[Returnanalytics-commits] r3617 - in pkg/FactorAnalytics: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Mar 16 21:41:50 CET 2015
Author: pragnya
Date: 2015-03-16 21:41:50 +0100 (Mon, 16 Mar 2015)
New Revision: 3617
Modified:
pkg/FactorAnalytics/R/fitTsfm.R
pkg/FactorAnalytics/R/summary.tsfm.r
pkg/FactorAnalytics/man/fitTsfm.Rd
pkg/FactorAnalytics/man/summary.tsfm.Rd
Log:
Added option to trim summary; Add mkt.name to output tsfm object
Modified: pkg/FactorAnalytics/R/fitTsfm.R
===================================================================
--- pkg/FactorAnalytics/R/fitTsfm.R 2015-03-16 16:15:01 UTC (rev 3616)
+++ pkg/FactorAnalytics/R/fitTsfm.R 2015-03-16 20:41:50 UTC (rev 3617)
@@ -95,6 +95,7 @@
#' \item{data}{xts data object containing the assets and factors.}
#' \item{asset.names}{asset.names as input.}
#' \item{factor.names}{factor.names as input.}
+#' \item{mkt.name}{mkt.name as input}
#' \item{fit.method}{fit.method as input.}
#' \item{variable.selection}{variable.selection as input.}
#' Where N is the number of assets, K is the number of factors and T is the
@@ -226,6 +227,8 @@
# spaces get converted to periods in colnames of xts object after merge
asset.names <- gsub(" ",".", asset.names, fixed=TRUE)
factor.names <- gsub(" ",".", factor.names, fixed=TRUE)
+ mkt.name <- gsub(" ",".", mkt.name, fixed=TRUE)
+ rf.name <- gsub(" ",".", rf.name, fixed=TRUE)
# Selects regression procedure based on specified variable.selection method.
# Each method returns a list of fitted factor models for each asset.
@@ -243,7 +246,7 @@
result.lars <- SelectLars(dat.xts, asset.names, factor.names, lars.args,
cv.lars.args, lars.criterion)
input <- list(call=call, data=dat.xts, asset.names=asset.names,
- factor.names=factor.names, fit.method=NULL,
+ factor.names=factor.names, mkt.name=mkt.name, fit.method=NULL,
variable.selection=variable.selection)
result <- c(result.lars, input)
class(result) <- "tsfm"
@@ -268,7 +271,8 @@
result <- list(asset.fit=reg.list, alpha=alpha, beta=beta, r2=r2,
resid.sd=resid.sd, call=call, data=dat.xts,
asset.names=asset.names, factor.names=factor.names,
- fit.method=fit.method, variable.selection=variable.selection)
+ mkt.name=mkt.name, fit.method=fit.method,
+ variable.selection=variable.selection)
class(result) <- "tsfm"
return(result)
}
Modified: pkg/FactorAnalytics/R/summary.tsfm.r
===================================================================
--- pkg/FactorAnalytics/R/summary.tsfm.r 2015-03-16 16:15:01 UTC (rev 3616)
+++ pkg/FactorAnalytics/R/summary.tsfm.r 2015-03-16 20:41:50 UTC (rev 3617)
@@ -1,134 +1,147 @@
-#' @title Summarizing a fitted time series factor model
-#'
-#' @description \code{summary} method for object of class \code{tsfm}.
-#' Returned object is of class {summary.tsfm}.
-#'
-#' @details The default \code{summary} method for a fitted \code{lm} object
-#' computes the standard errors and t-statistics under the assumption of
-#' homoskedasticty. Argument \code{se.type} gives the option to compute
-#' heteroskedasticity-consistent (HC) or
-#' heteroskedasticity-autocorrelation-consistent (HAC) standard errors and
-#' t-statistics using \code{\link[lmtest]{coeftest}}. This option is meaningful
-#' only if \code{fit.method = "OLS" or "DLS"}.
-#'
-#' Standard errors are currently not available for
-#' \code{variable.selection="lars"} as there seems to be no consensus on a
-#' statistically valid method of calculating standard errors for the lasso
-#' predictions.
