R/gg_raw_pgram.R

In garma: Fitting and Forecasting Gegenbauer ARMA Time Series Models

#' Display raw periodogram
#'
#' Display the raw periodogram for a time series, and not on a log scale.
#'
#' The standard "R" functions display periodograms on a log scale which can make it more difficult to locate high peaks in the
#' spectrum at differing frequencies. This routine will display the peaks on a raw scale.
#'
#' @param x (num) This should be a numeric vector representing the process to estimate.
#' @param k (int) The number of Gegenbauer factors
#' @return A ggplot object representing the raw periodogram
#' @examples
#' data(AirPassengers)
#' ap <- as.numeric(diff(AirPassengers, 12))
#' sp <- ggbr_semipara(ap)
#' print(sp)
#' @export
gg_raw_pgram <- function(x, k = 1) {
  x <- as.numeric(x) # "spectrum" function does weird things if x is a "ts" object

  ssx <- internal_garma_pgram(as.numeric(x))
  df <- data.frame(Frequency = ssx$freq, Intensity = ssx$spec)

  sp <- ggbr_semipara(x, k = k)
  annotate_df <- data.frame(x = numeric(0), y = numeric(0), label = character(0))
  for (factor in sp) {
    idx <- which(ssx$freq == factor$freq)
    annotate_df <- rbind(
      annotate_df,
      data.frame(x = factor$freq, y = ssx$spec[idx], label = sprintf(" Period: %.2f", 1.0 / factor$freq))
    )
  }

  # set up some dummy vars to prevent the RStudio checks from throwing warnings...
  Frequency <- Intensity <- y <- label <- NA
  # now plot it
  ggplot2::ggplot(data = df, ggplot2::aes(x = Frequency, y = Intensity)) +
    ggplot2::geom_line() +
    ggplot2::ggtitle(paste("Raw periogram of", deparse(match.call()$x))) +
    ggplot2::geom_text(data = annotate_df, aes(x = x, y = y, label = label), size = 2.5, hjust = 0) +
    ggplot2::theme_bw()
}