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#' plot_specific
#'
#' Graphs a line plot of a row with a selected window width in a matrix,
#' produced by \code{\link{daily_response}} function.
#'
#' @param result_daily_response a list with three objects as produced by
#' daily_response function
#' @param window_width integer representing window width to be displayed
#' @param title logical, if set to FALSE, no plot title is displayed
#'
#' @return A ggplot2 object containing the plot display
#' @export
#'
#' @examples
#' \dontrun{
#' data(daily_temperatures_example)
#' data(example_proxies_1)
#' Example1 <- daily_response(response = example_proxies_1,
#' env_data = daily_temperatures_example, method = "lm", measure = "r.squared",
#' lower_limit = 90, upper_limit = 150)
#' plot_specific(Example1, window_width = 90)
#'
#' Example2 <- daily_response(response = example_proxies_1,
#' env_data = daily_temperatures_example, method = "brnn",
#' measure = "adj.r.squared", lower_limit = 150, upper_limit = 155,
#' neurons = 1)
#' plot_specific(Example2, window_width = 153, title = TRUE)
#' }
plot_specific <- function(result_daily_response, window_width, title = TRUE) {
# Short description of the function. It
# - extracts matrix (the frst object of a list)
# - verification of whetere we deal with negative correlations. In this case
# we will expose globail minimum (and not maximum, as in the case of positive
# correlations, r.squared and adj.r.squared)
# - subseting extracted matrix to keep only row, as defined by the argument
# window_width
# In case of there are more than 366 columns (days), xlabs are properly
# labeled
# A) Extracting a matrix from a list and converting it into a data frame
result_daily_element1 <- data.frame(result_daily_response[[1]])
# warning msg in case of selected window_width not among row.names.
# support_string suggests, which window_widths are avaliable.
support_string <- paste(row.names(result_daily_element1), sep = "",
collapse = ", ")
if (as.character(window_width) %in% row.names(result_daily_element1)
== FALSE) {
stop(paste("Selected window_width is not avaliable.",
"Select one among:", support_string, sep = ""))
}
# Subseting result_daily_element1 to keep only row with selected window_width.
# Subset is transposed and converted to a data frame, so data is ready for
# ggplot2
temoporal_vector <-
data.frame(t(result_daily_element1[row.names(result_daily_element1)
== as.character(window_width), ]))
# Removing missing values at the end of tempora_vector
# It is important to remove missing values only at the end of the
# temporal_vector!
row_count <- nrow(temoporal_vector)
delete_rows <- 0
while (is.na(temoporal_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(temoporal_vector[nrow(temoporal_vector), ] == TRUE)) {
temoporal_vector <- temoporal_vector[-c(row_count:(row_count +
delete_rows)), ]
}
temoporal_vector <- data.frame(temoporal_vector)
# renaming the first column.
# (I later use this name in the script for plotting)
names(temoporal_vector) <- c("var1")
# B) Sometime we have the case of negative correlations, the following code
# examines minimum and compare it with the maximum, to get the information if
# we have negative values. In this case, we will not be looking for max, but
# for min!
# With the following chunk, overall_maximum and overall_minimum values of
# subset are calculated and compared.
overall_max <- max(temoporal_vector$var1, na.rm = TRUE)
overall_min <- min(temoporal_vector$var1, na.rm = TRUE)
# absolute vales of overall_maximum and overall_minimum are compared and
# one of the following two if functions is used
if ((abs(overall_max) > abs(overall_min)) == TRUE) {
# maximum value is calculated and index of column is stored as index
# index represent the starting day (location) in the matrix, which gives
# the maximum result
max_result <- max(temoporal_vector, na.rm = TRUE)
calculated_measure <- round(max_result, 3)
index <- which(temoporal_vector$var1 == max_result, arr.ind = TRUE)
plot_column <- index
}
if ((abs(overall_max) < abs(overall_min)) == TRUE) {
# This is in case of negative values
# minimum value is calculated and index of column is stored as index
# index represent the starting day (location) in the matrix, which gives
# the minimum result
min_result <- min(temoporal_vector, na.rm = TRUE)
calculated_measure <- round(min_result, 3)
index <- which(temoporal_vector$var1 == min_result, arr.ind = TRUE)
