Nothing
R/mod_assumptions_server.r
In FAfA: Factor Analysis for All
Defines functions
assumptions_server
# Assumptions Server Module
assumptions_server <- function(input, output, session, data) {
assumption_calculation_results_rv <- reactiveValues(
descriptives = NULL,
multicollinearity = NULL,
# Store the 1-row data.frames from user's assumptions() output
Mardia_Skewness = NULL,
Mardia_Kurtosis = NULL,
# Mah_significant and n_outlier are used by wrangling_server_outliers
# but assumptions() returns them, so we can store if needed, though not directly used in this UI part
Mah_significant_outliers = NULL,
n_outliers_found = NULL
)
# --- Descriptive Statistics ---
observeEvent(input$run_descriptives_button, {
validate(need(data(), "Please upload your dataset to calculate descriptive statistics."))
current_data <- data()
if (is.null(current_data)) {
showNotification("Data is not available for descriptive statistics.", type = "warning")
output$descriptives_table_output <- renderTable(data.frame(Message = "Data not loaded."))
return()
}
if (!exists("assumptions")) {
showNotification("Error: The 'assumptions' function is not found. Please check 'utils.R'.", type = "error", duration = 10)
output$descriptives_table_output <- renderTable(data.frame(Error = "Core function missing."))
return()
}
progress_id <- showNotification("Calculating descriptive statistics...", duration = NULL, type = "message")
on.exit(removeNotification(progress_id), add = TRUE)
tryCatch({
message("DEBUG (Descriptives): Calling user's assumptions() function from utils.R.")
results_from_utils <- assumptions(current_data, mah_p_threshold = 0.001) # Pass threshold
assumption_calculation_results_rv$descriptives <- results_from_utils$descriptives
assumption_calculation_results_rv$multicollinearity <- results_from_utils$multicollinearity
assumption_calculation_results_rv$Mah_significant_outliers <- results_from_utils$Mah_significant # Match user's return name
assumption_calculation_results_rv$n_outliers_found <- results_from_utils$n_outlier # Match user's return name
assumption_calculation_results_rv$Mardia_Skewness <- results_from_utils$Mardia_Skewness # User's return name
assumption_calculation_results_rv$Mardia_Kurtosis <- results_from_utils$Mardia_Kurtosis # User's return name
message("DEBUG (Descriptives): Stored Mardia_Skewness from assumptions(). Content:")
print(assumption_calculation_results_rv$Mardia_Skewness)
message("DEBUG (Descriptives): Stored Mardia_Kurtosis from assumptions(). Content:")
print(assumption_calculation_results_rv$Mardia_Kurtosis)
output$descriptives_table_output <- renderTable({
req(assumption_calculation_results_rv$descriptives)
df_to_display <- as.data.frame(assumption_calculation_results_rv$descriptives)
# Rounding is now done in utils.R's assumptions() for descriptives
df_to_display
}, rownames = TRUE, striped = TRUE, hover = TRUE, bordered = TRUE, spacing = "xs", width = "auto")
showNotification("Descriptive statistics calculated.", type = "message")
}, error = function(e) {
cat("--- ERROR CAUGHT IN assumptions_server.R (Descriptives) ---\n"); print(e); cat("--- END ---\n")
showNotification(paste("Error (Descriptives):", conditionMessage(e)), type = "error", duration = 10)
output$descriptives_table_output <- renderTable(data.frame(Error = conditionMessage(e)))
})
})
output$download_descriptives_button <- downloadHandler(
filename = function() paste0("descriptive_statistics_", Sys.Date(), ".csv"),
content = function(file) {
req(assumption_calculation_results_rv$descriptives)
write.csv(as.data.frame(assumption_calculation_results_rv$descriptives), file, row.names = TRUE)
}
)
# --- Collinearity Statistics ---
observeEvent(input$run_collinearity_button, {
validate(need(data(), "Please upload your dataset to calculate collinearity statistics."))
current_data <- data()
if (is.null(current_data)) {
showNotification("Data is not available for collinearity.", type = "warning"); return()
}
if (!exists("assumptions")) {
showNotification("Error: 'assumptions' function missing.", type = "error"); return()
}
progress_id <- showNotification("Calculating collinearity statistics...", duration = NULL, type = "message")
on.exit(removeNotification(progress_id), add = TRUE)
tryCatch({
if (is.null(assumption_calculation_results_rv$multicollinearity)) {
message("DEBUG (Collinearity): Calling user's assumptions() function from utils.R.")
