Description Usage Arguments Details Value See Also Examples
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 
Only used 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 covariate and with 
This is the main function. It computes the PCEV using either the classical method, block approach or singular. 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 for the block approach, only pvalues obtained from a permutation procedure are available.
When estimation = "singular"
, the pvalue is computed using a heuristic: using the method
of moments and a small number of permutations (i.e. 25), a locationscale family of the
TracyWidom distribution of order 1 is fitted to the null distribution. This fitted distribution
is then used to compute pvalues.
When estimation = "block"
, there are three different ways of specifying the blocks: 1) if
index
is a vector of the same length as the number of columns in response
, then it
is used to match each response to a block. 2) If index
is a single positive integer, it is
understood as the number of blocks, and each response is matched to a block randomly. 3) If
index = "adaptive"
(the default), the number of blocks is chosen so that there are about
n/2 responses per block, and each response is match to a block randomly. All other values of
index
should result in an error.
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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.