xlab(TeX('$\\hat{\\beta}_2^T\\mathbf{X}$')) +
ylab(TeX('$Y$'))+
theme_bw()+
labs(title="Response versus the second reduced predictor")+
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_text(size = 14),
plot.title = element_text(size = 18, hjust = 0.5),
strip.text = element_text(size = 14),
strip.background = element_blank(),
panel.spacing = unit(2, "lines"))
print(g22)
a <- read.table("output/NIR_est_new_test")
a
a <- read.table("output/NIR_est_new_test_norifle")
a
a <- read.table("output/NIR_est_new_test")
ind1 <- c(1:2,5:6,9:10)
ind2 <- setdiff(seq(12), ind1)
m <- apply(a, 2, mean, na.rm=TRUE)
m1 <- sprintf("%.1f", m[ind1]*100)
m2 <- sprintf("%.1f", m[ind2])
m[ind1] <- m1
m[ind2] <- m2
se <- apply(a, 2, function(x){sd(x, na.rm = TRUE)/sqrt(sum(!is.na(x)))})
se1 <- sprintf("%.1f", se[ind1]*100)
se2 <- sprintf("%.1f", se[ind2])
se[ind1] <- se1
se[ind2] <- se2
cat(paste0(m[1:4], ' (', se[1:4], ')', collapse = "&"), "\\\\", "\n", paste0(m[5:8], ' (', se[5:8], ')', collapse= "&"), "\\\\", "\n", paste0(m[9:12], ' (', se[9:12], ')', collapse = "&"), "\\\\", "\n")
a <- read.table("output/NIR_est_new_test_norifle")
ind1 <- c(1:2,5:6,9:10)
ind2 <- setdiff(seq(12), ind1)
m <- apply(a, 2, mean, na.rm=TRUE)
m1 <- sprintf("%.1f", m[ind1]*100)
m2 <- sprintf("%.1f", m[ind2])
m[ind1] <- m1
m[ind2] <- m2
se <- apply(a, 2, function(x){sd(x, na.rm = TRUE)/sqrt(sum(!is.na(x)))})
se1 <- sprintf("%.1f", se[ind1]*100)
se2 <- sprintf("%.1f", se[ind2])
se[ind1] <- se1
se[ind2] <- se2
cat(paste0(m[1:4], ' (', se[1:4], ')', collapse = "&"), "\\\\", "\n", paste0(m[5:8], ' (', se[5:8], ')', collapse= "&"), "\\\\", "\n", paste0(m[9:12], ' (', se[9:12], ')', collapse = "&"), "\\\\", "\n")
a <- read.table("output/lymphoma_est_new_test")
ind1 <- c(1:2,5:6,9:10)
ind2 <- setdiff(seq(12), ind1)
m <- apply(a, 2, mean, na.rm=TRUE)
m1 <- sprintf("%.1f", m[ind1]*100)
m2 <- sprintf("%.1f", m[ind2])
m[ind1] <- m1
m[ind2] <- m2
se <- apply(a, 2, function(x){sd(x, na.rm = TRUE)/sqrt(sum(!is.na(x)))})
se1 <- sprintf("%.1f", se[ind1]*100)
se2 <- sprintf("%.1f", se[ind2])
se[ind1] <- se1
se[ind2] <- se2
cat(paste0(m[1:4], ' (', se[1:4], ')', collapse = "&"), "\\\\", "\n", paste0(m[5:8], ' (', se[5:8], ')', collapse= "&"), "\\\\", "\n", paste0(m[9:12], ' (', se[9:12], ')', collapse = "&"), "\\\\", "\n")
a <- read.table("output/lymphoma_est_new_test_norifle")
ind1 <- c(1:2,5:6,9:10)
ind2 <- setdiff(seq(12), ind1)
m <- apply(a, 2, mean, na.rm=TRUE)
m1 <- sprintf("%.1f", m[ind1]*100)
m2 <- sprintf("%.1f", m[ind2])
m[ind1] <- m1
m[ind2] <- m2
se <- apply(a, 2, function(x){sd(x, na.rm = TRUE)/sqrt(sum(!is.na(x)))})
se1 <- sprintf("%.1f", se[ind1]*100)
se2 <- sprintf("%.1f", se[ind2])
se[ind1] <- se1
se[ind2] <- se2
cat(paste0(m[1:4], ' (', se[1:4], ')', collapse = "&"), "\\\\", "\n", paste0(m[5:8], ' (', se[5:8], ')', collapse= "&"), "\\\\", "\n", paste0(m[9:12], ' (', se[9:12], ')', collapse = "&"), "\\\\", "\n")
a <- read.table("output/lymphoma_pred_new_test")
