coef_boot: Percentile Bootstrap Estimation of canonical correlation...

In giac01/ccatools: Tools for Canonical Correlation Analysis

Description

This function runs the .cca Canonical Correlation Analysis function multiple times to assess variability in the CCA loadings and canonical correlations Because bootstrap resampling can change the order of canonical variates that are extracted, or sign flipping can occur in some cases (i.e. a very similar latent variable is extracted but on some occasions the loadings are mostly positive or negative), we rotate the loadings in each bootstrap resample to map onto the loadings generated from the full, raw input datsets.

Usage

coef_boot(
  X_FIT,
  Y_FIT,
  ncomp = 10,
  Nboot = 30,
  ProcrustX = NULL,
  ProcrustY = NULL
)

Arguments

X_FIT	Numeric Matrix or Data Frame [N, P1] containing the predictor variables.
Y_FIT	Numeric Matrix or Data Frame [N, P2] containing the outcome variables.
ncomp	Numeric Scalar. Number of CCA components to keep in analyses. Must be equal to or less than min(P1,P2).
Nboot	Numeric Scaler. Number of times to repeat bootstrap resampling.
ProcrustX	Numeric Matrix [ncomp, P1] containing target matrix for Procrustes Analysis. All CCA predictor raw coefficients obtained during the bootstrap resampling will be rotated to this target matrix.
ProcrustY	Numeric Matrix [ncomp, P2] containing target matrix for Procrustes Analysis. All CCA outcome raw coefficients obtained during the bootstrap resampling will be rotated to this target matrix.

giac01/ccatools documentation built on July 15, 2021, 4:33 a.m.