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#' @method summary dmrs
#' @export
summary.dmrs <- function(object, ...){
if(is.finite(mean(object[[1]], na.rm = TRUE)) == FALSE){
return("All calculations are insignificant! No summary output available.")
} else {
# This needs to be set to provide results in English language
Sys.setlocale("LC_TIME", "C")
# A) Extracting a matrix from a list and converting it into a data frame
result_daily_response <- object
type <- data.frame(object$type)
result_daily_element1 <- data.frame(object[[1]])
reference_window <- object$reference_window
# To keep RCMD check happy:
# With the following chunk, overall_maximum and overall_minimum values of
# result_daily_element1 matrix are calculated.
overall_max <- max(result_daily_element1, na.rm = TRUE)
overall_min <- min(result_daily_element1, na.rm = TRUE)
# absolute vales of overall_maximum and overall_minimum are compared and
# one of the following two if functions is used
# There are unimportant warnings produced:
# no non-missing arguments to max; returning -Inf
# Based on the answer on the StackOverlow site:
# https://stackoverflow.com/questions/24282550/no-non-missing-arguments-warning-when-using-min-or-max-in-reshape2
# Those Warnings could be easily ignored
if ((abs(overall_max) >= abs(overall_min)) == TRUE) {
# maximum value is located. Row indeces are needed to query information
# about the window width used to calculate the maximum. Column name is
# needed to query the starting day.
max_result <- suppressWarnings(which.max(apply(result_daily_element1,
MARGIN = 2, max, na.rm = TRUE)))
plot_column <- max_result
plot_column_source <- plot_column
max_index <- which.max(result_daily_element1[, names(max_result)])
row_index <- row.names(result_daily_element1)[max_index]
temporal_vector <- unlist(result_daily_element1[max_index, ])
temporal_vector <- data.frame(temporal_vector)
calculated_metric <- round(max(temporal_vector, na.rm = TRUE), 3)
lower_bound <- result_daily_response$boot_lower[max_index, as.numeric(max_result)]
upper_bound <- result_daily_response$boot_upper[max_index, as.numeric(max_result)]
# Here we remove missing values at the end of the temporal_vector.
# It is important to remove missing values only at the end of the
# temporal_vector!
row_count <- nrow(temporal_vector)
delete_rows <- 0
while (is.na(temporal_vector[row_count, ] == TRUE)){
delete_rows <- delete_rows + 1
row_count <- row_count - 1
}
# To check if the last row is a missing value
if (is.na(temporal_vector[nrow(temporal_vector), ] == TRUE)) {
temporal_vector <- temporal_vector[-c((row_count + 1):(row_count +
delete_rows)), ]
}
temporal_vector <- data.frame(temporal_vector)
}
if ((abs(overall_max) < abs(overall_min)) == TRUE) {
# minimum value is located. Row indeces are needed to query information
# about the window width used to calculate the minimum. Column name is
# needed to query the starting day.
min_result <- suppressWarnings(which.min(apply(result_daily_element1,
MARGIN = 2, min, na.rm = TRUE)))
plot_column <- min_result
plot_column_source <- plot_column
min_index <- which.min(result_daily_element1[, names(min_result)])
row_index <- row.names(result_daily_element1)[min_index]
temporal_vector <- unlist(result_daily_element1[min_index, ])
temporal_vector <- data.frame(temporal_vector)
calculated_metric <- round(min(temporal_vector, na.rm = TRUE), 3)
lower_bound <- result_daily_response$boot_lower[min_index, as.numeric(min_result)]
upper_bound <- result_daily_response$boot_upper[min_index, as.numeric(min_result)]
# Here we remove missing values
# We remove missing values at the end of the temporal_vector.
# It is important to remove missing values only at the end of the
# temporal_vector!
row_count <- nrow(temporal_vector)
delete_rows <- 0
while (is.na(temporal_vector[row_count, ] == TRUE)){
delete_rows <- delete_rows + 1
row_count <- row_count - 1
}
# To check if the last row is a missing value
if (is.na(temporal_vector[nrow(temporal_vector), ] == TRUE)) {
temporal_vector <- temporal_vector[-c((row_count + 1):(row_count +
delete_rows)), ]
}
temporal_vector <- data.frame(temporal_vector)