-#'
-#' @param object an object of class \code{tsfm} returned by \code{fitTsfm}.
-#' @param se.type one of "Default", "HC" or "HAC"; option for computing
-#' HC/HAC standard errors and t-statistics. Default is "Default".
-#' @param x an object of class \code{summary.tsfm}.
-#' @param digits number of significants digits to use when printing.
-#' Default is 3.
-#' @param ... futher arguments passed to or from other methods.
-#'
-#' @return Returns an object of class \code{summary.tsfm}.
-#' The print method for class \code{summary.tsfm} 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.tsfm} is a list of length N + 2 containing:
-#' \item{call}{the function call to \code{fitTsfm}}
-#' \item{se.type}{standard error type as input}
-#' \item{sum.list}{list of summaries of the N fit objects (of class \code{lm},
-#' \code{lmRob} or \code{lars}) for each asset in the factor model.}
-#'
-#' @author Sangeetha Srinivasan & Yi-An Chen.
-#'
-#' @seealso \code{\link{fitTsfm}}, \code{\link[stats]{summary.lm}}
-#'
-#' @examples
-#' data(managers)
-#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-#' factor.names=colnames(managers[,7:9]),
-#' data=managers)
-#'
-#' # summary of factor model fit for all assets
-#' summary(fit, "HAC")
-#'
-#' # summary of lm fit for a single asset
-#' summary(fit$asset.fit[[1]])
-#'
-#' @importFrom lmtest coeftest.default
-#' @importFrom sandwich vcovHC.default vcovHAC.default
-#'
-#' @method summary tsfm
-#' @export
-
-summary.tsfm <- function(object, se.type=c("Default","HC","HAC"), ...){
-
- # check input object validity
- if (!inherits(object, "tsfm")) {
- stop("Invalid 'tsfm' object")
- }
-
- #set default for se.type
- se.type = se.type[1]
-
- # note: fit.method=NULL for "lars" objects
- if (object$fit.method=="Robust" && se.type!="Default") {
- stop("Invalid argument: HC/HAC standard errors are applicable only if
- fit.method = 'OLS' or 'DLS'")
- }
-
- # extract summary.lm objects for each asset
- sum.list <- lapply(object$asset.fit, summary)
-
- # convert to HC/HAC standard errors and t-stats if specified
- # extract coefficients separately for "lars" variable.selection method
- for (i in object$asset.names) {
- if (se.type=="HC") {
- sum.list[[i]]$coefficients <- coeftest.default(object$asset.fit[[i]],
- vcov.=vcovHC.default)[,1:4]
- } else if (se.type=="HAC") {
- sum.list[[i]]$coefficients <- coeftest.default(object$asset.fit[[i]],
- vcov.=vcovHAC.default)[,1:4]
- }
- }
-
- if (object$variable.selection=="lars") {
- sum.list <- list()
- for (i in object$asset.names) {
- sum.list[[i]]$coefficients <- as.matrix(c(object$alpha[i], object$beta[i,]))
- rownames(sum.list[[i]]$coefficients)[1]="(Intercept)"
- colnames(sum.list[[i]]$coefficients)[1]="Estimate"
- sum.list[[i]]$r.squared <- as.numeric(object$r2[i])
- sum.list[[i]]$sigma <- as.numeric(object$resid.sd[i])
- }
- }
-
- # include the call and se.type to fitTsfm
- sum <- list(call=object$call, se.type=se.type, sum.list=sum.list)
- class(sum) <- "summary.tsfm"
- return(sum)
-}
-
-
-#' @rdname summary.tsfm
-#' @method print summary.tsfm
-#' @export
-
-print.summary.tsfm <- function(x, digits=3, ...) {
- if(!is.null(cl <- x$call)) {
- cat("\nCall:\n")
- dput(cl)
- }
- cat("\nFactor Model Coefficients:\n", sep="")
- n <- length(x$sum.list)
- for (i in 1:n) {
- options(digits = digits)
- table.coef <- (x$sum.list)[[i]]$coefficients
- if (dim(table.coef)[2] > 1) {
- cat("\nAsset", i, ": ", names(x$sum.list[i]), "\n(", x$se.type,
- " Standard Errors & T-stats)\n\n", sep="")
- } else {
- cat("\nAsset", 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="")
- }
-}
+#' @title Summarizing a fitted time series factor model
+#'
+#' @description \code{summary} method for object of class \code{tsfm}.