m <- apply(a, 2, mean, na.rm=TRUE)
m <- sprintf("%.1f", m*100)
se <- apply(a, 2, function(x){sd(x, na.rm = TRUE)/sqrt(sum(!is.na(x)))})
se <- sprintf("%.1f", se*100)
cat(paste0(m[1:4], collapse = "&"), "\\\\", "\n", paste0(m[5:8], collapse= "&"), "\\\\", "\n", paste0(m[9:12], collapse = "&"), "\\\\", "\n")
cat(paste0(m[1:4], ' (', se[1:4], ')', collapse = "&"), "\\\\", "\n", paste0(m[5:8], ' (', se[5:8], ')', collapse= "&"), "\\\\", "\n", paste0(m[9:12], ' (', se[9:12], ')', collapse = "&"), "\\\\", "\n")
a <- read.table("output/lymphoma_pred_new_test_norifle")
m <- apply(a, 2, mean, na.rm=TRUE)
m <- sprintf("%.1f", m*100)
se <- apply(a, 2, function(x){sd(x, na.rm = TRUE)/sqrt(sum(!is.na(x)))})
se <- sprintf("%.1f", se*100)
cat(paste0(m[1:4], collapse = "&"), "\\\\", "\n", paste0(m[5:8], collapse= "&"), "\\\\", "\n", paste0(m[9:12], collapse = "&"), "\\\\", "\n")
cat(paste0(m[1:4], ' (', se[1:4], ')', collapse = "&"), "\\\\", "\n", paste0(m[5:8], ' (', se[5:8], ')', collapse= "&"), "\\\\", "\n", paste0(m[9:12], ' (', se[9:12], ')', collapse = "&"), "\\\\", "\n")
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data/lymphoma_est.R')
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/NIR_cont_est.R')
NIR[,1]
data[,1]
rowSums(data[,2:4])
(data[,5])
c(rowSums(data[,2:4]))
rowSums(data[,2:4]) == data[,5]
rowSums(data[,2:4]) - data[,5]
all(rowSums(data[,2:4]) - data[,5] <= 1e-6)
all(rowSums(data[,2:4]) - data[,5] <= 1e-5)
all(rowSums(data[,2:4]) - data[,5] <= 1e-4)
all(abs(rowSums(data[,2:4]) - data[,5]) <= 1e-4)
all(abs(rowSums(data[,2:4]) - data[,5]) <= 1e-5)
which(abs(rowSums(data[,2:4]) - data[,5]) > 1e-5)
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/NIR_cont_est.R')
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/NIR_est.R')
debugSource('~/Dropbox/shared/SEAS_R1/code/demo/simulation1.R')
sessionInfo()
debugSource('~/Dropbox/shared/SEAS_R1/code/simulation_p1000/simulation1p1000.R')
sessionInfo()
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/lymphoma_pred.R')
sessionInfo()
library(np)
a <- read.table("../simulation_p1000/output/M1p1000")
is.data.frame(a)
citation("spls")
data <- read.table('../data/NIR.rda')
data[1:5,]
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/lymphoma_est_dat.R')
y
foldid
table(foldid)
y
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/lymphoma_est_dat.R')
cnt
table(sample(rep(seq(nfolds), length = cnt))
)
sample(rep(seq(nfolds), length = cnt))
table(sample(rep(seq(nfolds), length = cnt)))
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/lymphoma_est_dat.R')
source('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/lymphoma_est_dat.R')
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/lymphoma_est.R')
foldid
debugSource('~/Dropbox/shared/SEAS_R1/code/real_data_analysis/lymphoma_est.R')
ind1 <- grep("(dist)|(TPR)|(FPR)", names(a))
ind2 <- grep("(d_)", names(a))
ind3 <- grep("(time)", names(a))
m <- apply(a, 2, mean, na.rm=TRUE)
