vignettes/simulation_v1.R
In seonjoo/lcca:
# ## ----setup, include=FALSE-----------------------------------------------------
# knitr::opts_chunk$set(echo = TRUE)
#
# ## ----------------------------------------------------------------------------
# library(lcca)
# library(MASS)
# library(gplots)
# library(tidyverse)
#
# 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))
#
# cv_x1s = rep(0,100)
# cv_x1s = rep(0,100)
# cv_y0s = rep(0,100)
# cv_y1s = rep(0,100)
#
# set.seed(12345678)
#
# for(i in (1:100)){
# ## -----------------------------------------------------------------------------
# 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 = 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
# 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)
#
# 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$xcv_y1, nrow=81, ncol=1)
#
# cv_x1s[i] <- abs(cor(xcv_x0.array[,,i], phix0)[,1])
# cv_x1s[i] <- abs(cor(xcv_x1.array[,,i], phix1)[,1])
#
# cv_y0s[i] <- abs(cor(xcv_y0.array[,,i], phiy0)[,2])
# cv_y1s[i] <- abs(cor(xcv_y1.array[,,i], phiy1)[,2])
# }
#
# out=list(ccor.dim.list=ccor.dim.list, ccor.list=ccor.list,
# cv_x1s=cv_x1s,
# cv_x1s=cv_x1s,
# cv_y0s=cv_y0s,
# cv_y1s=cv_y1s)
# return(out)
# }
#
# ## ----------------------------------------------------------------------------
#
# get_bias = function(estimate, truth) {
# mean(estimate) - truth
# }
#
# ## ----------------------------------------------------------------------------
#
# ## I=50, r=0.8
# sim11 <- sim_lcca(I=50, r=0.8)
# ## I=50, r=0.5
# sim12 <- sim_lcca(I=50, r=0.5)
# ## I=50, r=0.3
# sim13 <- sim_lcca(I=50, r=0.3)
# ## I=50, r=0.1
# sim14 <- sim_lcca(I=50, r=0.1)
#
#
# ## I=100, r=0.8
# sim15 <- sim_lcca(I=100, r=0.8)
# ## I=100, r=0.5
# sim16 <- sim_lcca(I=100, r=0.5)
# ## I=100, r=0.3
# sim17 <- sim_lcca(I=100, r=0.3)
# ## I=100, r=0.1
# sim18 <- sim_lcca(I=100, r=0.1)
#
#
# ## I=200, r=0.8
# sim19 <- sim_lcca(I=200, r=0.8)
# ## I=200, r=0.5
# sim110 <- sim_lcca(I=200, r=0.5)
# ## I=200, r=0.3
# sim111 <- sim_lcca(I=200, r=0.3)
# ## I=200, r=0.1
# sim112 <- sim_lcca(I=200, r=0.1)
#
#
# ## I=400, r=0.8
# sim113 <- sim_lcca(I=400, r=0.8)
# ## I=400, r=0.5
# sim114 <- sim_lcca(I=400, r=0.5)
# ## I=400, r=0.3
# sim115 <- sim_lcca(I=400, r=0.3)
# ## I=400, r=0.1
# sim116 <- sim_lcca(I=400, r=0.1)
#
# ## ----------------------------------------------------------------------------
# # Canonical Correlations
# dd <- data.frame(ccors = c(sim11$ccor.list, sim12$ccor.list, sim13$ccor.list, sim14$ccor.list,
# sim15$ccor.list, sim16$ccor.list, sim17$ccor.list, sim18$ccor.list,
# sim19$ccor.list, sim110$ccor.list, sim111$ccor.list, sim112$ccor.list,
# sim113$ccor.list, sim114$ccor.list, sim115$ccor.list, sim116$ccor.list),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# ccors_fig <- ggplot(dd , aes(x=r, y=ccors)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated Canonical Correlations",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of estimated canonical correlations") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
# ## ----------------------------------------------------------------------------
# # CV X intercept
# dd <- data.frame(cv_x0 = c(sim11$cv_x0s, sim12$cv_x0s, sim13$cv_x0s, sim14$cv_x0s,
# sim15$cv_x0s, sim16$cv_x0s, sim17$cv_x0s, sim18$cv_x0s,
# sim19$cv_x0s, sim110$cv_x0s, sim111$cv_x0s, sim112$cv_x0s,
# sim113$cv_x0s, sim114$cv_x0s, sim115$cv_x0s, sim116$cv_x0s),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# cvx0_fig <- ggplot(dd , aes(x=r, y=cv_x0)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated CVs * True CVs X intercept",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of Inner product of Estimated CVs and True CVs X intercept") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
# ## ----------------------------------------------------------------------------
# # CV X slope
#
# dd <- data.frame(cv_x1 = c(sim11$cv_x1s, sim12$cv_x1s, sim13$cv_x1s, sim14$cv_x1s,
# sim15$cv_x1s, sim16$cv_x1s, sim17$cv_x1s, sim18$cv_x1s,
# sim19$cv_x1s, sim110$cv_x1s, sim111$cv_x1s, sim112$cv_x1s,