}
# In case of previous_year == TRUE, we calculate the day of a year
# (plot_column), considering 366 days of previous year.
if (ncol(result_daily_element1) > 366 & plot_column > 366) {
previous_year = TRUE
plot_column_extra <- plot_column %% 366
} else {
previous_year = FALSE
plot_column_extra <- plot_column
}
if (ncol(result_daily_element1) > 366) {
previous_year <- TRUE
} else {
previous_year <- FALSE
}
if (ncol(result_daily_element1) < 367){
doy <- seq(1:366)
date <- seq(as.Date('2013-01-01'),as.Date('2013-12-31'), by = "+1 day")
date[366] <- as.Date('2015-12-31')
date <- format(date, "%b %d")
date_codes <- data.frame(doy = doy, date = date)
} else {
doy <- seq(1:366)
date <- seq(as.Date('2013-01-01'),as.Date('2013-12-31'), by = "+1 day")
date[366] <- as.Date('2015-12-31')
date <- format(date, "%b %d")
date_codes <- data.frame(doy = doy, date = date)
date_codes$date <- paste0(date_codes$date, "*")
doy <- seq(1:366)
date <- seq(as.Date('2013-01-01'),as.Date('2013-12-31'), by = "+1 day")
date[366] <- as.Date('2015-12-31')
date <- format(date, "%b %d")
date_codes2 <- data.frame(doy = doy, date = date)
date_codes <- rbind(date_codes, date_codes2)
}
# Here, there is a special check if optimal window width is divisible by 2 or not.
if (as.numeric(row_index)%%2 == 0){
adjustment_1 = 0
adjustment_2 = 1
} else {
adjustment_1 = 1
adjustment_2 = 2
}
if (reference_window == "start"){
Optimal_string <- paste(as.character(date_codes[plot_column_source, 2]),"-",
as.character(date_codes[plot_column_source + as.numeric(row_index) - 1, 2]))
} else if (reference_window == "end") {
Optimal_string <- paste(as.character(date_codes[plot_column_source - as.numeric(row_index) + 1, 2]),"-",
as.character(date_codes[plot_column_source, 2]))
} else if (reference_window == "middle") {
Optimal_string <- paste(as.character(date_codes[(round2((plot_column_source - as.numeric(row_index)/2)) - adjustment_1), 2]),"-",
as.character(date_codes[(round2((plot_column_source + as.numeric(row_index)/2)) - adjustment_2), 2]))
}
# Here we define titles. They differ importantly among methods and arguments
# in the final output list from daily_response() function
if (result_daily_response[[2]] == "cor"){
y_lab <- NA
} else if (result_daily_response[[2]] == "pcor"){
y_lab <- NA
} else if (result_daily_response[[3]] == "r.squared"){
y_lab <- "Explained Variance"
} else if (result_daily_response[[3]] == "adj.r.squared"){
y_lab <- "Adjusted Explained Variance"
}
if (reference_window == 'start' && plot_column > 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("Starting Day of Optimal Window Width: Day ",
plot_column_extra, " of Current Year")}
if (reference_window == 'start' && plot_column <= 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("Starting Day of Optimal Window Width: Day ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'start' && plot_column <= 366 && ncol(result_daily_element1) <= 366){
reference_string <- paste0("Starting Day of Optimal Window Width: Day ",
plot_column_extra)}
if (reference_window == 'end' && plot_column > 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("Ending Day of Optimal Window Width: Day ",
plot_column_extra, " of Current Year")}
if (reference_window == 'end' && plot_column <= 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("Ending Day of Optimal Window Width: Day ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'end' && plot_column <= 366 && ncol(result_daily_element1) <= 366){
reference_string <- paste0("Ending Day of Optimal Window Width: Day ",
plot_column_extra)}
if (reference_window == 'middle' && plot_column > 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("Middle Day of Optimal Window Width: Day ",
plot_column_extra, " of Current Year")}
if (reference_window == 'middle' && plot_column <= 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("Middle Day of Optimal Window Width: Day ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'middle' && plot_column <= 366 && ncol(result_daily_element1) <= 366){
reference_string <- paste0("Middle Day of Optimal Window Width: Day ",
plot_column_extra)}
optimal_window_string <- paste0("Optimal Window Width: ", as.numeric(row_index),
" Days")
optimal_calculation <- paste0("The Highest ", y_lab,": " , calculated_metric)
period_string <- paste0("Analysed Period: ", result_daily_response[[4]])
if (result_daily_response[[2]] == 'cor'){
method_string <- paste0("Correlation Coefficient (", result_daily_response[[3]], ")")
} else if (result_daily_response[[2]] == 'pcor'){
method_string <- paste0("Partial Correlation Coefficient (", result_daily_response[[3]], ")")
} else if (result_daily_response[[2]] == 'lm'){
method_string <- paste0("Linear Regression")
} else if (result_daily_response[[2]] == 'brnn'){
method_string <- paste0("ANN with Bayesian Regularization")
