snpfmridata | R Documentation |
#A function
snpfmridata(n = 300, gamma=0.00001, ncomps = 2, jth = 1)
n |
the sample size |
gamma |
the hyper-parameters |
ncomps |
the number of canonical vectors |
jth |
the influence function of the jth canonical vector |
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 |
Md Ashad Alam <malam@tulane.edu>
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 as rkcca
, ifrkcca
, snpfmrimth3D
##Dummy data: n<-100 snpfmridata(n, 0.00001, 10, jth = 1)
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