results_from_utils <- assumptions(current_data, mah_p_threshold = 0.001)
assumption_calculation_results_rv$descriptives <- results_from_utils$descriptives
assumption_calculation_results_rv$multicollinearity <- results_from_utils$multicollinearity
assumption_calculation_results_rv$Mah_significant_outliers <- results_from_utils$Mah_significant
assumption_calculation_results_rv$n_outliers_found <- results_from_utils$n_outlier
assumption_calculation_results_rv$Mardia_Skewness <- results_from_utils$Mardia_Skewness
assumption_calculation_results_rv$Mardia_Kurtosis <- results_from_utils$Mardia_Kurtosis
} else {
message("Collinearity: Using previously calculated results from assumptions().")
}
output$collinearity_table_output <- renderTable({
req(assumption_calculation_results_rv$multicollinearity)
as.data.frame(assumption_calculation_results_rv$multicollinearity) # Rounding in utils.R
}, rownames = FALSE, striped = TRUE, hover = TRUE, bordered = TRUE)
showNotification("Collinearity statistics calculated.", type = "message")
}, error = function(e) {
cat("--- ERROR CAUGHT IN assumptions_server.R (Collinearity) ---\n"); print(e); cat("--- END ---\n")
showNotification(paste("Error (Collinearity):", conditionMessage(e)), type = "error", duration = 10)
output$collinearity_table_output <- renderTable(data.frame(Error = conditionMessage(e)))
})
})
# --- Multivariate Normality Tests ---
observeEvent(input$run_normality_tests_button, {
validate(need(data(), "Please upload your dataset to calculate multivariate normality tests."))
current_data <- data()
if (is.null(current_data) || !all(sapply(current_data, is.numeric)) || any(is.na(current_data)) || ncol(current_data) < 2) {
showNotification("Data is unsuitable for normality tests (must be numeric, complete, >=2 vars).", type = "error", duration = 8)
output$multivariate_normality_table_output <- renderTable(data.frame(Message = "Data unsuitable for tests."))
return()
}
message("DEBUG (Normality - Input Data): Preparing to display normality. current_data dims: ", paste(dim(current_data), collapse="x"))
progress_id <- showNotification("Calculating multivariate normality tests...", duration = NULL, type = "message")
on.exit(removeNotification(progress_id), add = TRUE)
output$multivariate_normality_table_output <- renderTable({
tryCatch({
mardia_s_res_from_util <- NULL
mardia_k_res_from_util <- NULL
if (!is.null(assumption_calculation_results_rv$Mardia_Skewness) && !is.null(assumption_calculation_results_rv$Mardia_Kurtosis)) {
message("DEBUG (Normality - Mardia): Using pre-calculated Mardia results from reactiveValues.")
mardia_s_res_from_util <- assumption_calculation_results_rv$Mardia_Skewness # User's name
mardia_k_res_from_util <- assumption_calculation_results_rv$Mardia_Kurtosis # User's name
} else if (exists("assumptions")) {
message("DEBUG (Normality - Mardia): Mardia results not in reactiveValues, calling user's assumptions() from utils.R.")
all_assumption_results <- assumptions(current_data, mah_p_threshold = 0.001)
assumption_calculation_results_rv$descriptives <- all_assumption_results$descriptives
assumption_calculation_results_rv$multicollinearity <- all_assumption_results$multicollinearity
assumption_calculation_results_rv$Mah_significant_outliers <- all_assumption_results$Mah_significant
assumption_calculation_results_rv$n_outliers_found <- all_assumption_results$n_outlier
assumption_calculation_results_rv$Mardia_Skewness <- all_assumption_results$Mardia_Skewness # Store with user's name
assumption_calculation_results_rv$Mardia_Kurtosis <- all_assumption_results$Mardia_Kurtosis # Store with user's name
mardia_s_res_from_util <- assumption_calculation_results_rv$Mardia_Skewness
mardia_k_res_from_util <- assumption_calculation_results_rv$Mardia_Kurtosis
} else {
message("DEBUG (Normality - Mardia): 'assumptions' function not found in utils.R.")
