# ## ----setup, include=FALSE-----------------------------------------------------
# knitr::opts_chunk$set(echo = TRUE)
#
# ## -----------------------------------------------------------------------------
# library(lcca)
# library(MASS)
# library(gplots)
#
# sim_lcca <- function(I, r){
# ccor.list = rep(0, 100)
# ccor.dim.list = rep(0, 100)
# xcv_x0.array= array(0, dim=c(144,1,100))
# xcv_x1.array = array(0, dim=c(144,1,100))
# xcv_y0.array = array(0, dim=c(81,1,100))
# xcv_y1.array = array(0, dim=c(81,1,100))
#
# set.seed(12345678)
#
# for(i in (1:100)){
# ## -----------------------------------------------------------------------------
# mu = c(0,0,0,0,0,0)
# stddev = sqrt(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 = I, mu = mu, Sigma = covmatx, empirical = FALSE)
#
# ## 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
# 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),]) + 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
# xi.Y = xi[,4:6]
# 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
# zeta.Y = t(matrix(rnorm(K.Y*3), ncol=K.Y)*c(8,4,2))*2
# Y = phiy0 %*% t(xi.Y[rep(1:I, visit.Y),]) + phiy1 %*% t(time.Y * xi.Y[rep(1:I, visit.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)
#
# ccor.dim.list[i] <- re$ccor.dim
# ccor.list[i] <- re$ccor
# xcv_x0.array[,,i] <- matrix(re$xcv_x0, nrow=144, ncol=1)
# xcv_x1.array[,,i] <- matrix(re$xcv_x1, nrow=144, ncol=1)
# xcv_y0.array[,,i] <- matrix(re$xcv_y0, nrow=81, ncol=1)
# xcv_y1.array[,,i] <- matrix(re$xcv_y1, nrow=81, ncol=1)
#
# xcv_x0s <- abs(cor(xcv_x0.array[,,i], phix0)[,1])
# xcv_x1s <- abs(cor(xcv_x1.array[,,i], phix1)[,1])
#
# xcv_y0s <- abs(cor(xcv_y0.array[,,i], phiy0)[,1])
# xcv_y1s <- abs(cor(xcv_y1.array[,,i], phiy1)[,1])
# }
#
# out=list(ccor.dim.list=ccor.dim.list, ccor.list=ccor.list,
# xcv_x0s=xcv_x0s,
# xcv_x1s=xcv_x1s,
# xcv_y0s=xcv_y0s,
# xcv_y1s=xcv_y1s)
# return(out)
# }
#
#
# ## I=50, r=0.8
# ## I=50, r=0.5
# ## I=50, r=0.3
# ## I=50, r=0.1
#
#
# ## I=100, r=0.8
# sim1 <- sim_lcca(I=100, r=0.8)
#
# # Dimensions
# mean(sim1$ccor.dim.list)
# sd(sim1$ccor.dim.list)
#
# # CCs
# mean(sim1$ccor.list)
# sd(sim1$ccor.list)
#
# # CVs
#
#
#
#
# ## I=100, r=0.5
# sim2 <- sim_lcca(I=100, r=0.5, varthresh=0.97)
#
# ## I=100, r=0.3
# sim3 <- sim_lcca(I=100, r=0.2, varthresh=0.97)
#
# ## I=100, r=0.1
# sim4 <- sim_lcca(I=100, r=0.8, varthresh=0.95)
#
#
# ## I=200, r=0.8
# ## I=200, r=0.5
# ## I=200, r=0.3
# ## I=200, r=0.1
#
#
# ## I=400, r=0.8
# ## I=400, r=0.5
# ## I=400, r=0.3
# ## I=400, r=0.1
#
#
#
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