Description Usage Arguments Value References Examples

seedCCA is to conduct CCA when the maximum of the dimensions of the two sets of variables is greater than the sample sizes.

1 |

`first.set` |
numeric matrix (n * p), the first set of variables |

`second.set` |
numeric matrix (n * r), the second set of variables |

`u1` |
the termination index of projections for the first set of variables |

`u2` |
the termination index of projections for the second set of variables |

`case1` |
logical If FALSE initialized CCA are conducted for both variables. |

`num.d` |
numeric, the number of the "num.d" largest eigenvectors of cov(first.set, second.set) and cov(second.set, first.set), if case1=FALSE. The default value is equal to 4. This option does not work, if case1=TRUE. |

`initialMX0` |
the initialized canonical coefficient matrices of the first set of variables |

`initialMY0` |
the initialized canonical coefficient matrices of the second set of variables |

`newX` |
the initially-CCAed first set of variables) |

`newY` |
the initially-CCAed second set of variables |

`xcoef` |
the estimated canonical coefficients for the first set of variables |

`ycoef` |
the estimated canonical coefficients for the second set of variables |

`Xcanvar` |
the estimated canonical variates for the first set of variables |

`Ycanvar` |
the estimated canonical variates for the second set of varialbes |

`eigenvalue` |
the two sets of canonical correlations |

Y. Im, H. Gang and JK. Yoo (2014). High-throughput data dimension reduction via seeded canonical correlation analysis, J. Chemometrics 2015; 29: 193-199.

R. A. Johnson and D. W. Wichern(2007). Applied Multivariate Statistical Analysis. Pearson Prentice Hall: New Jersey, USA; 6 edition.539-574.

R. D. Cook, B. Li and F. Chiaromonte (2007). Dimension reduction in regression without matrix inversion. Biometrika 2007; 94: 569-584.

K. Lee and JK. Yoo(2014). Canonical correlation analysis through linearmodeling. Aust. Nz. J. Stat. 2014; 56: 59-72.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
###### data(cookie) ######
data(cookie)
myseq<-seq(141,651,by=2)
X<-as.matrix(cookie[-c(23,61),myseq])
Y<-as.matrix(cookie[-c(23,61),701:704])
dim(X);dim(Y)
selectu(X, Y, case1=TRUE)
seedCCA(X, Y, u1=2, case1=TRUE)
######## data(nutrimouse) ########
data(nutrimouse)
X<-as.matrix(nutrimouse$gene)
Y<-as.matrix(nutrimouse$lipid)
dim(X);dim(Y)
covplot(X, Y, mind=10)
selectu(X, Y, u=10, num.d=4)
seedCCA(X, Y, u1=6, u2=5, num.d=4)
``` |

seedCCA documentation built on Aug. 30, 2017, 5:09 p.m.

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