inst/appliedPAClassificationScripts/Sasaki/postprocess/intensity/postprocess_lab.R
In elray1/PACwithDDM: Data set applications and simulation study for PACwithDDM paper.
rm(list = ls())
library("rayThesis")
library("irr")
results_dir <- "C:/Stat/HMM/HMMEnsembles/HMMapplication/Sasaki/intensity/results/lab"
combined_results_dir <- "C:/Stat/HMM/HMMEnsembles/rayThesis/data/"
setting <- "lab"
results_df <- data.frame(location = NA, class_var = NA, fit_method = NA, case = NA, subject = NA,
prop_correct = NA, F1_score_macro = NA, mse_pred = NA)
#results_df[, 6:7] <- as.numeric(results_df[, 6:7])
# prop_correct = na_vec, kappa = na_vec, F1_score_micro = na_vec, F1_score_macro = na_vec)
row_num <- 1
for(location in c("ankle", "hip", "wrist")) {
if(identical(location, "ankle")) {
if(identical(setting, "freeliving")) {
fit_data <- SasakiFreeLivingAnkle
} else if(identical(setting, "lab")) {
fit_data <- SasakiLabAnkle
N <- 34
} else {
stop("Invalid setting")
}
} else if(identical(location, "hip")) {
if(identical(setting, "freeliving")) {
fit_data <- SasakiFreeLivingHip
} else if(identical(setting, "lab")) {
fit_data <- SasakiLabHip
N <- 35
} else {
stop("Invalid setting")
}
} else if(identical(location, "wrist")) {
if(identical(setting, "freeliving")) {
fit_data <- SasakiFreeLivingWrist
} else if(identical(setting, "lab")) {
fit_data <- SasakiLabWrist
N <- 35
} else {
stop("Invalid setting")
}
} else {
stop("Invalid location")
}
for(class_var in c("y_intensity")) {
for(case in c("1stage/truth", "1stage/notruth", "2stage/stage2")) {
if(identical(case, "2stage/stage2")) {
all_fit_methods <- c("baggedFeatureSubsetGradientTreeBoostCRF", "parametricBoostCRF", "RFCRF", "RF", "RFHMM")
} else {
all_fit_methods <- c("baggedFeatureSubsetGradientTreeBoostCRF", "parametricBoostCRF", "RFCRF", "RFCRFseqbag", "gradientTreeBoostCRF", "unregularizedParametricCRF", "RF", "RFHMM", "MLRHMM", "normalHMM")
}
for(fit_method in all_fit_methods) {
for(subject in seq_len(N)) {
if(identical(fit_method, "RF") && identical(case, "2stage/stage2")) {
results_filename <- paste0("ReducedTrans_subject", subject, ".Rdata")
} else {
results_filename <- paste0("FullTrans_subject", subject, ".Rdata")
}
if(identical(case, "2stage/stage2")) {
load(file.path(results_dir, case, location, class_var, fit_method, results_filename))
} else {
load(file.path(results_dir, case, location, fit_method, results_filename))
}
num_classes <- 4
if(identical(fit_method, "gradientTreeBoostCRF")) {
F1_score_macro_by_m <- lapply(confusion_matrix_by_m, function(confusion_matrix, num_classes) {
temp <- as.data.frame(confusion_matrix)
inds <- as.matrix(temp[, 1:2])
inds[inds == "5"] <- "4"
storage.mode(inds) <- "integer"
vals <- temp[, 3]
confusion_matrix <- matrix(0, nrow = num_classes, ncol = num_classes)
confusion_matrix[inds] <- temp[, 3]
# # calculate kappa (relative to true values)
# kappa <- kappa2(cbind(fit_data[[subject]][[class_var]], y_pred))$value
#
tp_by_class <- diag(confusion_matrix)
tn_by_class <- sapply(seq_along(tp_by_class), function(ind) sum(tp_by_class[-ind]))
fp_by_class <- apply(confusion_matrix, 2, sum) - tp_by_class
fn_by_class <- apply(confusion_matrix, 1, sum) - tp_by_class
#
# # calculate micro F1
# precision_micro <- sum(tp_by_class) / (sum(tp_by_class) + sum(fp_by_class))
# recall_micro <- sum(tp_by_class) / (sum(tp_by_class) + sum(fn_by_class))
# F1_score_micro <- 2 * precision_micro * recall_micro / (precision_micro + recall_micro)
#
# calculate macro F1
to_keep <- (apply(confusion_matrix, 1, sum) > 0)
