R/summary.ffm.R

In sangeeuw/factorAnalytics_Avinash: Factor Analytics

#' @title Summarizing a fitted fundamental factor model
#' 
#' @description \code{summary} method for object of class \code{ffm}. 
#' Returned object is of class {summary.ffm}.
#' 
#' @details The default \code{summary} method for a fitted \code{lm} object 
#' computes the standard errors and t-statistics under the assumption of 
#' homoskedasticty.
#' 
#' Note: This gives a summary of the fited factor returns at each time period. 
#' If \code{T} is large, you might prefer the more succint summary produced by 
#' \code{\link{print.ffm}}.
#'  
#' @param object an object of class \code{ffm} returned by \code{fitFfm}.
#' @param x an object of class \code{summary.ffm}.
#' @param digits number of significants digits to use when printing. 
#' Default is 3.
#' @param labels option to print labels and legend in the summary. Default is 
#' \code{TRUE}. When \code{FALSE}, only the coefficient matrx with standard 
#' errors is printed. 
#' @param ... futher arguments passed to or from other methods.
#' 
#' @return Returns an object of class \code{summary.ffm}. 
#' The print method for class \code{summary.ffm} outputs the call, 
#' coefficients (with standard errors and t-statistics), r-squared and 
#' residual volatilty (under the homoskedasticity assumption) for all assets. 
#' 
#' Object of class \code{summary.ffm} is a list of length N + 2 containing:
#' \item{call}{the function call to \code{fitFfm}}
#' \item{sum.list}{list of summaries of the T fit objects (of class \code{lm} or 
#' \code{lmRob}) for each time period in the factor model.}
#' 
#' @author Sangeetha Srinivasan & Yi-An Chen.
#' 
#' @seealso \code{\link{fitFfm}}, \code{\link[stats]{summary.lm}}
#' 
#' @examples
#' data(Stock.df)
#' exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP", "GICS.SECTOR")
#' fit2 <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN", 
#'                date.var="DATE", exposure.vars=exposure.vars)
#' 
#' # summary of factor returns estimated in each time period
#' summary(fit2)
#' 
#' # summary of lm fit for a single period
#' summary(fit2$factor.fit[[1]])
#' 
#' @method summary ffm
#' @export

summary.ffm <- function(object, ...){
  
  # check input object validity
  if (!inherits(object, "ffm")) {
    stop("Invalid 'ffm' object")
  }
  
  # extract summary.lm objects for each factor
  sum.list <- lapply(object$factor.fit, summary)
  
  # include the call and se.type to fitFfm
  sum <- list(call=object$call, sum.list=sum.list)
  class(sum) <- "summary.ffm"
  return(sum)
}


#' @rdname summary.ffm
#' @method print summary.ffm
#' @export

print.summary.ffm <- function(x, digits=3, labels=TRUE, ...) {
  n <- length(x$sum.list)
  if (labels==TRUE) {
    if(!is.null(cl <- x$call)) {
      cat("\nCall:\n")
      dput(cl)
    }
    cat("\nFactor Returns:\n", sep="")
    for (i in 1:n) {
      options(digits = digits)  
      table.coef <- (x$sum.list)[[i]]$coefficients
      cat("\nTime Period ", i, ": ", names(x$sum.list[i]), "\n\n", sep="")  
      r2 <- x$sum.list[[i]]$r.squared
      sigma <- x$sum.list[[i]]$sigma
      printCoefmat(table.coef, digits=digits, ...)
      cat("\nR-squared: ", r2,", Residual Volatility: ", sigma,"\n", sep="")
    }
  } else {
    for (i in 1:n) {
      options(digits = digits) 
      table.coef <- (x$sum.list)[[i]]$coefficients
      cat(names(x$sum.list[i]), "\n")
      printCoefmat(table.coef, digits=digits, signif.legend=FALSE, ...)
      cat("\n")
    }
  }
}