m1 <- sprintf("%.1f", m[ind1]*100)
m2 <- sprintf("%.2f", m[ind2])
m3 <- sprintf("%.3f", m[ind3])
m[ind1] <- m1
m[ind2] <- m2
m[ind3] <- m3
se <- apply(a, 2, function(x){sd(x, na.rm = TRUE)/sqrt(sum(!is.na(x)))})
se1 <- sprintf("%.1f", se[ind1]*100)
se2 <- sprintf("%.2f", se[ind2])
se3 <- sprintf("%.3f", se[ind3])
se[ind1] <- se1
se[ind2] <- se2
se[ind3] <- se3
a <- read.table("output/lymphoma_est_LassoSIR")
a[1,]
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/lymphoma_est_LassoSIR.R')
length(out)
as.numeric(strsplit(out, " ")[[1]])
ind_hat
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/lymphoma_est_LassoSIR.R')
# })
# out <- paste0(dist, " ", smc, " ", s, " ", rank, collapse = " ")
# as.numeric(strsplit(out, " ")[[1]])
out <- c(dist, smc, s, rank)
out
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/lymphoma_est_LassoSIR.R')
boot_output
a <- read.table("output/lymphoma_est_LassoSIR-1")
colMeans(a)
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/lymphoma_est_LassoSIR.R')
ind
ind_hat
output$ind_hat
length(union(output$ind_hat, ind_hat))
length(intersect(output$ind_hat, ind_hat))
a <- read.table("output/lymphoma_pred_LassoSIR")
a[,1]
a[1,]
source('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/lymphoma_pred_dat.R')
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/lymphoma_pred_LassoSIR.R')
output
Q
a <- read.table("output/lymphoma_pred_LassoSIR")
a <- read.table("output/lymphoma_pred_LassoSIR-1")
colMeans(a)
a <- read.table("output/lymphoma_est_LassoSIR")
colMeans(a)
a <- read.table("output/lymphoma_est_LassoSIR-1")
colMeans(a)
a <- read.table("output/lymphoma_pred_LassoSIR")
colMeans(a)
a <- read.table("output/lymphoma_pred_LassoSIR-1")
colMeans(a)
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/NIR_cont_est_dat.R')
y
foldid
table(foldid)
source('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/NIR_cont_est_dat.R')
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/NIR_cont_est_LassoSIR.R')
foldid
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/NIR_cont_est_LassoSIR.R')
foldid
a <- read.table("output/NIR_cont_est_LassoSIR")
colMeans(a)
a <- read.table("output/NIR_cont_est-1")
colMeans(a)
a
a <- read.table("output/NIR_cont_pred")
colMeans(a)
a <- read.table("output/NIR_cont_est-1")
a
a <- read.table("output/NIR_est-1")
colMeans(a)
a <- read.table("output/NIR_pred-1")
colMeans(a)
a <- read.table("output/NIR_cont_est-2")
colMeans(a)
a
a[1,]
a <- read.table("output/NIR_cont_pred-1")
a
colMeans(a)
colMeans(a, na.rm = TRUE)
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/NIR_cont_est.R')
foldid
a <- read.table("output/NIR_cont_est")
a
colMeans(a)
a <- read.table("output/lymphoma_est1")
a
colMeans(a)
a_tmp <- a
a <- read.table("output/lymphoma_est2")
a_tmp <- rbind(a_tmp, a)
colMeans(a)
a <- read.table("output/lymphoma_est3")
a_tmp <- rbind(a_tmp, a)
colMeans(a)
a <- read.table("output/lymphoma_est4")
a_tmp <- rbind(a_tmp, a)
colMeans(a)
a <- read.table("output/lymphoma_est7")
