CF_Rsq.measure: Function to calculate the Rsq function as a total effect size...

In RsqMed: Total Mediation Effect Size Measure for High-Dimensional Mediators

Function to calculate the Rsq function as a total effect size measure for mediation effect using cross-fitted estimation

Description

Function to calculate the Rsq function as a total effect size measure for mediation effect using cross-fitted estimation

Usage

CF_Rsq.measure(
  Y,
  M,
  Covar = NULL,
  X,
  iter.max = 3,
  nsis = NULL,
  first.half = TRUE,
  seed = 2022,
  tune = c("aic", "bic"),
  penalty = c("MCP", "lasso")
)

Arguments

Y	vector of the outcome of interest; outcome has to follow a Gaussian distribution.
M	matrix of putative mediators
Covar	covariates matrix
X	vector of the independent variable of interest, e.g. environmental variable
iter.max	Maximum number of iterations used in iSIS, default = 3 (details see the SIS package).
nsis	Number of predictors recruited by iSIS, default = NULL
first.half	TRUE: split sample into two halves by the order in the dataset. FALSE: randomly split samples into halves, default = TRUE.
seed	Random seed used for sample splitting, default = 2022.
tune	Method for tuning the regularization parameter of the penalized likelihood subproblems and of the final model selected by (i)SIS. Options include tune = 'bic' and tune = 'aic'.
penalty	The penalty to be applied in the regularized likelihood subproblems. 'MCP', and 'lasso' are provided. 'MCP' is recommended.

Value

Output Vector consisting of Rsq mediated(Rsq.mediated), Lower confidence bound constructed by the asymptotic variance (CI_asym_low), Upper confidence bound constructed by the asymptotic variance (CI_asym_up), Lower confidence bound constructed by the conservative variance (CI_cons_low), Upper confidence bound constructed by the conservative variance (CI_cons_up), number of selected mediators in subsample 1 (pab1), number of selected mediators in subsample 2 (pab2), and the Rsq that used to calculate the Rsq measure: variance of outcome explained by mediator (Rsq.YM), variance of outcome explained by the independent variable (Rsq.YX), and variance of outcome explained by mediator and independent variable (Rsq.YMX); Sample Size in analysis (Sample Size)

Name of selected mediators in subsample 1 (select1)

Name of selected mediators in subsample 2 (select2)

Examples

{

data(example)
attach(example)
CF_Rsq.measure(Y=Y, M=M, X=X, tune = "bic", penalty = "MCP")

}

RsqMed documentation built on Nov. 15, 2023, 1:07 a.m.