RKUM (version 0.1.1.1)

snpfmridata: 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

%% ...

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

# NOT RUN {
##Dummy data:

n<-100

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