[Returnanalytics-commits] r3431 - in pkg/FactorAnalytics: . man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Jun 20 05:48:49 CEST 2014
Author: gyollin
Date: 2014-06-20 05:48:49 +0200 (Fri, 20 Jun 2014)
New Revision: 3431
Modified:
pkg/FactorAnalytics/DESCRIPTION
pkg/FactorAnalytics/man/plot.TimeSeriesFactorModel.Rd
pkg/FactorAnalytics/man/predict.FundamentalFactorModel.Rd
Log:
Fixed non-ASCII character in DESCRIPTION file.
Also, some Rd files got updated with the latest build
Modified: pkg/FactorAnalytics/DESCRIPTION
===================================================================
--- pkg/FactorAnalytics/DESCRIPTION 2014-06-20 00:08:48 UTC (rev 3430)
+++ pkg/FactorAnalytics/DESCRIPTION 2014-06-20 03:48:49 UTC (rev 3431)
@@ -1,12 +1,30 @@
-Package: factorAnalytics
-Type: Package
-Title: Factor Analytics
-Version: 1.0
-Date: 2014-06-18
-Author: Eric Zivot and Yi-An Chen
-Maintainer: Yi-An Chen <chenyian at uw.edu>
-Description: An R package for the estimation and risk analysis of linear factor models for asset returns and portfolios. It contains model fitting methods for the three major types of factor models: time series (or, macroeconomic) factor model, fundamental factor model and statistical factor model. They allow for different types of distributions to be specified for modeling the fat-tailed behavior of financial returns, including Edgeworth expansions. Risk analysis measures such as VaR and ES are also provided for the results of the fitted models.
-License: GPL-2
-Depends: R (≥ 2.15.1), robust, leaps, lars, PerformanceAnalytics, sn, tseries, strucchange, ellipse, doParallel
-Suggests: testthat
-LazyLoad: yes
+Package: factorAnalytics
+Type: Package
+Title: Factor Analytics
+Version: 1.0
+Date: 2014-06-18
+Author: Eric Zivot and Yi-An Chen
+Maintainer: Yi-An Chen <chenyian at uw.edu>
+Description: An R package for the estimation and risk analysis of linear factor
+ models for asset returns and portfolios. It contains model fitting methods
+ for the three major types of factor models: time series (or, macroeconomic)
+ factor model, fundamental factor model and statistical factor model. They
+ allow for different types of distributions to be specified for modeling the
+ fat-tailed behavior of financial returns, including Edgeworth expansions.
+ Risk analysis measures such as VaR and ES are also provided for the results
+ of the fitted models.
+License: GPL-2
+Depends:
+ R (>= 2.14.0),
+ robust,
+ leaps,
+ lars,
+ PerformanceAnalytics,
+ sn,
+ tseries,
+ strucchange,
+ ellipse,
+ doParallel
+Suggests:
+ testthat
+LazyLoad: yes
Modified: pkg/FactorAnalytics/man/plot.TimeSeriesFactorModel.Rd
===================================================================
--- pkg/FactorAnalytics/man/plot.TimeSeriesFactorModel.Rd 2014-06-20 00:08:48 UTC (rev 3430)
+++ pkg/FactorAnalytics/man/plot.TimeSeriesFactorModel.Rd 2014-06-20 03:48:49 UTC (rev 3431)
@@ -1,75 +1,76 @@
-% Generated by roxygen2 (4.0.1): do not edit by hand
-\name{plot.TimeSeriesFactorModel}
-\alias{plot.TimeSeriesFactorModel}
-\title{plot TimeSeriesFactorModel object.}
-\usage{
-\method{plot}{TimeSeriesFactorModel}(x, colorset = c(1:12),
- legend.loc = NULL, which.plot = c("none", "1L", "2L", "3L", "4L", "5L",
- "6L", "7L"), max.show = 6, plot.single = FALSE, asset.name,
- which.plot.single = c("none", "1L", "2L", "3L", "4L", "5L", "6L", "7L",
- "8L", "9L", "10L", "11L", "12L", "13L"), VaR.method = "historical")
-}
-\arguments{
-\item{x}{fit object created by \code{fitTimeSeriesFactorModel}.}
-
-\item{colorset}{Defualt colorset the same as \code{barplot}.}
-
-\item{legend.loc}{Plot legend or not. Defualt is \code{NULL}.}
-
-\item{which.plot}{Integer indicates which plot to create: "none" will
-create a menu to choose. Defualt is none.\cr
-1 = "Fitted factor returns", \cr
-2 = "R square", \cr
-3 = "Variance of Residuals",\cr
-4 = "FM Correlation",\cr
-5 = "Factor Contributions to SD",\cr
-6 = "Factor Contributions to ES",\cr
-7 = "Factor Contributions to VaR"}
-
-\item{max.show}{Maximum assets to plot. Default is 6.}
-
-\item{plot.single}{Plot a single asset of lm class. Defualt is \code{FALSE}.}
-
-\item{asset.name}{Name of the asset to be plotted.}
-
-\item{which.plot.single}{Integer indicates which plot to create: "none"
-will create a menu to choose. Defualt is none.\cr
- 1 = time series plot of actual and fitted values,\cr
- 2 = time series plot of residuals with standard error bands, \cr
- 3 = time series plot of squared residuals, \cr
- 4 = time series plot of absolute residuals,\cr
- 5 = SACF and PACF of residuals,\cr
- 6 = SACF and PACF of squared residuals,\cr
- 7 = SACF and PACF of absolute residuals,\cr
- 8 = histogram of residuals with normal curve overlayed,\cr
- 9 = normal qq-plot of residuals,\cr
- 10= CUSUM plot of recursive residuals,\cr
- 11= CUSUM plot of OLS residuals,\cr
- 12= CUSUM plot of recursive estimates relative to full sample estimates,\cr
- 13= rolling estimates over 24 month window.}
-
-\item{VaR.method}{Character, method for computing VaR. Valid choices are
-either "modified","gaussian","historical", "kernel". computation is done with the \code{VaR}
-in the PerformanceAnalytics package. Default is "historical".}
-}
-\description{
-Generic function of plot method for fitTimeSeriesFactorModel. Either plot
-all assets or choose a single asset to plot.
