fitGrid {scPCA} | R Documentation |
Identify the Optimal Contrastive and Penalty Parameters
Description
This function is used to automatically select the optimal
contrastive parameter and L1 penalty term for scPCA based on a clustering
algorithm and average silhouette width.
Usage
fitGrid(target, target_valid = NULL, center, scale, c_contrasts,
contrasts, penalties, n_eigen, clust_method = c("kmeans", "pam"),
n_centers, max_iter = 10)
Arguments
target |
The target (experimental) data set, in a standard format such
as a data.frame or matrix .
|
target_valid |
A holdout set of the target (experimental) data set, in a
standard format such as a data.frame or matrix . NULL by
default but used by cvSelectParams for cross-validated
selection of the contrastive and penalization parameters.
|
center |
A logical indicating whether the target and background
data sets should be centered to mean zero.
|
scale |
A logical indicating whether the target and background
data sets should be scaled to unit variance.
|
c_contrasts |
A list of contrastive covariances.
|
contrasts |
A numeric vector of the contrastive parameters used
to compute the contrastive covariances.
|
penalties |
A numeric vector of the penalty terms.
|
n_eigen |
A numeric indicating the number of eigenvectors to be
computed.
|
clust_method |
A character specifying the clustering method to
use for choosing the optimal constrastive parameter. Currently, this is
limited to either k-means or partitioning around medoids (PAM). The default
is k-means clustering.
|
n_centers |
A numeric giving the number of centers to use in the
clustering algorithm.
|
max_iter |
A numeric giving the maximum number of iterations to
be used in k-means clustering, defaulting to 10.
|
Value
A list similar to that output by prcomp
:
rotation - the matrix of variable loadings
x - the rotated data, centred and scaled, if requested, data
multiplied by the rotation matrix
contrast - the optimal contrastive parameter
penalty - the optimal L1 penalty term
[Package
scPCA version 1.0.0
Index]