}
message("DEBUG (Normality - Mardia): Content of Mardia Skewness result from utils.R (mardia_s_res_from_util):")
print(mardia_s_res_from_util)
message("DEBUG (Normality - Mardia): Content of Mardia Kurtosis result from utils.R (mardia_k_res_from_util):")
print(mardia_k_res_from_util)
energy_results <- NULL
if(nrow(current_data) >= 2 && ncol(current_data) >=1 && nrow(current_data) >= ncol(current_data)){
energy_results <- tryCatch(energy::mvnorm.test(as.matrix(current_data), R = 199), error = function(e){
message(paste("DEBUG: Energy test failed:", e$message)); NULL
})
} else { message("DEBUG: Skipping Energy test due to data conditions.") }
normality_summary_df <- data.frame(
Test_Name = c("Mardia's Skewness", "Mardia's Kurtosis", "Energy Test (E-test)"),
Statistic = NA_real_, p_value = NA_character_, Result_Interpretation = NA_character_,
stringsAsFactors = FALSE
)
# Populate from mardia_s_res_from_util (user's structure from utils.R)
# Expected columns from user's Mardia output: "Test", "Statistic", "p-value", "Result"
if(!is.null(mardia_s_res_from_util) && is.data.frame(mardia_s_res_from_util) && nrow(mardia_s_res_from_util) == 1 &&
all(c("Statistic", "p-value", "Result") %in% colnames(mardia_s_res_from_util))){
statistic_val_skew <- as.numeric(mardia_s_res_from_util[1, "Statistic"])
p_val_skew <- as.numeric(mardia_s_res_from_util[1, "p-value"])
result_interpretation_skew <- ifelse(as.character(mardia_s_res_from_util[1, "Result"]) != "NO", "Normality Supported (p >= 0.05)", "Normality Violated (p < 0.05)")
normality_summary_df[1, "Statistic"] <- round(statistic_val_skew, 3)
normality_summary_df[1, "p_value"] <- format.pval(p_val_skew, digits = 3, eps = 0.001)
normality_summary_df[1, "Result_Interpretation"] <- result_interpretation_skew
} else {
normality_summary_df[1, "Result_Interpretation"] <- "Mardia Skewness result not available/error"
}
if(!is.null(mardia_k_res_from_util) && is.data.frame(mardia_k_res_from_util) && nrow(mardia_k_res_from_util) == 1 &&
all(c("Statistic", "p-value", "Result") %in% colnames(mardia_k_res_from_util))){
statistic_val_kurt <- as.numeric(mardia_k_res_from_util[1, "Statistic"])
p_val_kurt <- as.numeric(mardia_k_res_from_util[1, "p-value"])
result_interpretation_kurt <- ifelse(as.character(mardia_k_res_from_util[1, "Result"]) != "NO", "Normality Supported (p >= 0.05)", "Normality Violated (p < 0.05)")
normality_summary_df[2, "Statistic"] <- round(statistic_val_kurt, 3)
normality_summary_df[2, "p_value"] <- format.pval(p_val_kurt, digits = 3, eps = 0.001)
normality_summary_df[2, "Result_Interpretation"] <- result_interpretation_kurt
} else {
normality_summary_df[2, "Result_Interpretation"] <- "Mardia Kurtosis result not available/error"
}
if(!is.null(energy_results)){
normality_summary_df[3, "Statistic"] <- round(energy_results$statistic, 3)
normality_summary_df[3, "p_value"] <- format.pval(energy_results$p.value, digits = 3, eps = 0.001)
normality_summary_df[3, "Result_Interpretation"] <- ifelse(energy_results$p.value >= 0.05, "Normality Supported (p >= 0.05)", "Normality Violated (p < 0.05)")
} else {
normality_summary_df[3, "Result_Interpretation"] <- "Energy test failed or not run"
}
showNotification("Multivariate normality tests calculated.", type = "message")
normality_summary_df
}, error = function(e) {
cat("--- ERROR CAUGHT IN assumptions_server.R (Normality) ---\n"); print(e); cat("--- END ---\n")
showNotification(paste("Error (Normality):", conditionMessage(e)), type = "error", duration = 10)
data.frame(Error = conditionMessage(e))
})
}, rownames = FALSE, striped = TRUE, hover = TRUE, bordered = TRUE)
})
}
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FAfA documentation built on June 8, 2025, 10:56 a.m.