precision_by_class <- tp_by_class[to_keep] / (tp_by_class[to_keep] + fp_by_class[to_keep])
precision_by_class[is.na(precision_by_class)] <- 0
recall_by_class <- tp_by_class[to_keep] / (tp_by_class[to_keep] + fn_by_class[to_keep])
recall_by_class[is.na(recall_by_class)] <- 0
precision_macro <- mean(precision_by_class)
recall_macro <- mean(recall_by_class)
F1_score_macro <- 2 * precision_macro * recall_macro / (precision_macro + recall_macro)
return(F1_score_macro)
}, num_classes)
mse_pred_by_m <- lapply(log_class_probs_by_m, function(log_class_probs) {
if(fit_method %in% c("baggedFeatureSubsetGradientTreeBoostCRF", "parametricBoostCRF", "RFCRF", "RFCRFseqbag", "gradientTreeBoostCRF", "unregularizedParametricCRF", "SVM", "RFHMM", "MLRHMM")) {
est_class_probs <- exp(log_class_probs[[1]])
} else if(fit_method %in% c("SVM")) {
est_class_probs <- matrix(NA, nrow = length(y_pred), ncol = num_classes)
} else {
est_class_probs <- exp(log_class_probs)
}
y_true_as_matrix <- matrix(0, nrow = length(fit_data[[subject]][[class_var]]), ncol = num_classes)
y_true_as_matrix[cbind(seq_along(fit_data[[subject]][[class_var]]), as.integer(fit_data[[subject]][[class_var]]))] <- 1
mse_pred <- sum((y_true_as_matrix - est_class_probs)^2) / length(fit_data[[subject]][[class_var]])
return(mse_pred)
})
# worst values
results_df[row_num, ] <- list(location = location, class_var = class_var, fit_method = "gradientTreeBoostCRF-worst",
case = case, subject = subject,
prop_correct = min(unlist(prop_correct_by_m)), F1_score_macro = min(unlist(F1_score_macro_by_m)), mse_pred = max(unlist(mse_pred_by_m)))
row_num <- row_num + 1
# median values
results_df[row_num, ] <- list(location = location, class_var = class_var, fit_method = "gradientTreeBoostCRF-med",
case = case, subject = subject,
prop_correct = median(unlist(prop_correct_by_m)), F1_score_macro = median(unlist(F1_score_macro_by_m)), mse_pred = median(unlist(mse_pred_by_m)))
row_num <- row_num + 1
# best values
results_df[row_num, ] <- list(location = location, class_var = class_var, fit_method = "gradientTreeBoostCRF-best",
case = case, subject = subject,
prop_correct = max(unlist(prop_correct_by_m)), F1_score_macro = max(unlist(F1_score_macro_by_m)), mse_pred = min(unlist(mse_pred_by_m)))
row_num <- row_num + 1
}
temp <- as.data.frame(confusion_matrix)
inds <- as.matrix(temp[, 1:2])
inds[inds == "5"] <- "4"
storage.mode(inds) <- "integer"
vals <- temp[, 3]
confusion_matrix <- matrix(0, nrow = num_classes, ncol = num_classes)
confusion_matrix[inds] <- temp[, 3]
# # calculate kappa (relative to true values)
# kappa <- kappa2(cbind(fit_data[[subject]][[class_var]], y_pred))$value
#
tp_by_class <- diag(confusion_matrix)
tn_by_class <- sapply(seq_along(tp_by_class), function(ind) sum(tp_by_class[-ind]))
fp_by_class <- apply(confusion_matrix, 2, sum) - tp_by_class
fn_by_class <- apply(confusion_matrix, 1, sum) - tp_by_class
#
# # calculate micro F1
# precision_micro <- sum(tp_by_class) / (sum(tp_by_class) + sum(fp_by_class))
# recall_micro <- sum(tp_by_class) / (sum(tp_by_class) + sum(fn_by_class))
# F1_score_micro <- 2 * precision_micro * recall_micro / (precision_micro + recall_micro)
#
# calculate macro F1
to_keep <- (apply(confusion_matrix, 1, sum) > 0)
precision_by_class <- tp_by_class[to_keep] / (tp_by_class[to_keep] + fp_by_class[to_keep])
