Description Usage Arguments Value References Examples

`hima`

is used to estimate and test high-dimensional mediation effects.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |

`X` |
a vector of exposure. |

`Y` |
a vector of outcome. Can be either continuous or binary (0-1). |

`M` |
a |

`COV.XM` |
a |

`COV.MY` |
a |

`family` |
either 'gaussian' or 'binomial', depending on the data type of outcome ( |

`penalty` |
the penalty to be applied to the model. Either 'MCP' (the default), 'SCAD', or
'lasso'. See |

`topN` |
an integer specifying the number of top markers from sure independent screening.
Default = |

`parallel` |
logical. Enable parallel computing feature? Default = |

`ncore` |
number of cores to run parallel computing Valid when |

`verbose` |
logical. Should the function be verbose? Default = |

`...` |
other arguments passed to |

A data.frame containing mediation testing results of selected mediators.

alpha: coefficient estimates of exposure (X) –> mediators (M).

beta: coefficient estimates of mediators (M) –> outcome (Y) (adjusted for exposure).

gamma: coefficient estimates of exposure (X) –> outcome (Y) (total effect).

alpha*beta: mediation effect.

% total effect: alpha*beta / gamma. Percentage of the mediation effect out of the total effect.

Bonferroni.p: statistical significance of the mediator (Bonferroni procedure).

BH.FDR: statistical significance of the mediator (Benjamini-Hochberg procedure).

Zhang H, Zheng Y, Zhang Z, Gao T, Joyce B, Yoon G, Zhang W, Schwartz J, Just A, Colicino E, Vokonas P, Zhao L, Lv J, Baccarelli A, Hou L, Liu L. Estimating and Testing High-dimensional Mediation Effects in Epigenetic Studies. Bioinformatics. 2016. DOI: 10.1093/bioinformatics/btw351. PubMed PMID: 27357171.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
n <- 200 # sample size
p <- 200 # the dimension of covariates
# the regression coefficients alpha (exposure --> mediators)
alpha <- rep(0, p)
# the regression coefficients beta (mediators --> outcome)
beta1 <- rep(0, p) # for continuous outcome
beta2 <- rep(0, p) # for binary outcome
# the first four markers are true mediators
alpha[1:4] <- c(0.45,0.5,0.6,0.7)
beta1[1:4] <- c(0.55,0.6,0.65,0.7)
beta2[1:4] <- c(1.45,1.5,1.55,1.6)
# these are not true mediators
alpha[7:8] <- 0.5
beta1[5:6] <- 0.8
beta2[5:6] <- 1.7
# Generate simulation data
simdat_cont = simHIMA(n, p, alpha, beta1, seed=1029)
simdat_bin = simHIMA(n, p, alpha, beta2, binaryOutcome = TRUE, seed=1029)
# Run HIMA with MCP penalty by default
# When Y is continuous (default)
hima.fit <- hima(simdat_cont$X, simdat_cont$Y, simdat_cont$M, verbose = TRUE)
hima.fit
# When Y is binary (should specify family)
hima.logistic.fit <- hima(simdat_bin$X, simdat_bin$Y, simdat_bin$M,
family = "binomial", verbose = TRUE)
hima.logistic.fit
``` |

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