computePCEV
computes the first PCEV and tests its significance.
1 2 3 
response 
A matrix of response variables. 
covariate 
An array or a data frame of covariates. 
confounder 
An array or data frame of confounders. 
estimation 
Character string specifying which estimation method to use:

inference 
Character string specifying which inference method to use:

index 
If 
shrink 
Should we use a shrinkage estimate of the residual variance?
Default value is 
nperm 
The number of permutations to perform if 
Wilks 
Should we use a Wilks test instead of Roy's largest test? This is only implemented for a single covariates. 
This is the main function. It computes the PCEV using either the classical method or the block approach. A pvalue is also computed, testing the significance of the PCEV.
The pvalue is computed using either a permutation approach or an exact test. The implemented exact tests use Wilks' Lambda (only for a single covariate) or Roy's Largest Root. The latter uses Johnstone's approximation to the null distribution. Note that this test is also available for the block approach, but there is no theoretical guarantee that it works, and the resulting pvalue should therefore be compared to that obtained using a permutation procedure.
An object of class Pcev
containing the first PCEV, the
pvalue, the estimate of the shrinkage factor, etc.
1 2 3 4 5  set.seed(12345)
Y < matrix(rnorm(100*20), nrow=100)
X < rnorm(100)
pcev_out < computePCEV(Y, X)
pcev_out2 < computePCEV(Y, X, shrink = TRUE)

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