a_tmp <- rbind(a_tmp, a)
colMeans(a)
colMeans(a_tmp)
a <- read.table("output/NIR_cont_pred-1")
colMeans(a)
a <- read.table("output/lymphoma_pred1")
a
colMeans(a)
a <- read.table("output/lymphoma_pred2")
colMeans(a)
a <- read.table("output/lymphoma_pred3")
colMeans(a)
a <- read.table("output/lymphoma_pred4")
colMeans(a)
a <- read.table("output/lymphoma_pred")
colMeans(a)
a <- read.table("output/lymphoma_pred1")
a_tmp <- a
a <- read.table("output/lymphoma_pred2")
a_tmp <- cbind(a_tmp,a)
a <- read.table("output/lymphoma_pred3")
a_tmp <- cbind(a_tmp,a)
a <- read.table("output/lymphoma_pred4")
a_tmp <- cbind(a_tmp,a)
a <- read.table("output/lymphoma_pred1")
a_tmp <- a
a <- read.table("output/lymphoma_pred2")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_pred3")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_pred4")
a_tmp <- rbind(a_tmp,a)
colMeans(a)
a <- read.table("output/lymphoma_pred1")
a_tmp <- a
a <- read.table("output/lymphoma_pred2")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_pred3")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_pred4")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_pred5")
a_tmp <- rbind(a_tmp,a)
write.table(a_tmp, file = "output/lymphoma_pred")
a <- read.table("output/lymphoma_pred")
colMeans()
colMeans(a)
a <- read.table("output/lymphoma_pred_LassoSIR")
colMeans(a)
a <- read.table("output/lymphoma_est1")
a_tmp <- a
a <- read.table("output/lymphoma_est2")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est3")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est4")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est5")
a <- read.table("output/lymphoma_est51")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est52")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est53")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est54")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est61")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est62")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est63")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est64")
a_tmp <- rbind(a_tmp,a)
a <- read.table("output/lymphoma_est7")
a_tmp <- rbind(a_tmp,a)
write.table(a_tmp, file = "output/lymphoma_est")
a <- read.table("output/lymphoma_est")
colMeans(a)
a <- read.table("output/lymphoma_est")
colMeans(a)
a <- read.table("output/lymphoma_pred")
colMeans(a)
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/plot_NIR_cont2.R')
source('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/plot_NIR_cont2.R')
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/plot_NIR_cont2.R')
beta_nssir <- readMat("matlab/output/nssir_NIR_cont_est-1.mat")$nssir.beta # Natural SSIR
beta_rssir <- readMat("matlab/output/rssir_NIR_cont_est-1.mat")$rssir.beta # Refined SSIR
newx_nssir <- x %*% beta_nssir
newx_rssir <- x %*% beta_rssir
# Combine all reduced predictors. We adjust the signs for some reduced predictors such that all plots have the same trend.
