Description Usage Arguments Author(s) References See Also
Perform MCC_1 by successively considering each of the n samples as a potential outlier. Otherwise the syntax and output are the same as getbetap.A
.
1 | getbetap.A.2(x,y,z=NULL)
|
x |
matrix (m \times n) of predictors. |
y |
clinical/experimental n-vector. |
z |
covariate n-vector, assumed discrete with at least two observations per value of z. If z is not provided, the function assumes no covariates. Generates the first 4 moments of pearson correlation under permutation of A=∑_{k}^K ∑_i(x_{ik} y_{π[i]k}), given K covariate classes defined by z. getAkmoment provides the results for the samples in stratum k. |
Yi-Hui Zhou: yihui_zhou@ncsu.edu
Yi-Hui Zhou, Fred Wright, 2013, Fast And Robust Association Testing For High-Throughput Testing, Submitted.
See also the vignette included with this package.
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