data.frame(X = rnorm(n), C = rnorm(n))
data$M <- 0.5 * data$X + 0.3 * data$C + rnorm(n)
data$Y <- 0.4 * data$M + 0.2 * data$X + 0.2 * data$C + rnorm(n)

# Compute with parametric bootstrap
result <- pmed(
  Y ~ X + M + C,
  formula_m = M ~ X + C,
  data = data,
  treatment = "X",
  mediator = "M",
  method = "parametric_bootstrap",
  n_boot = 500
)
print(result)

## Not run: 
# View bootstrap distribution
summary(result)
plot(result)

## End(Not run)

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Compute P_med from Formula

Description

Compute P_med from Formula

Arguments

object	Formula for outcome model (Y ~ X + M + C)
formula_m	Formula for mediator model (M ~ X + C)
data	Data frame
treatment	Character: treatment variable name
mediator	Character: mediator variable name
family_y	Family for outcome model (default: gaussian())
family_m	Family for mediator model (default: gaussian())
x_ref	Reference treatment value (default: 0)
x_value	Treatment value (default: 1)
method	Inference method: "parametric_bootstrap", "nonparametric_bootstrap", "plugin", "mbco"
n_boot	Number of bootstrap samples (default: 1000)
ci_level	Confidence level (default: 0.95)
seed	Random seed for reproducibility
...	Additional arguments

Details

method = "mbco" returns a Model-Based Constrained Optimization interval (Tofighi & Kelley, 2020): a likelihood-ratio interval for P_med and for the indirect effect a*b, obtained by inverting the constrained-likelihood test rather than by resampling. It is deterministic (no n_boot, no seed) and supports a Gaussian outcome and mediator, with covariates, and any treatment contrast x_ref != x_value. For binary or other non-Gaussian models, use the bootstrap methods.

For method = "mbco", the converged flag reflects the P_med interval only. The indirect-effect interval is reported separately and may be NA on a degenerate design (e.g. a non-finite delta-method scale for a*b) even when the P_med interval converges; check ie_ci_lower / ie_ci_upper directly.

Value

PmedResult object

Examples

# Toy example: Simple mediation model
# Generate data where X affects Y through M
set.seed(123)
n <- 100
data <- data.frame(
  X = rnorm(n),
  C = rnorm(n)
)
data$M <- 0.5 * data$X + 0.3 * data$C + rnorm(n, sd = 0.5)
data$Y <- 0.4 * data$M + 0.2 * data$X + 0.2 * data$C + rnorm(n, sd = 0.5)

# Compute P_med using plugin estimator (fast, no CI)
result_plugin <- pmed(
  Y ~ X + M + C,
  formula_m = M ~ X + C,
  data = data,
  treatment = "X",
  mediator = "M",
  method = "plugin"
)
print(result_plugin)


# With parametric bootstrap for confidence intervals
result_boot <- pmed(
  Y ~ X + M + C,
  formula_m = M ~ X + C,
  data = data,
  treatment = "X",
  mediator = "M",
  method = "parametric_bootstrap",
  n_boot = 200,  # Use more (e.g., 1000+) in practice
  seed = 456
)
print(result_boot)

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Compute P_med from MediationData

Description

Compute P_med from MediationData

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Compute parallel (joint) P_med from a ParallelMediationData object

Description

Computes the joint P_med for k parallel mediators (all moved together from their control to their treated levels). Gaussian outcome and mediators only. See vignette("parallel-mediation", package = "probmed") for the estimand.

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P_med Result Class

Description

S7 class for P_med computation results.

Usage

PmedResult(
  estimate = integer(0),
  ci_lower = integer(0),
  ci_upper = integer(0),
  ci_level = integer(0),
  method = character(0),
  n_boot = NA_integer_,
  boot_estimates = numeric(0),
  ie_estimate = NA_real_,
  ie_ci_lower = NA_real_,
  ie_ci_upper = NA_real_,
  ie_boot_estimates = numeric(0),
  x_ref = integer(0),
  x_value = integer(0),
  source_extract = NULL,
  converged = logical(0),
  call = NULL
)

Arguments

estimate	Numeric: P_med point estimate
ci_lower	Numeric: lower bound of confidence interval
ci_upper	Numeric: upper bound of confidence interval
ci_level	Numeric: confidence level (e.g., 0.95)
method	Character: inference method used
n_boot	Integer: number of bootstrap samples (NA if not bootstrap)
boot_estimates	Numeric vector: bootstrap distribution (empty if not bootstrap)
ie_estimate	Numeric: Indirect Effect (NIE) point estimate
ie_ci_lower	Numeric: lower bound of NIE confidence interval
ie_ci_upper	Numeric: upper bound of NIE confidence interval
ie_boot_estimates	Numeric vector: bootstrap distribution of NIE
x_ref	Numeric: reference treatment value
x_value	Numeric: treatment value for contrast
source_extract	medfit::MediationData object: source of the estimates
converged	Logical: did computation converge
call	Call object: original function call

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Print PmedResult

Description

Print PmedResult

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Summary Method

Description

Summary Method

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