checkArgs: Check Arguments passed to the scPCA Function
In scPCA: Sparse Contrastive Principal Component Analysis

Description Usage Arguments Value References

Checks whether or not the all arguments in the scPCA functions are input properly.

checkArgs(
  target,
  background,
  center,
  scale,
  n_eigen,
  contrasts,
  penalties,
  clust_method,
  linkage_method,
  clusters,
  eigdecomp_tol,
  eigdecomp_iter,
  n_centers
)

`target`	The target (experimental) data set, in a standard format such as a `data.frame` or `matrix`.
`background`	The background data set, in a standard format such as a `data.frame` or `matrix`.
`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.
`n_eigen`	A `numeric` indicating the number of eigenvectors to be computed.
`contrasts`	A `numeric` vector of the contrastive parameters.
`penalties`	A `numeric` vector of the penalty terms.
`clust_method`	A `character` specifying the clustering method to use for choosing the optimal constrastive parameter. Currently, this is limited to either k-means, partitioning around medoids (PAM), and hierarchical clustering. The default is k-means clustering.
`linkage_method`	A `character` specifying the agglomerative linkage method to be used if `clust_method = "hclust"`. The options are `ward.D2`, `single`, `complete`, `average`, `mcquitty`, `median`, and `centroid`. The default is `complete`.
`clusters`	A `numeric` vector of cluster labels for observations in the `target` data. Defaults to `NULL`, but is otherwise used to identify the optimal set of hyperparameters when fitting the scPCA and the automated version of cPCA.
`eigdecomp_tol`	A `numeric` providing the level of precision used by eigendecompositon calculations.
`eigdecomp_iter`	A `numeric` indicating the maximum number of interations performed by eigendecompositon calculations.
`n_centers`	A `numeric` giving the number of centers to use in the clustering algorithm. If set to 1, cPCA, as first proposed by \insertCiteerichson2018sparse;textualscPCA, is performed, regardless of what the `penalties` argument is set to.