plot_column <- index
}
# In case of we have more than 366 days, we calculate the day of a year
# (plot_column), considering 366 days of previous year.
if (nrow(temoporal_vector) > 366 & plot_column > 366) {
plot_column_extra <- plot_column %% 366
} else {
plot_column_extra <- plot_column
}
# C) The final plot is being created. The first part of a plot is universal,
# the second part defines xlabs, ylabs and ggtitles.
# The definition of theme
journal_theme <- theme_bw() +
theme(axis.text = element_text(size = 16, face = "bold"),
axis.title = element_text(size = 18), text = element_text(size = 18),
plot.title = element_text(size = 16, face = "bold"))
if (title == FALSE){
journal_theme <- journal_theme +
theme(plot.title = element_blank())
}
final_plot <- ggplot(temoporal_vector, aes_(y = ~var1,
x = ~ seq(1, nrow(temoporal_vector)))) + geom_line(lwd = 1.2) +
geom_vline(xintercept = plot_column, col = "red") +
scale_x_continuous(breaks = sort(c(seq(0, nrow(temoporal_vector), 50),
plot_column), decreasing = FALSE),
labels = sort(c(seq(0, nrow(temoporal_vector), 50), plot_column))) +
annotate("label", label = as.character(calculated_measure),
y = calculated_measure, x = plot_column + 15) +
journal_theme
if ((nrow(temoporal_vector) > 366) && (plot_column > 366) &&
(result_daily_response [[2]] == "cor")) {
final_plot <- final_plot +
ggtitle(paste("Maximal correlation coefficient:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra, "of current year")) +
xlab("Day of Year (Including Previous Year)") +
ylab("Correlation Coefficient")
}
if ((nrow(temoporal_vector) > 366) && (plot_column < 366) &&
(result_daily_response [[2]] == "cor")) {
final_plot <- final_plot +
ggtitle(paste("Maximal correlation coefficient:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra, "of previous year")) +
xlab("Day of Year (Including Previous Year)") +
ylab("Correlation Coefficient")
}
if ((nrow(temoporal_vector) < 366) &&
(result_daily_response [[2]] == "cor")) {
final_plot <- final_plot +
ggtitle(paste("Maximal correlation coefficient:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra)) +
xlab("Day of Year") +
ylab("Correlation Coefficient")
}
# plot for lm and brnn method; using r.squared
if ((nrow(temoporal_vector) > 366) && (plot_column > 366) &&
((result_daily_response [[2]] == "lm") |
(result_daily_response [[2]] == "brnn")) &&
(result_daily_response [[3]] == "r.squared")) {
final_plot <- final_plot +
ggtitle(paste("Maximal R squared:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra, "of current year")) +
xlab("Day of Year (Including Previous Year)") +
ylab("Explained Variance")
}
if ((nrow(temoporal_vector) > 366) && (plot_column < 366) &&
(result_daily_response[[2]] == "lm" |
result_daily_response[[2]] == "brnn") &&
result_daily_response[[3]] == "r.squared") {
final_plot <- final_plot +
ggtitle(paste("Maximal R squared:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra, "of previous year")) +
xlab("Day of Year (Including Previous Year)") +
ylab("Explained Variance")
}
if (nrow(temoporal_vector) < 366 &&
(result_daily_response[[2]] == "lm" |
result_daily_response[[2]] == "brnn") &&
result_daily_response[[3]] == "r.squared") {
final_plot <- final_plot +
ggtitle(paste("Maximal R squared:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra)) +
xlab("Day of Year") +
ylab("Explained Variance")
}
# plot for lm and brnn method; using adj.r.squared
if ((nrow(temoporal_vector) > 366) && (plot_column > 366) &&
(result_daily_response[[2]] == "lm" |
result_daily_response[[2]] == "brnn") &&
(result_daily_response[[3]] == "adj.r.squared")) {
final_plot <- final_plot +
ggtitle(paste("Maximal Adjusted R squared:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra, "of current year")) +
xlab("Day of Year (Including Previous Year)") +
ylab("Adjusted Explained Variance")
}
if ((nrow(temoporal_vector) > 366) && (plot_column < 366) &&
((result_daily_response [[2]] == "lm" |
result_daily_response [[2]] == "brnn")) &&
(result_daily_response [[3]] == "adj.r.squared")) {
final_plot <- final_plot +
ggtitle(paste("Maximal Adjusted R squared:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra,
"of previous year")) +
xlab("Day of Year (Including Previous Year)") +
ylab("Adjusted Explained Variance")
}
if ((nrow(temoporal_vector) < 366) &&
(result_daily_response [[2]] == "lm" |
result_daily_response [[2]] == "brnn") &&
(result_daily_response [[3]] == "adj.r.squared")) {
final_plot <- final_plot +
ggtitle(paste("Maximal Adjusted R squared:", calculated_measure,
"\nSelected window width:", window_width, "days",
"\nStarting day of selected window width: day",
plot_column_extra)) +
xlab("Day of Year") +
ylab("Adjusted Explained Variance")
}
final_plot
}
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