Description Usage Arguments Value Examples

Function to calculate the Rsq function as a total mediation effect size measure (Gaussian outcome only). If method='iSIS', a two-step procedure is performed, where the first step filters the non-mediators based on part of the data and the second step calculates the point estimates for Rsq using random-effect models on the remaining data. If method='ALL', Rsq is calculated based on all subjects and variables.

1 2 3 | ```
Rsq.measure(p = 1/2, outcome, med, covar, indp, method = c("iSIS",
"ALL"), iter.max = 10, nsis = NULL, init.cutoff = 0.1,
screening = FALSE)
``` |

`p` |
Proportion of the training dataset for selecting mediators regarding the whole dataset, default is set as 1/2. If method='ALL', keep p at default. |

`outcome` |
Vector of outcome type of interest; Only Gaussian distributed outcome is accepted. |

`med` |
Matrix of putative mediators |

`covar` |
Covariate matrix |

`indp` |
Vector of the independent variable of interest, e.g. environmental variable |

`method` |
Method used to screen out non-mediators. When no variable selection is required, method='ALL'; otherwise, iterative sure independence screening (SIS) is used for variable selection, i.e., method='iSIS'. Note that when method='ALL', no screening is performed, i.e., the Rsq measure is calculated on all data and all variables included. |

`iter.max` |
Maximum number of iteration used in iSIS, default=10 (please refer the SIS package for detail explanation) |

`nsis` |
Number of variables recruited by iterative SIS |

`init.cutoff` |
The percentage of mediators remaining after the screening step. |

`screening` |
T if filtering mediators based on the strength of independent variable and mediators as a preprocessing step; F if all putative mediators are included, default=F. |

Output vector consist of Rsq mediated(Rsq.mediated), shared over simple effects (SOS), number of selected mediators (pab), 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), n is the sample size based on which the random effect models are fitted.

Name of selected mediators (select)

1 2 3 4 5 6 7 | ```
{
#\donttest{
data(example)
attach(example)
Rsq.measure(p=1/2, outcome=Y, med=M,covar=Cov,indp=X,method='iSIS', iter.max=1)
#}
}
``` |

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