# sim113$cv_x1s, sim114$cv_x1s, sim115$cv_x1s, sim116$cv_x1s),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# cvx1_fig <- ggplot(dd , aes(x=r, y=cv_x1)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated CVs * True CVs X slope",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of Inner product of Estimated CVs and True CVs X slope") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
#
# ## ----------------------------------------------------------------------------
# # CV Y intercept
#
# dd <- data.frame(cv_y0 = c(sim11$cv_y0s, sim12$cv_y0s, sim13$cv_y0s, sim14$cv_y0s,
# sim15$cv_y0s, sim16$cv_y0s, sim17$cv_y0s, sim18$cv_y0s,
# sim19$cv_y0s, sim110$cv_y0s, sim111$cv_y0s, sim112$cv_y0s,
# sim113$cv_y0s, sim114$cv_y0s, sim115$cv_y0s, sim116$cv_y0s),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# cvy0_fig <- ggplot(dd , aes(x=r, y=cv_y0)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated CVs * True CVs Y intercept",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of Inner product of Estimated CVs and True CVs Y intercept") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
#
# ## ----------------------------------------------------------------------------
# # CV Y slope
#
# dd <- data.frame(cv_y1 = c(sim11$cv_y1s, sim12$cv_y1s, sim13$cv_y1s, sim14$cv_y1s,
# sim15$cv_y1s, sim16$cv_y1s, sim17$cv_y1s, sim18$cv_y1s,
# sim19$cv_y1s, sim110$cv_y1s, sim111$cv_y1s, sim112$cv_y1s,
# sim113$cv_y1s, sim114$cv_y1s, sim115$cv_y1s, sim116$cv_y1s),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# cvy1_fig <- ggplot(dd , aes(x=r, y=cv_y1)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated CVs * True CVs Y slope",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of Inner product of Estimated CVs and True CVs Y slope") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
# save.image("simv1.rdat")
seonjoo/lcca documentation built on April 29, 2024, 12:03 a.m.
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# ## ----setup, include=FALSE-----------------------------------------------------
# knitr::opts_chunk$set(echo = TRUE)
#
# ## ----------------------------------------------------------------------------
# library(lcca)
# library(MASS)
# library(gplots)
# library(tidyverse)
#
# 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))
#
# cv_x1s = rep(0,100)
# cv_x1s = rep(0,100)
# cv_y0s = rep(0,100)
# cv_y1s = rep(0,100)
#
# set.seed(12345678)
#
# for(i in (1:100)){
# ## -----------------------------------------------------------------------------
# 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 = 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
# 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)
#
# 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)
#
# cv_x1s[i] <- abs(cor(xcv_x0.array[,,i], phix0)[,1])
# cv_x1s[i] <- abs(cor(xcv_x1.array[,,i], phix1)[,1])
#
# cv_y0s[i] <- abs(cor(xcv_y0.array[,,i], phiy0)[,2])
# cv_y1s[i] <- abs(cor(xcv_y1.array[,,i], phiy1)[,2])
# }
#
# out=list(ccor.dim.list=ccor.dim.list, ccor.list=ccor.list,
# cv_x1s=cv_x1s,
# cv_x1s=cv_x1s,
# cv_y0s=cv_y0s,
# cv_y1s=cv_y1s)
# return(out)
# }
#
# ## ----------------------------------------------------------------------------
#
# get_bias = function(estimate, truth) {
# mean(estimate) - truth
# }
#
# ## ----------------------------------------------------------------------------
#
# ## I=50, r=0.8
# sim11 <- sim_lcca(I=50, r=0.8)
# ## I=50, r=0.5
# sim12 <- sim_lcca(I=50, r=0.5)
# ## I=50, r=0.3
# sim13 <- sim_lcca(I=50, r=0.3)
# ## I=50, r=0.1
# sim14 <- sim_lcca(I=50, r=0.1)
#
#
# ## I=100, r=0.8
# sim15 <- sim_lcca(I=100, r=0.8)
# ## I=100, r=0.5
# sim16 <- sim_lcca(I=100, r=0.5)
# ## I=100, r=0.3
# sim17 <- sim_lcca(I=100, r=0.3)
# ## I=100, r=0.1
# sim18 <- sim_lcca(I=100, r=0.1)
#
#
# ## I=200, r=0.8
# sim19 <- sim_lcca(I=200, r=0.8)
# ## I=200, r=0.5
# sim110 <- sim_lcca(I=200, r=0.5)
# ## I=200, r=0.3
# sim111 <- sim_lcca(I=200, r=0.3)
# ## I=200, r=0.1
# sim112 <- sim_lcca(I=200, r=0.1)
#
#
# ## I=400, r=0.8
# sim113 <- sim_lcca(I=400, r=0.8)
# ## I=400, r=0.5
# sim114 <- sim_lcca(I=400, r=0.5)