+#' Returned object is of class {summary.tsfm}.
+#'
+#' @details The default \code{summary} method for a fitted \code{lm} object
+#' computes the standard errors and t-statistics under the assumption of
+#' homoskedasticty. Argument \code{se.type} gives the option to compute
+#' heteroskedasticity-consistent (HC) or
+#' heteroskedasticity-autocorrelation-consistent (HAC) standard errors and
+#' t-statistics using \code{\link[lmtest]{coeftest}}. This option is meaningful
+#' only if \code{fit.method = "OLS" or "DLS"}.
+#'
+#' Standard errors are currently not available for
+#' \code{variable.selection="lars"} as there seems to be no consensus on a
+#' statistically valid method of calculating standard errors for the lasso
+#' predictions.
+#'
+#' @param object an object of class \code{tsfm} returned by \code{fitTsfm}.
+#' @param se.type one of "Default", "HC" or "HAC"; option for computing
+#' HC/HAC standard errors and t-statistics. Default is "Default".
+#' @param x an object of class \code{summary.tsfm}.
+#' @param digits number of significants digits to use when printing.
+#' Default is 3.
+#' @param trim option to supress labels and legend in the summary. When
+#' \code{TRUE}, only the coefficient matrx with standard eroors is printed.
+#' Default is \code{FALSE}.
+#' @param ... futher arguments passed to or from other methods.
+#'
+#' @return Returns an object of class \code{summary.tsfm}.
+#' The print method for class \code{summary.tsfm} 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.tsfm} is a list of length N + 2 containing:
+#' \item{call}{the function call to \code{fitTsfm}}
+#' \item{se.type}{standard error type as input}
+#' \item{sum.list}{list of summaries of the N fit objects (of class \code{lm},
+#' \code{lmRob} or \code{lars}) for each asset in the factor model.}
+#'
+#' @author Sangeetha Srinivasan & Yi-An Chen.
+#'
+#' @seealso \code{\link{fitTsfm}}, \code{\link[stats]{summary.lm}}
+#'
+#' @examples
+#' data(managers)
+#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
+#' factor.names=colnames(managers[,7:9]),
+#' data=managers)
+#'
+#' # summary of factor model fit for all assets
+#' summary(fit, "HAC")
+#'
+#' # summary of lm fit for a single asset
+#' summary(fit$asset.fit[[1]])
+#'
+#' @importFrom lmtest coeftest.default
+#' @importFrom sandwich vcovHC.default vcovHAC.default
+#'
+#' @method summary tsfm
+#' @export
+
+summary.tsfm <- function(object, se.type=c("Default","HC","HAC"), ...){
+
+ # check input object validity
+ if (!inherits(object, "tsfm")) {
+ stop("Invalid 'tsfm' object")
+ }
+
+ #set default for se.type
+ se.type = se.type[1]
+
+ # note: fit.method=NULL for "lars" objects
+ if (object$fit.method=="Robust" && se.type!="Default") {
+ stop("Invalid argument: HC/HAC standard errors are applicable only if
+ fit.method = 'OLS' or 'DLS'")
+ }
+
+ # extract summary.lm objects for each asset
+ sum.list <- lapply(object$asset.fit, summary)
+
+ # convert to HC/HAC standard errors and t-stats if specified
+ # extract coefficients separately for "lars" variable.selection method
+ for (i in object$asset.names) {
+ if (se.type=="HC") {
+ sum.list[[i]]$coefficients <- coeftest.default(object$asset.fit[[i]],
+ vcov.=vcovHC.default)[,1:4]
+ } else if (se.type=="HAC") {
+ sum.list[[i]]$coefficients <- coeftest.default(object$asset.fit[[i]],
+ vcov.=vcovHAC.default)[,1:4]
+ }
+ }
+
+ if (object$variable.selection=="lars") {