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Defines functions assumptions_server
# Assumptions Server Module
assumptions_server <- function(input, output, session, data) {
assumption_calculation_results_rv <- reactiveValues(
descriptives = NULL,
multicollinearity = NULL,
# Store the 1-row data.frames from user's assumptions() output
Mardia_Skewness = NULL,
Mardia_Kurtosis = NULL,
# Mah_significant and n_outlier are used by wrangling_server_outliers
# but assumptions() returns them, so we can store if needed, though not directly used in this UI part
Mah_significant_outliers = NULL,
n_outliers_found = NULL
)
# --- Descriptive Statistics ---
observeEvent(input$run_descriptives_button, {
validate(need(data(), "Please upload your dataset to calculate descriptive statistics."))
current_data <- data()
if (is.null(current_data)) {
showNotification("Data is not available for descriptive statistics.", type = "warning")
output$descriptives_table_output <- renderTable(data.frame(Message = "Data not loaded."))
return()
}
if (!exists("assumptions")) {
showNotification("Error: The 'assumptions' function is not found. Please check 'utils.R'.", type = "error", duration = 10)
output$descriptives_table_output <- renderTable(data.frame(Error = "Core function missing."))
return()
}
progress_id <- showNotification("Calculating descriptive statistics...", duration = NULL, type = "message")
on.exit(removeNotification(progress_id), add = TRUE)
tryCatch({
message("DEBUG (Descriptives): Calling user's assumptions() function from utils.R.")
results_from_utils <- assumptions(current_data, mah_p_threshold = 0.001) # Pass threshold
assumption_calculation_results_rv$descriptives <- results_from_utils$descriptives
assumption_calculation_results_rv$multicollinearity <- results_from_utils$multicollinearity
assumption_calculation_results_rv$Mah_significant_outliers <- results_from_utils$Mah_significant # Match user's return name
assumption_calculation_results_rv$n_outliers_found <- results_from_utils$n_outlier # Match user's return name
assumption_calculation_results_rv$Mardia_Skewness <- results_from_utils$Mardia_Skewness # User's return name
assumption_calculation_results_rv$Mardia_Kurtosis <- results_from_utils$Mardia_Kurtosis # User's return name
message("DEBUG (Descriptives): Stored Mardia_Skewness from assumptions(). Content:")
print(assumption_calculation_results_rv$Mardia_Skewness)
message("DEBUG (Descriptives): Stored Mardia_Kurtosis from assumptions(). Content:")
print(assumption_calculation_results_rv$Mardia_Kurtosis)
output$descriptives_table_output <- renderTable({
req(assumption_calculation_results_rv$descriptives)
df_to_display <- as.data.frame(assumption_calculation_results_rv$descriptives)
# Rounding is now done in utils.R's assumptions() for descriptives
df_to_display
}, rownames = TRUE, striped = TRUE, hover = TRUE, bordered = TRUE, spacing = "xs", width = "auto")
showNotification("Descriptive statistics calculated.", type = "message")
}, error = function(e) {
cat("--- ERROR CAUGHT IN assumptions_server.R (Descriptives) ---\n"); print(e); cat("--- END ---\n")
showNotification(paste("Error (Descriptives):", conditionMessage(e)), type = "error", duration = 10)
output$descriptives_table_output <- renderTable(data.frame(Error = conditionMessage(e)))
})
})
output$download_descriptives_button <- downloadHandler(
filename = function() paste0("descriptive_statistics_", Sys.Date(), ".csv"),
content = function(file) {
req(assumption_calculation_results_rv$descriptives)
write.csv(as.data.frame(assumption_calculation_results_rv$descriptives), file, row.names = TRUE)
}
)
# --- Collinearity Statistics ---
observeEvent(input$run_collinearity_button, {
validate(need(data(), "Please upload your dataset to calculate collinearity statistics."))
current_data <- data()
if (is.null(current_data)) {
showNotification("Data is not available for collinearity.", type = "warning"); return()
}
if (!exists("assumptions")) {
showNotification("Error: 'assumptions' function missing.", type = "error"); return()
}
progress_id <- showNotification("Calculating collinearity statistics...", duration = NULL, type = "message")
on.exit(removeNotification(progress_id), add = TRUE)
tryCatch({
if (is.null(assumption_calculation_results_rv$multicollinearity)) {
message("DEBUG (Collinearity): Calling user's assumptions() function from utils.R.")
