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.
Usage
getbetap.A.2(x,y,z=NULL)
Arguments
x
matrix (m\timesn) 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.