RKUM (version 0.1.1.1)

snpfmrimth3D: An example of imaging genetics and epi-genetics data to calcualte influential observations from three view data

Description

#A function

Usage

snpfmrimth3D(n = 500, gamma = 1e-05, ncomps = 1, 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

IFim

Influence value of multiple kernel canonical correlation analysis for the ideal data

IFcm

Influence value of multiple kernel 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, snpfmridata, ifrkcca

Examples

# NOT RUN {
##Dummy data:

n<-100

snpfmrimth3D(n, 0.00001,  10, 1)
# }