seedCCA: Seeded Canonical correlation analysis

Description Usage Arguments Value References Examples

View source: R/seedCCA.R

Description

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

Usage

1
seedCCA(first.set, second.set, u1=2, u2=2, case1=FALSE, num.d=4)

Arguments

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.

Value

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

References

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.

Examples

 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.

Related to seedCCA in seedCCA...