[Returnanalytics-commits] r2449 - pkg/FactorAnalytics/man

[noreply at r-forge.r-project.org]

Author: chenyian
Date: 2013-06-26 21:45:48 +0200 (Wed, 26 Jun 2013)
New Revision: 2449

Removed:
   pkg/FactorAnalytics/man/fitMacroeconomicFactorModel.Rd
Log:


Deleted: pkg/FactorAnalytics/man/fitMacroeconomicFactorModel.Rd
===================================================================
--- pkg/FactorAnalytics/man/fitMacroeconomicFactorModel.Rd	2013-06-26 18:38:19 UTC (rev 2448)
+++ pkg/FactorAnalytics/man/fitMacroeconomicFactorModel.Rd	2013-06-26 19:45:48 UTC (rev 2449)
@@ -1,111 +0,0 @@
-\name{fitMacroeconomicFactorModel}
-\alias{fitMacroeconomicFactorModel}
-\title{Fit macroeconomic factor model by time series regression techniques.}
-\usage{
-  fitMacroeconomicFactorModel(assets.names, factors.names,
-    data = data, factor.set = 3,
-    fit.method = c("OLS", "DLS", "Robust"),
-    variable.selection = c("stepwise", "all subsets", "lar", "lasso"),
-    decay.factor = 0.95, nvmax = 8, force.in = NULL,
-    subsets.method = c("exhaustive", "backward", "forward", "seqrep"),
-    lars.criteria = c("Cp", "cv"))
-}
-\arguments{
-  \item{assets.names}{names of assets returns.}
-
-  \item{factors.names}{names of factors returns.}
-
-  \item{factor.set}{scalar, number of factors}
-
-  \item{data}{a vector, matrix, data.frame, xts, timeSeries
-  or zoo object with asset returns and factors retunrs
-  rownames}
-
-  \item{fit.method}{"OLS" is ordinary least squares method,
-  "DLS" is discounted least squares method. Discounted
-  least squares (DLS) estimation is weighted least squares
-  estimation with exponentially declining weights that sum
-  to unity. "Robust"}
-
-  \item{variable.selection}{"stepwise" is traditional
-  forward/backward stepwise OLS regression, starting from
-  the initial set of factors, that adds factors only if the
-  regression fit as measured by the Bayesian Information
-  Criteria (BIC) or Akaike Information Criteria (AIC) can
-  be done using the R function step() from the stats
-  package. If \code{Robust} is chosen, the function
-  step.lmRob in Robust package will be used. "all subsets"
-  is Traditional all subsets regression can be done using
-  the R function regsubsets() from the package leaps. "lar"
-  , "lasso" is based on package "lars", linear angle
-  regression.}
-
-  \item{decay.factor}{for DLS. Default is 0.95.}
-
-  \item{nvmax}{control option for all subsets. maximum size
-  of subsets to examine}
-
-  \item{force.in}{control option for all subsets. The
-  factors that should be in all models.}
-
-  \item{subsets.method}{control option for all subsets. se
-  exhaustive search, forward selection, backward selection
-  or sequential replacement to search.}
-
-  \item{lars.criteria}{either choose minimum "Cp": unbiased
-  estimator of the true rist or "cv" 10 folds
-  cross-validation. See detail.}
-}
-\value{
-  an S3 object containing \item{asset.fit}{Fit objects for
-  each asset. This is the class "lm" for each object.}
-  \item{alpha.vec}{N x 1 Vector of estimated alphas.}
-  \item{beta.mat}{N x K Matrix of estimated betas.}
-  \item{r2.vec}{N x 1 Vector of R-square values.}
-  \item{residVars.vec}{N x 1 Vector of residual variances.}
-  \item{call}{function call.} \item{ret.assets}{Assets
-  returns of input data.} \item{factors Factors of input
-  data.} \item{variable.selection variables selected by the
-  user.}
-}
-\description{
-  Fit macroeconomic factor model by time series regression
-  techniques. It creates the class of "MacroFactorModel".
-}
-\details{
-  If \code{Robust} is chosen, there is no subsets but all
-  factors will be used.  Cp is defined in
-  http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf.
-  p17.
-}
-\examples{
-\dontrun{
-# load data from the database
-data(managers.df)
-ret.assets = managers.df[,(1:6)]
-factors    = managers.df[,(7:9)]
-# fit the factor model with OLS
-fit <- fitMacroeconomicFactorModel(ret.assets,factors,fit.method="OLS",
-                                 variable.selection="all subsets")
-# summary of HAM1
-summary(fit$asset.fit$HAM1)
-# plot actual vs. fitted over time for HAM1
-# use chart.TimeSeries() function from PerformanceAnalytics package
-dataToPlot = cbind(fitted(fit$asset.fit$HAM1), na.omit(managers.df$HAM1))
-colnames(dataToPlot) = c("Fitted","Actual")
-chart.TimeSeries(dataToPlot, main="FM fit for HAM1",
-                 colorset=c("black","blue"), legend.loc="bottomleft")
- }
-}
-\author{
-  Eric Zivot and Yi-An Chen.
-}
-\references{
-  1. Efron, Hastie, Johnstone and Tibshirani (2002) "Least
-  Angle Regression" (with discussion) Annals of Statistics;
-  see also
-  http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf.
-  2. Hastie, Tibshirani and Friedman (2008) Elements of
-  Statistical Learning 2nd edition, Springer, NY.
-}
-