-}
-\examples{
-\dontrun{
-# load data from the database
-data(managers.df)
-fit.macro <- fitTimeseriesFactorModel(assets.names=colnames(managers.df[,(1:6)]),
- factors.names=c("EDHEC.LS.EQ","SP500.TR"),
- data=managers.df,fit.method="OLS")
-# plot of all assets and show only first 4 assets.
-plot(fit.macro,max.show=4)
-# single plot of HAM1 asset
-plot(fit.macro, plot.single=TRUE, asset.name="HAM1")
-}
-}
-\author{
-Eric Zivot and Yi-An Chen.
-}
-
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{plot.TimeSeriesFactorModel}
+\alias{plot.TimeSeriesFactorModel}
+\title{plot TimeSeriesFactorModel object.}
+\usage{
+\method{plot}{TimeSeriesFactorModel}(x, colorset = c(1:12),
+ legend.loc = NULL, which.plot = c("none", "1L", "2L", "3L", "4L", "5L",
+ "6L", "7L"), max.show = 6, plot.single = FALSE, asset.name,
+ which.plot.single = c("none", "1L", "2L", "3L", "4L", "5L", "6L", "7L",
+ "8L", "9L", "10L", "11L", "12L", "13L"), VaR.method = "historical", ...)
+}
+\arguments{
+\item{x}{fit object created by \code{fitTimeSeriesFactorModel}.}
+
+\item{colorset}{Defualt colorset the same as \code{barplot}.}
+
+\item{legend.loc}{Plot legend or not. Defualt is \code{NULL}.}
+
+\item{which.plot}{Integer indicates which plot to create: "none" will
+create a menu to choose. Defualt is none.\cr
+1 = "Fitted factor returns", \cr
+2 = "R square", \cr
+3 = "Variance of Residuals",\cr
+4 = "FM Correlation",\cr
+5 = "Factor Contributions to SD",\cr
+6 = "Factor Contributions to ES",\cr
+7 = "Factor Contributions to VaR"}
+
+\item{max.show}{Maximum assets to plot. Default is 6.}
+
+\item{plot.single}{Plot a single asset of lm class. Defualt is \code{FALSE}.}
+
+\item{asset.name}{Name of the asset to be plotted.}
+
+\item{which.plot.single}{Integer indicates which plot to create: "none"}
+
+\item{...}{will create a menu to choose. Defualt is none.\cr
+1 = time series plot of actual and fitted values,\cr
+2 = time series plot of residuals with standard error bands, \cr
+3 = time series plot of squared residuals, \cr
+4 = time series plot of absolute residuals,\cr
+5 = SACF and PACF of residuals,\cr
+6 = SACF and PACF of squared residuals,\cr
+7 = SACF and PACF of absolute residuals,\cr
+8 = histogram of residuals with normal curve overlayed,\cr
+9 = normal qq-plot of residuals,\cr
+10= CUSUM plot of recursive residuals,\cr
+11= CUSUM plot of OLS residuals,\cr
+12= CUSUM plot of recursive estimates relative to full sample estimates,\cr
+13= rolling estimates over 24 month window.}
+
+\item{VaR.method}{Character, method for computing VaR. Valid choices are
+either "modified","gaussian","historical", "kernel". computation is done with the \code{VaR}
+in the PerformanceAnalytics package. Default is "historical".}
+}
+\description{
+Generic function of plot method for fitTimeSeriesFactorModel. Either plot
+all assets or choose a single asset to plot.