precision_by_class[is.na(precision_by_class)] <- 0
recall_by_class <- tp_by_class[to_keep] / (tp_by_class[to_keep] + fn_by_class[to_keep])
recall_by_class[is.na(recall_by_class)] <- 0
precision_macro <- mean(precision_by_class)
recall_macro <- mean(recall_by_class)
F1_score_macro <- 2 * precision_macro * recall_macro / (precision_macro + recall_macro)
# calculate MSE / Brier score
if(fit_method %in% c("baggedFeatureSubsetGradientTreeBoostCRF", "parametricBoostCRF", "RFCRF", "RFCRFseqbag", "gradientTreeBoostCRF", "unregularizedParametricCRF", "SVM", "RFHMM", "MLRHMM")) {
est_class_probs <- exp(log_class_probs[[1]])
} else if(fit_method %in% c("SVM")) {
est_class_probs <- matrix(NA, nrow = length(y_pred), ncol = num_classes)
} else {
est_class_probs <- exp(log_class_probs)
}
y_true_as_matrix <- matrix(0, nrow = length(fit_data[[subject]][[class_var]]), ncol = num_classes)
y_true_as_matrix[cbind(seq_along(fit_data[[subject]][[class_var]]), as.integer(fit_data[[subject]][[class_var]]))] <- 1
mse_pred <- sum((y_true_as_matrix - est_class_probs)^2) / length(fit_data[[subject]][[class_var]])
true_subject <- subject
if(location == "ankle" && subject >= 32) {
true_subject <- subject + 1
}
results_df[row_num, ] <- list(location = location, class_var = class_var, fit_method = fit_method,
case = case, subject = true_subject,
prop_correct = prop_correct, F1_score_macro = F1_score_macro, mse_pred = mse_pred)
row_num <- row_num + 1
}
}
}
}
}
results_df$fit_method[results_df$fit_method == "parametricBoostCRF"] <- "BB-Par-CRF"
results_df$fit_method[results_df$fit_method == "baggedFeatureSubsetGradientTreeBoostCRF"] <- "BB-Nonpar-CRF"
results_df$fit_method[results_df$fit_method == "RFCRF"] <- "RF-CRF"
results_df$fit_method[results_df$fit_method == "RFCRFseqbag"] <- "RF-seq-CRF"
results_df$fit_method[results_df$fit_method == "gradientTreeBoostCRF"] <- "gradient-tree-CRF-LOP"
results_df$fit_method[results_df$fit_method == "gradientTreeBoostCRF-worst"] <- "gradient-tree-CRF-worst"
results_df$fit_method[results_df$fit_method == "gradientTreeBoostCRF-med"] <- "gradient-tree-CRF-med"
results_df$fit_method[results_df$fit_method == "gradientTreeBoostCRF-best"] <- "gradient-tree-CRF-best"
results_df$fit_method[results_df$fit_method == "unregularizedParametricCRF"] <- "Par-CRF"
results_df$fit_method[results_df$fit_method == "RFHMM"] <- "RF-HMM"
results_df$fit_method[results_df$fit_method == "MLRHMM"] <- "MLR-HMM"
results_df$fit_method[results_df$fit_method == "RF"] <- "RF"
results_df$fit_method[results_df$fit_method == "normalHMM"] <- "Gaussian-mixt-HMM"
results_df$fit_method <- factor(results_df$fit_method, levels = c("BB-Par-CRF", "BB-Nonpar-CRF", "RF-CRF", "RF-seq-CRF", "gradient-tree-CRF-LOP", "gradient-tree-CRF-worst", "gradient-tree-CRF-med", "gradient-tree-CRF-best", "Par-CRF", "RF-HMM", "MLR-HMM", "RF", "Gaussian-mixt-HMM"))
results_df$location <- as.character(results_df$location)
results_df$location[results_df$location == "ankle"] <- "Ankle"
results_df$location[results_df$location == "hip"] <- "Hip"
results_df$location[results_df$location == "wrist"] <- "Wrist"
SasakiLabIntensityResults <- results_df
for(colind in seq_len(5))
SasakiLabIntensityResults[, colind] <- as.factor(SasakiLabIntensityResults[, colind])
save(SasakiLabIntensityResults, file = paste0(combined_results_dir, "SasakiLabIntensityResults.rdata"))
elray1/PACwithDDM documentation built on May 13, 2019, 8:37 a.m.
