snpfmridata: An example of imaging genetics data to calcualte influential...

In RKUM: Robust Kernel Unsupervised Methods

An example of imaging genetics data to calcualte influential observations from two view data

Description

#A function

Usage

snpfmridata(n = 300, gamma=0.00001, ncomps = 2, jth = 1)

Arguments

n	the sample size
gamma	the hyper-parameters
ncomps	the number of canonical vectors
jth	the influence function of the jth canonical vector

Value

IFCCAID	Influence value of canonical correlation analysis for the ideal data
IFCCACD	Influence value of canonical correlation analysis for the contaminated data
IFKCCAID	Influence value of kernel canonical correlation analysis for the ideal data
IFKCCACD	Influence value of kernel canonical correlation analysis for the contaminated data
IFHACCAID	Influence value of robsut (Hampel's loss) canonical correlation analysis for the ideal data
IFHACCACD	Influence value of robsut (Hampel's loss) canonical correlation analysis for the contaminated data
IFHUCCAID	Influence value of robsut (Huber's loss) canonical correlation analysis for the ideal data
IFHUCCACD	Influence value of robsut (Huber's loss) canonical correlation analysis for the contaminated data

Author(s)

Md Ashad Alam <[e-mail]>

References

Md Ashad Alam, Kenji Fukumizu and Yu-Ping Wang (2018), Influence Function and Robust Variant of Kernel Canonical Correlation Analysis, Neurocomputing, Vol. 304 (2018) 12-29.

Md Ashad Alam, Vince D. Calhoun and Yu-Ping Wang (2018), Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics, Computational Statistics and Data Analysis, Vol. 125, 70- 85

See Also

See also as rkcca, ifrkcca, snpfmrimth3D

Examples

##Dummy data:

n<-100

snpfmridata(n, 0.00001,  10, jth = 1)

RKUM documentation built on June 22, 2022, 9:06 a.m.