+ sum.list <- list()
+ for (i in object$asset.names) {
+ sum.list[[i]]$coefficients <- as.matrix(c(object$alpha[i], object$beta[i,]))
+ rownames(sum.list[[i]]$coefficients)[1]="(Intercept)"
+ colnames(sum.list[[i]]$coefficients)[1]="Estimate"
+ sum.list[[i]]$r.squared <- as.numeric(object$r2[i])
+ sum.list[[i]]$sigma <- as.numeric(object$resid.sd[i])
+ }
+ }
+
+ # include the call and se.type to fitTsfm
+ sum <- list(call=object$call, se.type=se.type, sum.list=sum.list)
+ class(sum) <- "summary.tsfm"
+ return(sum)
+}
+
+
+#' @rdname summary.tsfm
+#' @method print summary.tsfm
+#' @export
+
+print.summary.tsfm <- function(x, digits=3, trim=FALSE, ...) {
+ n <- length(x$sum.list)
+ if (trim==FALSE) {
+ if(!is.null(cl <- x$call)) {
+ cat("\nCall:\n")
+ dput(cl)
+ }
+ cat("\nFactor Model Coefficients:\n", sep="")
+ for (i in 1:n) {
+ options(digits = digits)
+ table.coef <- (x$sum.list)[[i]]$coefficients
+ if (dim(table.coef)[2] > 1) {
+ cat("\nAsset", i, ": ", names(x$sum.list[i]), "\n(", x$se.type,
+ " Standard Errors & T-stats)\n\n", sep="")
+ } else {
+ cat("\nAsset", i, ": ", names(x$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")
+ }
+ }
+}
Modified: pkg/FactorAnalytics/man/fitTsfm.Rd
===================================================================
--- pkg/FactorAnalytics/man/fitTsfm.Rd 2015-03-16 16:15:01 UTC (rev 3616)
+++ pkg/FactorAnalytics/man/fitTsfm.Rd 2015-03-16 20:41:50 UTC (rev 3617)
@@ -74,6 +74,7 @@
\item{data}{xts data object containing the assets and factors.}
\item{asset.names}{asset.names as input.}
\item{factor.names}{factor.names as input.}
+\item{mkt.name}{mkt.name as input}
\item{fit.method}{fit.method as input.}
\item{variable.selection}{variable.selection as input.}
Where N is the number of assets, K is the number of factors and T is the
Modified: pkg/FactorAnalytics/man/summary.tsfm.Rd
===================================================================
--- pkg/FactorAnalytics/man/summary.tsfm.Rd 2015-03-16 16:15:01 UTC (rev 3616)
+++ pkg/FactorAnalytics/man/summary.tsfm.Rd 2015-03-16 20:41:50 UTC (rev 3617)
@@ -1,73 +1,76 @@
-% Generated by roxygen2 (4.1.0): do not edit by hand
-% Please edit documentation in R/summary.tsfm.r
-\name{summary.tsfm}
-\alias{print.summary.tsfm}
-\alias{summary.tsfm}
-\title{Summarizing a fitted time series factor model}
-\usage{
-\method{summary}{tsfm}(object, se.type = c("Default", "HC", "HAC"), ...)
-
-\method{print}{summary.tsfm}(x, digits = 3, ...)
-}
-\arguments{
-\item{object}{an object of class \code{tsfm} returned by \code{fitTsfm}.}
-
-\item{se.type}{one of "Default", "HC" or "HAC"; option for computing
-HC/HAC standard errors and t-statistics. Default is "Default".}
-
-\item{...}{futher arguments passed to or from other methods.}
-
-\item{x}{an object of class \code{summary.tsfm}.}
-
-\item{digits}{number of significants digits to use when printing.
-Default is 3.}
-}
-\value{
-Returns an object of class \code{summary.tsfm}.
-The print method for class \code{summary.tsfm} 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.tsfm} is a list of length N + 2 containing:
-\item{call}{the function call to \code{fitTsfm}}
-\item{se.type}{standard error type as input}
-\item{sum.list}{list of summaries of the N fit objects (of class \code{lm},
-\code{lmRob} or \code{lars}) for each asset in the factor model.}
-}
-\description{
-\code{summary} method for object of class \code{tsfm}.