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rm(list = ls())
library("rayThesis")
library("irr")
results_dir <- "C:/Stat/HMM/HMMEnsembles/HMMapplication/Sasaki/intensity/results/lab"
combined_results_dir <- "C:/Stat/HMM/HMMEnsembles/rayThesis/data/"
setting <- "lab"
results_df <- data.frame(location = NA, class_var = NA, fit_method = NA, case = NA, subject = NA,
prop_correct = NA, F1_score_macro = NA, mse_pred = NA)
#results_df[, 6:7] <- as.numeric(results_df[, 6:7])
# prop_correct = na_vec, kappa = na_vec, F1_score_micro = na_vec, F1_score_macro = na_vec)
row_num <- 1
for(location in c("ankle", "hip", "wrist")) {
if(identical(location, "ankle")) {
if(identical(setting, "freeliving")) {
fit_data <- SasakiFreeLivingAnkle
} else if(identical(setting, "lab")) {
fit_data <- SasakiLabAnkle
N <- 34
} else {
stop("Invalid setting")
}
} else if(identical(location, "hip")) {
if(identical(setting, "freeliving")) {
fit_data <- SasakiFreeLivingHip
} else if(identical(setting, "lab")) {
fit_data <- SasakiLabHip
N <- 35
} else {
stop("Invalid setting")
}
} else if(identical(location, "wrist")) {
if(identical(setting, "freeliving")) {
fit_data <- SasakiFreeLivingWrist
} else if(identical(setting, "lab")) {
fit_data <- SasakiLabWrist
N <- 35
} else {
stop("Invalid setting")
}
} else {
stop("Invalid location")
}
for(class_var in c("y_intensity")) {
for(case in c("1stage/truth", "1stage/notruth", "2stage/stage2")) {
if(identical(case, "2stage/stage2")) {
all_fit_methods <- c("baggedFeatureSubsetGradientTreeBoostCRF", "parametricBoostCRF", "RFCRF", "RF", "RFHMM")
} else {
all_fit_methods <- c("baggedFeatureSubsetGradientTreeBoostCRF", "parametricBoostCRF", "RFCRF", "RFCRFseqbag", "gradientTreeBoostCRF", "unregularizedParametricCRF", "RF", "RFHMM", "MLRHMM", "normalHMM")
}
for(fit_method in all_fit_methods) {
for(subject in seq_len(N)) {
if(identical(fit_method, "RF") && identical(case, "2stage/stage2")) {
results_filename <- paste0("ReducedTrans_subject", subject, ".Rdata")
} else {
results_filename <- paste0("FullTrans_subject", subject, ".Rdata")
}
if(identical(case, "2stage/stage2")) {
load(file.path(results_dir, case, location, class_var, fit_method, results_filename))
} else {
load(file.path(results_dir, case, location, fit_method, results_filename))
}
num_classes <- 4
if(identical(fit_method, "gradientTreeBoostCRF")) {
F1_score_macro_by_m <- lapply(confusion_matrix_by_m, function(confusion_matrix, num_classes) {
temp <- as.data.frame(confusion_matrix)
inds <- as.matrix(temp[, 1:2])
inds[inds == "5"] <- "4"
storage.mode(inds) <- "integer"
vals <- temp[, 3]
confusion_matrix <- matrix(0, nrow = num_classes, ncol = num_classes)
confusion_matrix[inds] <- temp[, 3]
# # calculate kappa (relative to true values)
# kappa <- kappa2(cbind(fit_data[[subject]][[class_var]], y_pred))$value
#
tp_by_class <- diag(confusion_matrix)
tn_by_class <- sapply(seq_along(tp_by_class), function(ind) sum(tp_by_class[-ind]))
fp_by_class <- apply(confusion_matrix, 2, sum) - tp_by_class
fn_by_class <- apply(confusion_matrix, 1, sum) - tp_by_class
#
# # calculate micro F1
# precision_micro <- sum(tp_by_class) / (sum(tp_by_class) + sum(fp_by_class))
# recall_micro <- sum(tp_by_class) / (sum(tp_by_class) + sum(fn_by_class))
# F1_score_micro <- 2 * precision_micro * recall_micro / (precision_micro + recall_micro)
#
# calculate macro F1
to_keep <- (apply(confusion_matrix, 1, sum) > 0)
