aGE {aGE} | R Documentation |

aGE interaction test

aGE(Y, G, cov = NULL, model = c("gaussian", "binomial"), pow = c(1:6), n.perm = 1000, method = "Simulation", nonparaE = F, DF = 10, stepwise = T)

`Y` |
a numeric vector of phenotype values |

`G` |
a matrix for all RVs in the test gene or genomic region. The order of rows must match the order of Y. Missing is imputed as 0. |

`cov` |
a matrix with first column as the environmental variable to be tested. The order of rows must match the order of Y. |

`model` |
"binomial" for binary traits or "gaussian" for quantitative traits. |

`pow` |
Gamma set used to build a family of tests, default=c(1:6) for rare variants |

`n.perm` |
number of simulation to calculate the p-values, default=1000. Can increase to higher value depending on the signficiance level. |

`method` |
only have one option: "Simulation", also called Monte Carlo Method. |

`nonparaE` |
"T": use cubic splines for the environmental variable to fit the model; "F": use a linear function of the environmental variable to fit the model |

`DF` |
degree of freedom to use in the cubic splines, default=10. This option only works when nonparaE is set to "T" |

`stepwise` |
an option to speed up the simulation procedure for large n.perm number in real-data application. Up to $n.perm=10^8$ |

p-values

{ set.seed(12345) phenotype <- c(rep(1,50),rep(0,50)) genotype <- data.frame(g1=sample(c(rep(1,10),rep(0,90))),g2=sample(c(rep(1,5), rep(0,95)))) covariates <- data.frame(Envir=rnorm(100), Age=rnorm(100,60,5)) exD <- list(Y=phenotype, G=genotype, X=covariates) aGE(Y=exD$Y, G=exD$G, cov=exD$X, model='binomial', nonparaE=FALSE, stepwise=FALSE) }

[Package *aGE* version 0.0.9 Index]