perm_kCCA: Calculation of Strength of the Connectivity among Multiple...

Description Usage Arguments Details Value Author(s) References

View source: R/perm_kCCA.R

Description

This function is the core part for kernel canonical correlation analysis. Generally you do not need to use this function unless you are famaliar with kcca algorithm.

Usage

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perm_kCCA(x, y, sig = 0.1, gama = 0.1, ncomps = 1, permNum = 50,
  kernel = "rbfdot")

perm_kCCA_par(x, y, sig = 0.1, gama = 0.1, ncomps = 1, permNum = 500,
  kernel = "rbfdot")

Arguments

x

region 1, a matrix containing data index by row.

y

region 2, a matrix containing data index by row.

sig

inverse kernel width for the Radial Basis kernel function "rbfdot" and the Laplacian kernel "laplacedot".

gama

regularization parameter (default: 0.1).

ncomps

number of canonical components (default: 1).

permNum

number of permutation (default 50).

kernel

type of kernel.

Details

Kernel canonical correlation analysis (KCCA) can explore the nonlinear relationship between two variables. It transformed sample vectors into the Hilbert space and maximize correlation coefficient by solving quadratically regularized Lagrangean function. Refer to Kang's paper for more details: Kang J, Bowman FD, Mayberg H, Liu H (2016). "A depression network of functionallyconnected regions discovered via multi-attribute canonical correlation graphs."NeuroImage,141, 431-441.

Value

(lists of) list of region index, p-value, region type ("two" or "multiple"), and region name.

Author(s)

Xubo Yue, Chia-Wei Hsu (tester), Jian Kang (maintainer)

References

https://www.ncbi.nlm.nih.gov/pubmed/27474522


neuroconductor/brainKCCA documentation built on Feb. 9, 2020, 10:23 a.m.