precision_by_class <- tp_by_class[to_keep] / (tp_by_class[to_keep] + fp_by_class[to_keep])
precision_by_class[is.na(precision_by_class)] <- 0
recall_by_class <- tp_by_class[to_keep] / (tp_by_class[to_keep] + fn_by_class[to_keep])
recall_by_class[is.na(recall_by_class)] <- 0
precision_macro <- mean(precision_by_class)
recall_macro <- mean(recall_by_class)
F1_score_macro <- 2 * precision_macro * recall_macro / (precision_macro + recall_macro)
return(F1_score_macro)
}, num_classes)
mse_pred_by_m <- lapply(log_class_probs_by_m, function(log_class_probs) {
if(fit_method %in% c("baggedFeatureSubsetGradientTreeBoostCRF", "parametricBoostCRF", "RFCRF", "RFCRFseqbag", "gradientTreeBoostCRF", "unregularizedParametricCRF", "SVM", "RFHMM", "MLRHMM")) {
est_class_probs <- exp(log_class_probs[[1]])
} else if(fit_method %in% c("SVM")) {
est_class_probs <- matrix(NA, nrow = length(y_pred), ncol = num_classes)
} else {
est_class_probs <- exp(log_class_probs)
}
y_true_as_matrix <- matrix(0, nrow = length(fit_data[[subject]][[class_var]]), ncol = num_classes)
y_true_as_matrix[cbind(seq_along(fit_data[[subject]][[class_var]]), as.integer(fit_data[[subject]][[class_var]]))] <- 1
mse_pred <- sum((y_true_as_matrix - est_class_probs)^2) / length(fit_data[[subject]][[class_var]])
return(mse_pred)
})
# worst values
results_df[row_num, ] <- list(location = location, class_var = class_var, fit_method = "gradientTreeBoostCRF-worst",
case = case, subject = subject,
prop_correct = min(unlist(prop_correct_by_m)), F1_score_macro = min(unlist(F1_score_macro_by_m)), mse_pred = max(unlist(mse_pred_by_m)))
row_num <- row_num + 1
# median values
results_df[row_num, ] <- list(location = location, class_var = class_var, fit_method = "gradientTreeBoostCRF-med",
case = case, subject = subject,
prop_correct = median(unlist(prop_correct_by_m)), F1_score_macro = median(unlist(F1_score_macro_by_m)), mse_pred = median(unlist(mse_pred_by_m)))
row_num <- row_num + 1
# best values
results_df[row_num, ] <- list(location = location, class_var = class_var, fit_method = "gradientTreeBoostCRF-best",
case = case, subject = subject,
prop_correct = max(unlist(prop_correct_by_m)), F1_score_macro = max(unlist(F1_score_macro_by_m)), mse_pred = min(unlist(mse_pred_by_m)))
row_num <- row_num + 1
}
temp <- as.data.frame(confusion_matrix)
inds <- as.matrix(temp[, 1:2])
inds[inds == "5"] <- "4"
storage.mode(inds) <- "integer"
vals <- temp[, 3]
confusion_matrix <- matrix(0, nrow = num_classes, ncol = num_classes)
confusion_matrix[inds] <- temp[, 3]
# # calculate kappa (relative to true values)
# kappa <- kappa2(cbind(fit_data[[subject]][[class_var]], y_pred))$value
#
tp_by_class <- diag(confusion_matrix)
tn_by_class <- sapply(seq_along(tp_by_class), function(ind) sum(tp_by_class[-ind]))
fp_by_class <- apply(confusion_matrix, 2, sum) - tp_by_class
fn_by_class <- apply(confusion_matrix, 1, sum) - tp_by_class
#
# # calculate micro F1
# precision_micro <- sum(tp_by_class) / (sum(tp_by_class) + sum(fp_by_class))
# recall_micro <- sum(tp_by_class) / (sum(tp_by_class) + sum(fn_by_class))
# F1_score_micro <- 2 * precision_micro * recall_micro / (precision_micro + recall_micro)
#
# calculate macro F1
to_keep <- (apply(confusion_matrix, 1, sum) > 0)
precision_by_class <- tp_by_class[to_keep] / (tp_by_class[to_keep] + fp_by_class[to_keep])
precision_by_class[is.na(precision_by_class)] <- 0
