lcca.linear: Longitudial canonical correlation analysis

In seonjoo/lcca:

Longitudial canonical correlation analysis

Description

Current version is implemented for only the linear trajectory.

Usage

lcca.linear(
  x,
  y,
  varthresh = 0.95,
  projectthresh = 1,
  method = "Wilks",
  verbose = FALSE
)

Arguments

x	object for input list(x, time, J, I, visit)
projectthresh	(default=1) threshold for the dimension reduction projection in lfpca.
method	(defualt='Wilks') test statistic to be used. "Wilks","Hotelling", "Pillai", or "Roy".
verbose	(default=FALSE) print all details
varthreshold	(default=0.95) threshold to detemined the number of components of lpcs.
timeadjust	(defult=FALSE)

Value

ccor

xcv_x0: Longitudinal Canonical vector for the intercept for x

xcv_x1: Longitudinal Canonical vector for the slope for x

xcv_y0: Longitudinal Canonical vector for the intercept for y

xcv_y1: Longitudinal Canonical vector for the slope for y

Examples

set.seed(12345678)
r=0.8
mu = c(0,0,0,0,0,0)
stddev = rep(c(8,4,2),2)
cormatx = diag(1,6,6)
cormatx[1,5] <- r
cormatx[5,1] <- r
covmatx = stddev %*% t(stddev) * cormatx

## Generate scores
xi = mvrnorm(n = 100, mu = mu, Sigma = covmatx, empirical = FALSE)
I=100

## X
visit.X =rpois(I,1)+3
time.X = unlist(lapply(visit.X, function(x) scale(c(0,cumsum(rpois(x-1,1)+1)))))
J.X = sum(visit.X)
xi.X = xi[,1:3]
V.x=144
phix0 = matrix(0,V.x,3); phix0[1:12, 1]<-.1; phix0[1:12 + 12, 2]<-.1; phix0[1:12 + 12*2, 3]<-.1
phix1 = matrix(0,V.x,3); phix1[1:12 + 12*3, 1]<-.1; phix1[1:12 + 12*4, 2]<-.1; phix1[1:12 + 12*5, 3]<-.1
phixw = matrix(0,V.x,3); phixw[1:12 + 12*6, 1]<-.1; phixw[1:12 + 12*7, 2]<-.1; phixw[1:12 + 12*8, 3]<-.1
zeta.X = t(matrix(rnorm(J.X*3), ncol=J.X)*c(8,4,2))*2
X = phix0 %*% t(xi.X[rep(1:I, visit.X),]) + phix1 %*% t(time.X * xi.X[rep(1:I, visit.X),]) + phixw %*% t(zeta.X) + matrix(rnorm(V.x*J.X, 0, .1), V.x, J.X)

## Y
visit.Y=rpois(I,1)+3
time.Y = unlist(lapply(visit.Y, function(x) scale(c(0,cumsum(rpois(x-1,1)+1)))))
K.Y = sum(visit.Y)

V.y=81
phiy0 = matrix(0,V.y,3); phiy0[1:9, 1]<-.1; phiy0[1:9 + 9, 2]<-.1; phiy0[1:9 + 9*2, 3]<-.1
phiy1 = matrix(0,V.y,3); phiy1[1:9 + 9*3, 1]<-.1; phiy1[1:9 + 9*4, 2]<-.1; phiy1[1:9 + 9*5, 3]<-.1
phiyw = matrix(0,V.y,3); phiyw[1:9 + 9*6, 1]<-.1; phiyw[1:9 + 9*7, 2]<-.1; phiyw[1:9 + 9*8, 3]<-.1
zeta.Y = t(matrix(rnorm(K.Y*3), ncol=K.Y)*c(8,4,2))*2
xi.Y = xi[,4:6]
Y = phiy0 %*% t(xi.Y[rep(1:I, visit.Y),]) + phiy1 %*% t(time.Y * xi.Y[rep(1:I, visit.Y),]) + phiyw %*% t(zeta.Y) + matrix(rnorm(V.y*K.Y ,0, .1), V.y, K.Y)

x = list(X=X, time=time.X, I=I, J=sum(visit.X),visit=visit.X)
y = list(X=Y, time=time.Y, I=I, J=sum(visit.Y),visit=visit.Y)
re = lcca.linear(x=x,y=y)
re

seonjoo/lcca documentation built on April 29, 2024, 12:03 a.m.