CF_Rsq.measure {RsqMed}

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")

}