newx1 <- rbind(newx_seassir[,1,drop=FALSE], -newx_seasintra[,1,drop=FALSE], -newx_seaspfc[,1,drop=FALSE], newx_lassosir[,1,drop=FALSE], newx_nssir[,1,drop=FALSE], newx_rssir[,1,drop=FALSE])
newx2 <- rbind(newx_seassir[,2,drop=FALSE], newx_seasintra[,2,drop=FALSE], -newx_seaspfc[,2,drop=FALSE], newx_lassosir[,2,drop=FALSE], newx_nssir[,2,drop=FALSE], newx_rssir[,2,drop=FALSE])
data <- data.frame(x1 = newx1, x2 = newx2, y = rep(y, 6), class = factor(rep(c("SEAS-SIR", "SEAS-Intra", "SEAS-PFC", "Lasso-SIR", "natural SSIR", "refined SSIR"), each = nrow(x)), levels = c("SEAS-SIR", "SEAS-Intra", "SEAS-PFC", "Lasso-SIR", "natural SSIR", "refined SSIR")))
# Figure 2
g1 <- ggplot(data, aes(x = x1, y = y)) +
geom_point() +
facet_wrap(~class, nrow = 2, ncol = 3, shrink = FALSE, scales = "free_x") +
xlab(TeX('$\\hat{\\beta}_1^T\\mathbf{X}$')) +
ylab(TeX('$Y$'))+
theme_bw()+
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_blank(),
panel.spacing = unit(2, "lines"))
print(g1)
beta_seassir <- output$beta[[1]] # SEAS-SIR
beta_seasintra <- output$beta[[2]] # SEAS-Intra
beta_seaspfc <- output$beta[[3]] # SEAS-PFC
beta_lassosir <- output$beta[[4]] # Lasso-SIR
beta_nssir <- readMat("matlab/output/nssir_NIR_cont_est.mat")$nssir.beta # Natural SSIR
beta_rssir <- readMat("matlab/output/rssir_NIR_cont_est.mat")$rssir.beta # Refined SSIR
# The reduced predictors corresponding to each method.
newx_seassir <- x %*% beta_seassir
newx_seasintra <- x %*% beta_seasintra
newx_seaspfc <- x %*% beta_seaspfc
newx_lassosir <- x %*% beta_lassosir
newx_nssir <- x %*% beta_nssir
newx_rssir <- x %*% beta_rssir
# Combine all reduced predictors. We adjust the signs for some reduced predictors such that all plots have the same trend.
newx1 <- rbind(newx_seassir[,1,drop=FALSE], -newx_seasintra[,1,drop=FALSE], -newx_seaspfc[,1,drop=FALSE], newx_lassosir[,1,drop=FALSE], newx_nssir[,1,drop=FALSE], newx_rssir[,1,drop=FALSE])
newx2 <- rbind(newx_seassir[,2,drop=FALSE], newx_seasintra[,2,drop=FALSE], -newx_seaspfc[,2,drop=FALSE], newx_lassosir[,2,drop=FALSE], newx_nssir[,2,drop=FALSE], newx_rssir[,2,drop=FALSE])
data <- data.frame(x1 = newx1, x2 = newx2, y = rep(y, 6), class = factor(rep(c("SEAS-SIR", "SEAS-Intra", "SEAS-PFC", "Lasso-SIR", "natural SSIR", "refined SSIR"), each = nrow(x)), levels = c("SEAS-SIR", "SEAS-Intra", "SEAS-PFC", "Lasso-SIR", "natural SSIR", "refined SSIR")))
# Figure 2
g1 <- ggplot(data, aes(x = x1, y = y)) +
geom_point() +
facet_wrap(~class, nrow = 2, ncol = 3, shrink = FALSE, scales = "free_x") +
xlab(TeX('$\\hat{\\beta}_1^T\\mathbf{X}$')) +
ylab(TeX('$Y$'))+
theme_bw()+
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_blank(),
panel.spacing = unit(2, "lines"))
print(g1)
# Figure A.3, top panel
g21 <- ggplot(data, aes(x = x1, y = y)) +
geom_point() +
facet_wrap(~class, nrow = 2, ncol = 3, shrink = FALSE, scales = "free_x") +
xlab(TeX('$\\hat{\\beta}_1^T\\mathbf{X}$')) +
ylab(TeX('$Y$'))+
theme_bw()+
labs(title="Response versus the first reduced predictor")+
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_text(size = 14),
plot.title = element_text(size = 18, hjust = 0.5),
strip.text = element_text(size = 14),
strip.background = element_blank(),
panel.spacing = unit(2, "lines"))
print(g21)
# Figure A.3, bottom panel
g22 <- ggplot(data, aes(x = x2, y = y)) +