recall_by_class <- tp_by_class[to_keep] / (tp_by_class[to_keep] + fn_by_class[to_keep])
recall_by_class[is.na(recall_by_class)] <- 0
precision_macro <- mean(precision_by_class)
recall_macro <- mean(recall_by_class)
F1_score_macro <- 2 * precision_macro * recall_macro / (precision_macro + recall_macro)
# calculate MSE / Brier score
if(fit_method %in% c("baggedFeatureSubsetGradientTreeBoostCRF", "parametricBoostCRF", "RFCRF", "RFCRFseqbag", "gradientTreeBoostCRF", "unregularizedParametricCRF", "SVM", "RFHMM", "MLRHMM")) {
est_class_probs <- exp(log_class_probs[[1]])
} else if(fit_method %in% c("SVM")) {
est_class_probs <- matrix(NA, nrow = length(y_pred), ncol = num_classes)
} else {
est_class_probs <- exp(log_class_probs)
}
y_true_as_matrix <- matrix(0, nrow = length(fit_data[[subject]][[class_var]]), ncol = num_classes)
y_true_as_matrix[cbind(seq_along(fit_data[[subject]][[class_var]]), as.integer(fit_data[[subject]][[class_var]]))] <- 1
mse_pred <- sum((y_true_as_matrix - est_class_probs)^2) / length(fit_data[[subject]][[class_var]])
true_subject <- subject
if(location == "ankle" && subject >= 32) {
true_subject <- subject + 1
}
results_df[row_num, ] <- list(location = location, class_var = class_var, fit_method = fit_method,
case = case, subject = true_subject,
prop_correct = prop_correct, F1_score_macro = F1_score_macro, mse_pred = mse_pred)
row_num <- row_num + 1
}
}
}
}
}
results_df$fit_method[results_df$fit_method == "parametricBoostCRF"] <- "BB-Par-CRF"
results_df$fit_method[results_df$fit_method == "baggedFeatureSubsetGradientTreeBoostCRF"] <- "BB-Nonpar-CRF"
results_df$fit_method[results_df$fit_method == "RFCRF"] <- "RF-CRF"
results_df$fit_method[results_df$fit_method == "RFCRFseqbag"] <- "RF-seq-CRF"
results_df$fit_method[results_df$fit_method == "gradientTreeBoostCRF"] <- "gradient-tree-CRF-LOP"
results_df$fit_method[results_df$fit_method == "gradientTreeBoostCRF-worst"] <- "gradient-tree-CRF-worst"
results_df$fit_method[results_df$fit_method == "gradientTreeBoostCRF-med"] <- "gradient-tree-CRF-med"
results_df$fit_method[results_df$fit_method == "gradientTreeBoostCRF-best"] <- "gradient-tree-CRF-best"
results_df$fit_method[results_df$fit_method == "unregularizedParametricCRF"] <- "Par-CRF"
results_df$fit_method[results_df$fit_method == "RFHMM"] <- "RF-HMM"
results_df$fit_method[results_df$fit_method == "MLRHMM"] <- "MLR-HMM"
results_df$fit_method[results_df$fit_method == "RF"] <- "RF"
results_df$fit_method[results_df$fit_method == "normalHMM"] <- "Gaussian-mixt-HMM"
results_df$fit_method <- factor(results_df$fit_method, levels = c("BB-Par-CRF", "BB-Nonpar-CRF", "RF-CRF", "RF-seq-CRF", "gradient-tree-CRF-LOP", "gradient-tree-CRF-worst", "gradient-tree-CRF-med", "gradient-tree-CRF-best", "Par-CRF", "RF-HMM", "MLR-HMM", "RF", "Gaussian-mixt-HMM"))
results_df$location <- as.character(results_df$location)
results_df$location[results_df$location == "ankle"] <- "Ankle"
results_df$location[results_df$location == "hip"] <- "Hip"
results_df$location[results_df$location == "wrist"] <- "Wrist"
SasakiLabIntensityResults <- results_df
for(colind in seq_len(5))
SasakiLabIntensityResults[, colind] <- as.factor(SasakiLabIntensityResults[, colind])
save(SasakiLabIntensityResults, file = paste0(combined_results_dir, "SasakiLabIntensityResults.rdata"))
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