results_from_utils <- assumptions(current_data, mah_p_threshold = 0.001)
assumption_calculation_results_rv$descriptives <- results_from_utils$descriptives
assumption_calculation_results_rv$multicollinearity <- results_from_utils$multicollinearity
assumption_calculation_results_rv$Mah_significant_outliers <- results_from_utils$Mah_significant
assumption_calculation_results_rv$n_outliers_found <- results_from_utils$n_outlier
assumption_calculation_results_rv$Mardia_Skewness <- results_from_utils$Mardia_Skewness
assumption_calculation_results_rv$Mardia_Kurtosis <- results_from_utils$Mardia_Kurtosis
} else {
message("Collinearity: Using previously calculated results from assumptions().")
}
output$collinearity_table_output <- renderTable({
req(assumption_calculation_results_rv$multicollinearity)
as.data.frame(assumption_calculation_results_rv$multicollinearity) # Rounding in utils.R
}, rownames = FALSE, striped = TRUE, hover = TRUE, bordered = TRUE)
showNotification("Collinearity statistics calculated.", type = "message")
}, error = function(e) {
cat("--- ERROR CAUGHT IN assumptions_server.R (Collinearity) ---\n"); print(e); cat("--- END ---\n")
showNotification(paste("Error (Collinearity):", conditionMessage(e)), type = "error", duration = 10)
output$collinearity_table_output <- renderTable(data.frame(Error = conditionMessage(e)))
})
})
# --- Multivariate Normality Tests ---
observeEvent(input$run_normality_tests_button, {
validate(need(data(), "Please upload your dataset to calculate multivariate normality tests."))
current_data <- data()
if (is.null(current_data) || !all(sapply(current_data, is.numeric)) || any(is.na(current_data)) || ncol(current_data) < 2) {
showNotification("Data is unsuitable for normality tests (must be numeric, complete, >=2 vars).", type = "error", duration = 8)
output$multivariate_normality_table_output <- renderTable(data.frame(Message = "Data unsuitable for tests."))
return()
}
message("DEBUG (Normality - Input Data): Preparing to display normality. current_data dims: ", paste(dim(current_data), collapse="x"))
progress_id <- showNotification("Calculating multivariate normality tests...", duration = NULL, type = "message")
on.exit(removeNotification(progress_id), add = TRUE)
output$multivariate_normality_table_output <- renderTable({
tryCatch({
mardia_s_res_from_util <- NULL
mardia_k_res_from_util <- NULL
if (!is.null(assumption_calculation_results_rv$Mardia_Skewness) && !is.null(assumption_calculation_results_rv$Mardia_Kurtosis)) {
message("DEBUG (Normality - Mardia): Using pre-calculated Mardia results from reactiveValues.")
mardia_s_res_from_util <- assumption_calculation_results_rv$Mardia_Skewness # User's name
mardia_k_res_from_util <- assumption_calculation_results_rv$Mardia_Kurtosis # User's name
} else if (exists("assumptions")) {
message("DEBUG (Normality - Mardia): Mardia results not in reactiveValues, calling user's assumptions() from utils.R.")
all_assumption_results <- assumptions(current_data, mah_p_threshold = 0.001)
assumption_calculation_results_rv$descriptives <- all_assumption_results$descriptives
assumption_calculation_results_rv$multicollinearity <- all_assumption_results$multicollinearity
assumption_calculation_results_rv$Mah_significant_outliers <- all_assumption_results$Mah_significant
assumption_calculation_results_rv$n_outliers_found <- all_assumption_results$n_outlier
assumption_calculation_results_rv$Mardia_Skewness <- all_assumption_results$Mardia_Skewness # Store with user's name
assumption_calculation_results_rv$Mardia_Kurtosis <- all_assumption_results$Mardia_Kurtosis # Store with user's name
mardia_s_res_from_util <- assumption_calculation_results_rv$Mardia_Skewness
mardia_k_res_from_util <- assumption_calculation_results_rv$Mardia_Kurtosis
} else {
message("DEBUG (Normality - Mardia): 'assumptions' function not found in utils.R.")