geom_point() +
facet_wrap(~class, nrow = 2, ncol = 3, shrink = FALSE, scales = "free_x") +
xlab(TeX('$\\hat{\\beta}_2^T\\mathbf{X}$')) +
ylab(TeX('$Y$'))+
theme_bw()+
labs(title="Response versus the second reduced predictor")+
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_text(size = 14),
plot.title = element_text(size = 18, hjust = 0.5),
strip.text = element_text(size = 14),
strip.background = element_blank(),
panel.spacing = unit(2, "lines"))
print(g22)
a <- read.table("output/NIR_cont_pred_ssir")
colMeans(a)
a <- read.table("output/NIR_cont_pred")
colMeans(a)
a <- read.table("output/NIR_pred")
colMeans(a)
a <- read.table("output/NIR_pred_ssir")
colMeans(a)
a <- read.table("output/NIR_pred")
colMeans(a)
a <- read.table("output/NIR_pred-1")
colMeans(a)
a <- read.table("output/NIR_est")
colMeans(a)
a <- read.table("output/NIR_cont_est-1")
colMeans(a)
a <- read.table("output/NIR_est-1")
colMeans(a)
a <- read.table("output/NIR_cont_est-1")
colMeans(a)
a <- read.table("output/NIR_cont_est")
colMeans(a)
a <- read.table("output/NIR_cont_pred")
colMeans(a)
a <- read.table("output/NIR_cont_pred-1")
colMeans(a)
a <- read.table("output/lymphoma_pred")
colMeans(a)
a <- read.table("output/lymphoma_pred-1")
colMeans(a)
a <- read.table("output/lymphoma_pred")
colMeans(a)
a <- read.table("output/lymphoma_est")
colMeans(a)
a <- read.table("output/lymphoma_est")
a <- read.table("output/lymphoma_pred_ssir")
colMeans(a)
a <- read.table("output/lymphoma_pred")
colMeans(a)
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/lymphoma_pred_ssir.R')
a <- read.table("../../lymphoma_large_p/output/lymphoma_pred")
a[1,]
a[2,]
a[3,]
a[4,]
a[5,]
a[6,]
a <- read.table("../../lymphoma_large_p/output/lymphoma_pred_LassoSIR")
a
a[1,]
c(err_nssir_d1, err_rssir_d1, err_nssir_d2, err_rssir_d2)
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/plot_lymphoma.R')
debugSource('~/OneDrive - Florida State Students/ongoing_projects/SEAS_code/code_R1/real_data_analysis/plot_NIR_cont.R')
length(output$beta)
length(y)
y
rep(y)
rep(y,2)
data
a <- read.table("output/lymphoma_est")
colMeans(a)
a <- read.table("output/lymphoma_est")
a <- read.table("lymphoma_est")
colMeans(a)
a <- read.table("output/lymphoma_pred")
colMeans(a)
a <- read.table("lymphoma_pred")
colMeans(a)
a <- read.table("output/NIR_cont_est")
colMeans(a)
a <- read.table("NIR_cont_est")
colMeans(a)
a <- read.table("output/NIR_cont_pred")
colMeans(a)
a <- read.table("NIR_cont_pred")
colMeans(a)
a <- read.table("output/NIR_pred")
colMeans(a)
a <- read.table("NIR_pred")
colMeans(a)
a <- read.table("output/NIR_est")
colMeans(a)
a <- read.table("NIR_est")
colMeans(a)
pwd
pwd()
getwe()
getwd()
a <- read.table("output/lymphoma_est")
colMeans(a)
a <- read.table("output/lymphoma_est-1")
colMeans(a)
a <- read.table("output/lymphoma_pred")
colMeans(a)
a <- read.table("output/lymphoma_pred-1")
colMeans(a)
a <- read.table("../../code_R1_with_matlab/real_data_analysis/output/lymphoma_est")
colMeans(a)
a <- read.table("../../code_R1_with_matlab/real_data_analysis/output/lymphoma_est-1")
colMeans(a)
a <- read.table("../../code_R1_with_matlab/real_data_analysis/output/lymphoma_pred")
colMeans(a)
a <- read.table("../../code_R1_with_matlab/real_data_analysis/output/lymphoma_pred-1")
colMeans(a)
