R/rg-summary.R

In EchoRLiu/robustGarch: Robust Garch(1,1) Model

#' @title Summary for rg class
#'
#' @description Summary for rg S3 class
#'
#' @param fit A RG fit object of class \code{\link{rg}}
#' @param digits the number of digits for print and plot, default is 3.
#' @param main_name the title of the plot, default is "Conditional SD (vs returns)"
#' @param estimation_pos string that determines the legend position that specifies gamma, alpha, beta estimations. Choice of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center". Default is "topleft".
#' @param line_name_pos string that determines the legend position that specifies the names of lines in the plot. Choice of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center". Default is "topright".
#' @param par_ graphical parameters that can be set, which is in the form of par(...). The default is par(no.readonly = TRUE).
#' @param original_ a logical argument. If TRUE, the original return will be plotted. Default is FALSE
#' @param pctReturn_ a logical argument. IF TRUE, the plot function will plot the returns in percentage instead of original. Default is TRUE.
#' @param abs_ a logical argument, when TRUE, the plot function will plot abs(returns) with conditional standard deviation instead of returns, default to TRUE.
#'
#' @name rg-summary
#' @aliases summary.rg
#' @aliases print.rg
#' @aliases plot.rg
#' @aliases aef
#'
#' @examples
#'
#' data("rtn")
#' fit <- robGarch(rtn, methods="BM", fixed_pars = c(0.8, 3.0), optimizer="Rsolnp", stdErr_method = "numDeriv")
#' summary(fit)
#' print(fit)
#' plot(fit)
#' aef(fit)
#'
#' @rdname rg-summary
#' @export
summary.rg <- function(fit, digits = 3){

  res <- rbind(round(fit$fitted_pars, digits), round(fit$standard_error, digits), round(fit$t_value, digits), round(fit$p_value, digits))
  colnames(res) <- names(fit$fitted_pars)
  rownames(res) <- c("Estimates", "Std. Errors", "t-statistic", "p-value")

  cat("Model: ", fit$methods, " ")
  if (fit$methods == "BM"){
    cat("with div = ", fit$fixed_pars[1], ", k = ", fit$fixed_pars[2])
  }
  if (fit$methods == "M"){
    cat("with div = ", fit$fixed_pars[1])
  }
  cat("\nData: ", fit$data_name, "\n")
  cat("Observations: ", length(fit$data), "\n")
  cat("\nResult:\n")
  print(res)
  cat("\nLog-likelihood: ", fit$objective)
  cat("\n\nOptimizer: ", fit$optimizer)
  if (fit$methods == "MLE"){
    cat("\nInitial parameter estimates: ", fit$optimizer_x0[1:3], fit$optimizer_x0[5])
  } else{
    cat("\nInitial parameter estimates: ", fit$optimizer_x0[1:3])
  }
  cat("\nTime elapsed: ", fit$time_elapsed)
  cat("\nConvergence Message: ", fit$message)
}
#' @rdname rg-summary
#' @export
print.rg <- function(fit, digits = 3){

  res <- rbind(round(fit$fitted_pars, digits))
  colnames(res) <- names(fit$fitted_pars)
  rownames(res) <- c("Estimates (Std. Errors)")

  res[1,1] <- gsub("  ","",paste(res[1,1],'( ', round(fit$standard_error[1], digits), ' )'))
  res[1,2] <- gsub("  ","",paste(res[1,2],'( ', round(fit$standard_error[2], digits), ' )'))
  res[1,3] <- gsub("  ","",paste(res[1,3],'( ', round(fit$standard_error[3], digits), ' )'))
  if (fit$methods == "MLE"){
    res[1,4] <- gsub("  ","",paste(res[1,4],'( ', round(fit$standard_error[4], digits), ' )'))
  }

  cat("Model: ", fit$methods, "\n")
  cat("Data: ", fit$data_name, "\n")
  cat("Result:\n")
  noquote(res)
}
#' @rdname rg-summary
#' @export
plot.rg <- function(fit, digits = 3, estimation_pos = "topleft", line_name_pos = "topright", par_ = par(no.readonly = TRUE), pctReturn_ = TRUE, abs_ = TRUE, original_ = FALSE, main_name = "Conditional Volatility (vs |pctReturns(%)|)"){

  .plot.garchsim(fit, digits, estimation_pos, line_name_pos, par_, pctReturn_, abs_, original_, main_name)

}
#' @rdname rg-summary
#' @export
aef <- function(fit, v=5){

  aTop <- .aValue(fit, TRUE, v)
  aBottom <- .aValue(fit, FALSE, v)

  aef <- aTop/aBottom

  cat("The AEF of the estimate with respect to QML is: ", aef, "\n")

  aef
}