}
if (type == "monthly"){
# Plural or singular?
if (as.numeric(row_index) == 1){
month_string <- " Month"
} else {
month_string <- " Months"
}
# In case of previous_year == TRUE, we calculate the day of a year
# (plot_column), considering 366 days of previous year.
if (ncol(result_daily_response[[1]]) > 12 & plot_column > 12) {
plot_column_extra <- plot_column %% 12
} else {
plot_column_extra <- plot_column
}
if (ncol(result_daily_response[[1]]) > 12) {
previous_year <- TRUE
} else {
previous_year <- FALSE
}
if (reference_window == 'start' && plot_column > 12 && ncol(result_daily_response[[1]]) > 12){
reference_string <- paste0("Starting Month of Optimal Window Width: Month ",
plot_column_extra, " of Current Year")}
if (reference_window == 'start' && plot_column <= 12 && ncol(result_daily_response[[1]]) > 12){
reference_string <- paste0("Starting Month of Optimal Window Width: Month ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'start' && plot_column <= 12 && ncol(result_daily_response[[1]]) <= 12){
reference_string <- paste0("Starting Month of Optimal Window Width: Month ",
plot_column_extra)}
if (reference_window == 'end' && plot_column > 12 && ncol(result_daily_response[[1]]) > 12){
reference_string <- paste0("Ending Month of Optimal Window Width: Month ",
plot_column_extra, " of Current Year")}
if (reference_window == 'end' && plot_column <= 12 && ncol(result_daily_response[[1]]) > 12){
reference_string <- paste0("Ending Month of Optimal Window Width: Month ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'end' && plot_column <= 12 && ncol(result_daily_response[[1]]) <= 12){
reference_string <- paste0("Ending Month of Optimal Window Width: Month ",
plot_column_extra)}
if (reference_window == 'middle' && plot_column > 12 && ncol(result_daily_response[[1]]) > 12){
reference_string <- paste0("Middle Month of Optimal Window Width: Month ",
plot_column_extra, " of Current Year")}
if (reference_window == 'middle' && plot_column <= 12 && ncol(result_daily_response[[1]]) > 12){
reference_string <- paste0("Middle Month of Optimal Window Width: Month ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'middle' && plot_column <= 12 && ncol(result_daily_response[[1]]) <= 12){
reference_string <- paste0("Middle Month of Optimal Window Width: Month ",
plot_column_extra)}
optimal_window_string <- paste0("Optimal Window Width: ", as.numeric(row_index),
month_string)
# Here we define a data frame of months. Later
# this dataframe will be used to describe tht optimal sequence of days
if (ncol(result_daily_response[[1]]) > 12){
date_codes <- c("Jan*", "Feb*", "Mar*", "Apr*", "May*", "Jun*", "Jul*", "Aug*", "Sep*", "Oct*", "Nov*", "Dec*",
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
} else if (ncol(result_daily_response[[1]]) <= 12){
date_codes <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
}
if (reference_window == "start"){
Optimal_string <- paste(as.character(date_codes[plot_column_source]),"-",
as.character(date_codes[plot_column_source + as.numeric(row_index) - 1]))
} else if (reference_window == "end") {
Optimal_string <- paste(as.character(date_codes[as.numeric(plot_column_source) - as.numeric(row_index) + 1]),"-",
as.character(date_codes[plot_column_source]))
} else if (reference_window == "middle") {
Optimal_string <- paste(as.character(date_codes[(round2((plot_column_source - as.numeric(row_index)/2)) - adjustment_1)]),"-",
as.character(date_codes[(round2((plot_column_source + as.numeric(row_index)/2)) - adjustment_2)]))
}
if (as.numeric(row_index == 1)){
Optimal_string <- substr(Optimal_string, 1, nchar(Optimal_string)-6)
}
}
output_df <- data.frame(Variable = c("approach",
"method",
"metric",
"analysed_years",
"maximal_calculated_metric",
"lower_ci",
"upper_ci",
"reference_window",
"analysed_previous_year",
"optimal_time_window",
"optimal_time_window_length"),
Value = c(result_daily_response$type,
method_string,
y_lab,
result_daily_response[[4]],
calculated_metric,
round(lower_bound, 3),
round(upper_bound, 3),
reference_string,
previous_year,
Optimal_string,
as.numeric(row_index)))
return(output_df)
}
}
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