Main workhorse of dcca_factor — .tiltedCCA_common

Given the two matrices (given by svd_1 and svd_2) and the CCA solution in cca_res, compute the common scores. This calls the functions .common_decomposition and .compute_distinct_score.

Usage

.tiltedCCA_common_score(
  averaging_mat,
  cca_res,
  discretization_gridsize,
  enforce_boundary,
  fix_tilt_perc,
  snn_bool_cosine,
  snn_bool_intersect,
  snn_k,
  snn_min_deg,
  snn_num_neigh,
  svd_1,
  svd_2,
  target_dimred,
  verbose = 0
)

Arguments

averaging_mat: sparse matrix
cca_res: returned object from .cca
discretization_gridsize: positive integer for how many values between 0 and 1 (inclusive) to search the appropriate amount of tilt over
enforce_boundary: boolean, on whether or not the tilt is required to stay between the two canonical score vectors
fix_tilt_perc: boolean or a numeric. If FALSE, then the tilt is adaptively determined, and if TRUE, then the tilt is set to be equal to 0.5. If numeric, the value should be between 0 and 1, which the tilt will be set to.
snn_bool_cosine: boolean
snn_bool_intersect: boolean
snn_k: integer
snn_min_deg: integer
snn_num_neigh: integer
svd_1: SVD of the denoised variant of mat_1 from dcca_factor
svd_2: SVD of the denoised variant of mat_2 from dcca_factor
target_dimred: matrix
verbose: non-negative integer

Value

list