hilma: Estimation and Inference for High Dimensional Mediation...

In freebird: Estimation and Inference for High Dimensional Mediation and Surrogate Analysis

Estimation and Inference for High Dimensional Mediation Analysis

Description

This function implements the estimation and inference for the indirect effect in high dimensional linear mediation analysis models. It provides estimates and p-values under both incomplete mediation, where a direct effect may exist, as well as complete mediation, where the direct effect is known to be absent.

Usage

hilma(
  Y,
  G,
  S,
  mediation_setting = "incomplete",
  tuning_method = "uniform",
  lam_list = NA,
  min.ratio = 0.1,
  n.lambda = 5,
  center = TRUE
)

Arguments

Y	The n-dimensional outcome vector.
G	The n by p mediator matrix. p can be larger than n.
S	The n by q exposure matrix. q can be 1, and q < n is required.
mediation_setting	Either ‘incomplete’ or ‘complete’
tuning_method	‘uniform’ or ‘aic’, the default is ‘uniform’
lam_list	tuning parameter for uniform tuning or list of tuning parameter for aic tuning
min.ratio	the ratio of the minimum lambda to the maximum
n.lambda	number of tuning parameters to choose from
center	center the data or not, the default is TRUE

Value

A list with components:

beta_hat	estimated indirect effect
alpha1_hat	estimated direct effect
pvalue_beta_hat	the p value for testing the significance of the indirect effect
lambda_used	lambda used during optimization

Author(s)

Ruixuan Zhou

Examples

n = 30
p = 50
q = 2
G = MASS::mvrnorm(n, rep(0,p), diag(p))
S = as.matrix(MASS::mvrnorm(n, rep(0,q), diag(q)))
Y = as.matrix(rnorm(n))
out = hilma(Y,G,S, mediation_setting = 'complete', tuning_method = 'uniform', lam_list = 0.2)
out

freebird documentation built on Sept. 27, 2022, 5:05 p.m.