Description Usage Arguments Details Value See Also Examples
computePCEV
computes the first PCEV and tests its significance.
1 2 3 4 |
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 |
na_action |
how NAs are treated. The default is to raise an error. See details. |
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 p-value is also computed, testing the significance of the PCEV.
The p-value 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 p-values obtained from a permutation procedure are available.
When estimation = "singular"
, the p-value is computed using a
heuristic: using the method of moments and a small number of permutations
(i.e. 25), a location-scale family of the Tracy-Widom distribution of order 1
is fitted to the null distribution. This fitted distribution is then used to
compute p-values.
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.
By default, missing values are not allowed. This can be relaxed with
na_action
. If na_action = "omit"
, then all rows with at least
one missing value will be removed from response
before computation. If
na_action = "column"
, then the estimation of the linear model
parameters is done column-wise with the non-missing value. This approach
maximises the information. Note that missing values are still not allowed in
covariate
and confounder
.
An object of class Pcev
containing the first PCEV, the
p-value, 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.