aSPUr: Robust Sum of powered score (SPU) tests and aSPU test for a...

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

Description

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

Usage

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aSPUr(Y, X, cov = NULL, pow = c(1:8, Inf), B = 1000, C = 1.345)

Arguments

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.

Value

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).

Author(s)

Yiwei Zhang and Wei Pan

References

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.

See Also

aSPU

Examples

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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.

aSPU documentation built on May 1, 2019, 7:04 p.m.