# ## I=400, r=0.3
# sim115 <- sim_lcca(I=400, r=0.3)
# ## I=400, r=0.1
# sim116 <- sim_lcca(I=400, r=0.1)
#
# ## ----------------------------------------------------------------------------
# # Canonical Correlations
# dd <- data.frame(ccors = c(sim11$ccor.list, sim12$ccor.list, sim13$ccor.list, sim14$ccor.list,
# sim15$ccor.list, sim16$ccor.list, sim17$ccor.list, sim18$ccor.list,
# sim19$ccor.list, sim110$ccor.list, sim111$ccor.list, sim112$ccor.list,
# sim113$ccor.list, sim114$ccor.list, sim115$ccor.list, sim116$ccor.list),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# ccors_fig <- ggplot(dd , aes(x=r, y=ccors)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated Canonical Correlations",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of estimated canonical correlations") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
# ## ----------------------------------------------------------------------------
# # CV X intercept
# dd <- data.frame(cv_x0 = c(sim11$cv_x0s, sim12$cv_x0s, sim13$cv_x0s, sim14$cv_x0s,
# sim15$cv_x0s, sim16$cv_x0s, sim17$cv_x0s, sim18$cv_x0s,
# sim19$cv_x0s, sim110$cv_x0s, sim111$cv_x0s, sim112$cv_x0s,
# sim113$cv_x0s, sim114$cv_x0s, sim115$cv_x0s, sim116$cv_x0s),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# cvx0_fig <- ggplot(dd , aes(x=r, y=cv_x0)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated CVs * True CVs X intercept",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of Inner product of Estimated CVs and True CVs X intercept") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
# ## ----------------------------------------------------------------------------
# # CV X slope
#
# dd <- data.frame(cv_x1 = c(sim11$cv_x1s, sim12$cv_x1s, sim13$cv_x1s, sim14$cv_x1s,
# sim15$cv_x1s, sim16$cv_x1s, sim17$cv_x1s, sim18$cv_x1s,
# sim19$cv_x1s, sim110$cv_x1s, sim111$cv_x1s, sim112$cv_x1s,
# sim113$cv_x1s, sim114$cv_x1s, sim115$cv_x1s, sim116$cv_x1s),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# cvx1_fig <- ggplot(dd , aes(x=r, y=cv_x1)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated CVs * True CVs X slope",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of Inner product of Estimated CVs and True CVs X slope") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
#
# ## ----------------------------------------------------------------------------
# # CV Y intercept
#
# dd <- data.frame(cv_y0 = c(sim11$cv_y0s, sim12$cv_y0s, sim13$cv_y0s, sim14$cv_y0s,
# sim15$cv_y0s, sim16$cv_y0s, sim17$cv_y0s, sim18$cv_y0s,
# sim19$cv_y0s, sim110$cv_y0s, sim111$cv_y0s, sim112$cv_y0s,
# sim113$cv_y0s, sim114$cv_y0s, sim115$cv_y0s, sim116$cv_y0s),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# cvy0_fig <- ggplot(dd , aes(x=r, y=cv_y0)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated CVs * True CVs Y intercept",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of Inner product of Estimated CVs and True CVs Y intercept") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
#
# ## ----------------------------------------------------------------------------
# # CV Y slope
#
# dd <- data.frame(cv_y1 = c(sim11$cv_y1s, sim12$cv_y1s, sim13$cv_y1s, sim14$cv_y1s,
# sim15$cv_y1s, sim16$cv_y1s, sim17$cv_y1s, sim18$cv_y1s,
# sim19$cv_y1s, sim110$cv_y1s, sim111$cv_y1s, sim112$cv_y1s,
# sim113$cv_y1s, sim114$cv_y1s, sim115$cv_y1s, sim116$cv_y1s),
# r = factor(rep(c(0.8,0.5,0.3,0.1), each=100)),
# i = factor(rep(c(50,100,200,400), each=400)))
#
# cvy1_fig <- ggplot(dd , aes(x=r, y=cv_y1)) +
# geom_boxplot() +
# scale_y_continuous(name = "Estimated CVs * True CVs Y slope",
# breaks = seq(0,1,.2),
# limits=c(0, 1)) +
# scale_x_discrete(name = "Canonical Correlations") +
# ggtitle("Boxplot of Inner product of Estimated CVs and True CVs Y slope") +
# theme_bw() +
# theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
# text = element_text(size = 12, family = "Tahoma"),
# axis.title = element_text(face="bold"),
# axis.text.x=element_text(size = 11)) +
# facet_grid(. ~ i)
#
# save.image("simv1.rdat")
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