+}
+\examples{
+\dontrun{
+# load data from the database
+data(managers.df)
+fit.macro <- fitTimeseriesFactorModel(assets.names=colnames(managers.df[,(1:6)]),
+ factors.names=c("EDHEC.LS.EQ","SP500.TR"),
+ data=managers.df,fit.method="OLS")
+# plot of all assets and show only first 4 assets.
+plot(fit.macro,max.show=4)
+# single plot of HAM1 asset
+plot(fit.macro, plot.single=TRUE, asset.name="HAM1")
+}
+}
+\author{
+Eric Zivot and Yi-An Chen.
+}
+
Modified: pkg/FactorAnalytics/man/predict.FundamentalFactorModel.Rd
===================================================================
--- pkg/FactorAnalytics/man/predict.FundamentalFactorModel.Rd 2014-06-20 00:08:48 UTC (rev 3430)
+++ pkg/FactorAnalytics/man/predict.FundamentalFactorModel.Rd 2014-06-20 03:48:49 UTC (rev 3431)
@@ -1,45 +1,47 @@
-% Generated by roxygen2 (4.0.1): do not edit by hand
-\name{predict.FundamentalFactorModel}
-\alias{predict.FundamentalFactorModel}
-\title{predict method for FundamentalFactorModel object}
-\usage{
-\method{predict}{FundamentalFactorModel}(object, newdata, new.assetvar,
- new.datevar)
-}
-\arguments{
-\item{object}{fit "FundamentalFactorModel" object}
-
-\item{newdata}{An optional data frame in which to look for variables with which to predict.
-If omitted, the fitted values are used.}
-
-\item{new.assetvar}{Specify new asset variable in newdata if newdata is provided.}
-
-\item{new.datevar}{Speficy new date variable in newdata if newdata is provided.}
-}
-\description{
-Generic function of predict method for fitFundamentalFactorModel.
-}
-\details{
-\code{newdata} must be data.frame and contain date variable, asset variable and exact
-exposures names that are used in fit object by \code{fitFundamentalFactorModel}
-}
-\examples{
-data(Stock.df)
-fit.fund <- fitFundamentalFactorModel(exposure.names=c("BOOK2MARKET", "LOG.MARKETCAP")
- , data=stock,returnsvar = "RETURN",datevar = "DATE",
- assetvar = "TICKER",
- wls = TRUE, regression = "classic",
- covariance = "classic", full.resid.cov = FALSE)
-# If not specify anything, predict() will give fitted value
-pred.fund <- predict(fit.fund)
-
-# generate random data
-testdata <- stock[,c("DATE","TICKER")]
-testdata$BOOK2MARKET <- rnorm(n=42465)
-testdata$LOG.MARKETCAP <- rnorm(n=42465)
-pred.fund2 <- predict(fit.fund,testdata,new.assetvar="TICKER",new.datevar="DATE")
-}
-\author{
-Yi-An Chen
-}
-
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{predict.FundamentalFactorModel}
+\alias{predict.FundamentalFactorModel}
+\title{predict method for FundamentalFactorModel object}
+\usage{
+\method{predict}{FundamentalFactorModel}(object, newdata, new.assetvar,
+ new.datevar, ...)
+}
+\arguments{
+\item{object}{fit "FundamentalFactorModel" object}
+
+\item{newdata}{An optional data frame in which to look for variables with which to predict.
+If omitted, the fitted values are used.}
+
+\item{new.assetvar}{Specify new asset variable in newdata if newdata is provided.}
+
+\item{new.datevar}{Speficy new date variable in newdata if newdata is provided.}
+
+\item{...}{}
+}
+\description{
+Generic function of predict method for fitFundamentalFactorModel.
+}
+\details{
+\code{newdata} must be data.frame and contain date variable, asset variable and exact
+exposures names that are used in fit object by \code{fitFundamentalFactorModel}
+}
+\examples{
+data(Stock.df)
+fit.fund <- fitFundamentalFactorModel(exposure.names=c("BOOK2MARKET", "LOG.MARKETCAP")
+ , data=stock,returnsvar = "RETURN",datevar = "DATE",
+ assetvar = "TICKER",
+ wls = TRUE, regression = "classic",
+ covariance = "classic", full.resid.cov = FALSE)
+# If not specify anything, predict() will give fitted value
+pred.fund <- predict(fit.fund)
+
+# generate random data
+testdata <- stock[,c("DATE","TICKER")]
+testdata$BOOK2MARKET <- rnorm(n=42465)
+testdata$LOG.MARKETCAP <- rnorm(n=42465)
+pred.fund2 <- predict(fit.fund,testdata,new.assetvar="TICKER",new.datevar="DATE")
+}
+\author{
+Yi-An Chen
+}
+
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