}
message("DEBUG (Normality - Mardia): Content of Mardia Skewness result from utils.R (mardia_s_res_from_util):")
print(mardia_s_res_from_util)
message("DEBUG (Normality - Mardia): Content of Mardia Kurtosis result from utils.R (mardia_k_res_from_util):")
print(mardia_k_res_from_util)
energy_results <- NULL
if(nrow(current_data) >= 2 && ncol(current_data) >=1 && nrow(current_data) >= ncol(current_data)){
energy_results <- tryCatch(energy::mvnorm.test(as.matrix(current_data), R = 199), error = function(e){
message(paste("DEBUG: Energy test failed:", e$message)); NULL
})
} else { message("DEBUG: Skipping Energy test due to data conditions.") }
normality_summary_df <- data.frame(
Test_Name = c("Mardia's Skewness", "Mardia's Kurtosis", "Energy Test (E-test)"),
Statistic = NA_real_, p_value = NA_character_, Result_Interpretation = NA_character_,
stringsAsFactors = FALSE
)
# Populate from mardia_s_res_from_util (user's structure from utils.R)
# Expected columns from user's Mardia output: "Test", "Statistic", "p-value", "Result"
if(!is.null(mardia_s_res_from_util) && is.data.frame(mardia_s_res_from_util) && nrow(mardia_s_res_from_util) == 1 &&
all(c("Statistic", "p-value", "Result") %in% colnames(mardia_s_res_from_util))){
statistic_val_skew <- as.numeric(mardia_s_res_from_util[1, "Statistic"])
p_val_skew <- as.numeric(mardia_s_res_from_util[1, "p-value"])
result_interpretation_skew <- ifelse(as.character(mardia_s_res_from_util[1, "Result"]) != "NO", "Normality Supported (p >= 0.05)", "Normality Violated (p < 0.05)")
normality_summary_df[1, "Statistic"] <- round(statistic_val_skew, 3)
normality_summary_df[1, "p_value"] <- format.pval(p_val_skew, digits = 3, eps = 0.001)
normality_summary_df[1, "Result_Interpretation"] <- result_interpretation_skew
} else {
normality_summary_df[1, "Result_Interpretation"] <- "Mardia Skewness result not available/error"
}
if(!is.null(mardia_k_res_from_util) && is.data.frame(mardia_k_res_from_util) && nrow(mardia_k_res_from_util) == 1 &&
all(c("Statistic", "p-value", "Result") %in% colnames(mardia_k_res_from_util))){
statistic_val_kurt <- as.numeric(mardia_k_res_from_util[1, "Statistic"])
p_val_kurt <- as.numeric(mardia_k_res_from_util[1, "p-value"])
result_interpretation_kurt <- ifelse(as.character(mardia_k_res_from_util[1, "Result"]) != "NO", "Normality Supported (p >= 0.05)", "Normality Violated (p < 0.05)")
normality_summary_df[2, "Statistic"] <- round(statistic_val_kurt, 3)
normality_summary_df[2, "p_value"] <- format.pval(p_val_kurt, digits = 3, eps = 0.001)
normality_summary_df[2, "Result_Interpretation"] <- result_interpretation_kurt
} else {
normality_summary_df[2, "Result_Interpretation"] <- "Mardia Kurtosis result not available/error"
}
if(!is.null(energy_results)){
normality_summary_df[3, "Statistic"] <- round(energy_results$statistic, 3)
normality_summary_df[3, "p_value"] <- format.pval(energy_results$p.value, digits = 3, eps = 0.001)
normality_summary_df[3, "Result_Interpretation"] <- ifelse(energy_results$p.value >= 0.05, "Normality Supported (p >= 0.05)", "Normality Violated (p < 0.05)")
} else {
normality_summary_df[3, "Result_Interpretation"] <- "Energy test failed or not run"
}
showNotification("Multivariate normality tests calculated.", type = "message")
normality_summary_df
}, error = function(e) {
cat("--- ERROR CAUGHT IN assumptions_server.R (Normality) ---\n"); print(e); cat("--- END ---\n")
showNotification(paste("Error (Normality):", conditionMessage(e)), type = "error", duration = 10)
data.frame(Error = conditionMessage(e))
})
}, rownames = FALSE, striped = TRUE, hover = TRUE, bordered = TRUE)
})
}
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Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.