-Returned object is of class {summary.tsfm}.
-}
-\details{
-The default \code{summary} method for a fitted \code{lm} object
-computes the standard errors and t-statistics under the assumption of
-homoskedasticty. Argument \code{se.type} gives the option to compute
-heteroskedasticity-consistent (HC) or
-heteroskedasticity-autocorrelation-consistent (HAC) standard errors and
-t-statistics using \code{\link[lmtest]{coeftest}}. This option is meaningful
-only if \code{fit.method = "OLS" or "DLS"}.
-
-Standard errors are currently not available for
-\code{variable.selection="lars"} as there seems to be no consensus on a
-statistically valid method of calculating standard errors for the lasso
-predictions.
-}
-\examples{
-data(managers)
-fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
- factor.names=colnames(managers[,7:9]),
- data=managers)
-
-# summary of factor model fit for all assets
-summary(fit, "HAC")
-
-# summary of lm fit for a single asset
-summary(fit$asset.fit[[1]])
-}
-\author{
-Sangeetha Srinivasan & Yi-An Chen.
-}
-\seealso{
-\code{\link{fitTsfm}}, \code{\link[stats]{summary.lm}}
-}
-
+% Generated by roxygen2 (4.0.2): do not edit by hand
+\name{summary.tsfm}
+\alias{print.summary.tsfm}
+\alias{summary.tsfm}
+\title{Summarizing a fitted time series factor model}
+\usage{
+\method{summary}{tsfm}(object, se.type = c("Default", "HC", "HAC"), ...)
+
+\method{print}{summary.tsfm}(x, digits = 3, trim = FALSE, ...)
+}
+\arguments{
+\item{object}{an object of class \code{tsfm} returned by \code{fitTsfm}.}
+
+\item{se.type}{one of "Default", "HC" or "HAC"; option for computing
+HC/HAC standard errors and t-statistics. Default is "Default".}
+
+\item{...}{futher arguments passed to or from other methods.}
+
+\item{x}{an object of class \code{summary.tsfm}.}
+
+\item{digits}{number of significants digits to use when printing.
+Default is 3.}
+
+\item{trim}{option to supress labels and legend in the summary. When
+\code{TRUE}, only the coefficient matrx with standard eroors is printed.
+Default is \code{FALSE}.}
+}
+\value{
+Returns an object of class \code{summary.tsfm}.
+The print method for class \code{summary.tsfm} 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.tsfm} is a list of length N + 2 containing:
+\item{call}{the function call to \code{fitTsfm}}
+\item{se.type}{standard error type as input}
+\item{sum.list}{list of summaries of the N fit objects (of class \code{lm},
+\code{lmRob} or \code{lars}) for each asset in the factor model.}
+}
+\description{
+\code{summary} method for object of class \code{tsfm}.
+Returned object is of class {summary.tsfm}.
+}
+\details{
+The default \code{summary} method for a fitted \code{lm} object
+computes the standard errors and t-statistics under the assumption of
+homoskedasticty. Argument \code{se.type} gives the option to compute
+heteroskedasticity-consistent (HC) or
+heteroskedasticity-autocorrelation-consistent (HAC) standard errors and
+t-statistics using \code{\link[lmtest]{coeftest}}. This option is meaningful
+only if \code{fit.method = "OLS" or "DLS"}.
+
+Standard errors are currently not available for
+\code{variable.selection="lars"} as there seems to be no consensus on a
+statistically valid method of calculating standard errors for the lasso
+predictions.
+}
+\examples{
+data(managers)
+fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
+ factor.names=colnames(managers[,7:9]),
+ data=managers)
+
+# summary of factor model fit for all assets
+summary(fit, "HAC")
+
+# summary of lm fit for a single asset
+summary(fit$asset.fit[[1]])
+}
+\author{
+Sangeetha Srinivasan & Yi-An Chen.
+}
+\seealso{
+\code{\link{fitTsfm}}, \code{\link[stats]{summary.lm}}
+}
+
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