Description Usage Arguments Value Author(s) References See Also Examples

The test is based on the Huber loss function and using the parametric bootstrap for inference (i.e. bootstrapping residuals).

1 |

`Y` |
a vector of quantitative traits (QTs). |

`X` |
Genotype or other data; each row for a subject, and each column for an SNP (or a predictor). The value of each SNP is the # of the copies for an allele. A matrix with dimension n by k (n : number of observation, k : number of SNPs (or predictors) ). |

`cov` |
Covariates. A matrix with dimension n by k2 (n :number of observation, k2 : number of covariates). |

`pow` |
power used in SPUr test. A vector of the powers. |

`B` |
number of bootstraps. |

`C` |
Constant in huber loss function. C = 1.345 is chosen to maintain a high efficiency for a Normal error. |

p-values of the SPUr tests in the order of supplied pow values; finally, the p-value of the aSPUr test (that combines the SPUs tests with pow by taking their min P-value and adjust for multiple testing).

Yiwei Zhang and Wei Pan

Peng Wei, Ying Cao, Yiwei Zhang, Zhiyuan Xu, Il-Youp Kwak, Eric Boerwinkle, Wei Pan (2016) On Robust Association Testing for Quantitative Traits and Rare Variants, G3, 6(12) 3941-3950.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(exdat)
## example analysis using aSPU test on exdat data.
QT <- jitter(exdat$Y)
out <- aSPUr(Y = QT, X = exdat$X, cov = NULL, B = 100)
out
## This is a vector of p-values for SPUr and aSPUr tests.
## SPU1 to SPUInf corresponds with the option pow=c(1:8, Inf)
## They are p-values for corresponding SPUr tests.
## The last element is p-value of aSPUr test.
``` |

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