From 40bef56e8bbef25f00a63afe4713fea9d354f205 Mon Sep 17 00:00:00 2001
From: JustinMShea <jshea@roosevelt.edu>
Date: Sat, 21 Aug 2021 19:37:33 -0500
Subject: [PATCH] #43 fix examples related `managers` data containing spaces in
 names, which get filled in by "." during merge.xts.

---
 R/fmEsDecomp.R          | 21 +++++++--------------
 R/fmSdDecomp.R          | 15 ++++++++-------
 R/fmVaRDecomp.R         | 26 +++++++++++---------------
 R/fmmcSemiParam.R       | 10 +++++++++-
 R/portEsDecomp.R        | 17 +++++++++++++----
 R/portSdDecomp.R        | 11 ++++++++++-
 R/portVaRDecomp.R       | 12 +++++++++++-
 R/portVolDecomp.R       | 11 ++++++++++-
 R/predict.tsfmUpDn.r    | 16 +++++++++++-----
 R/print.tsfmUpDn.r      | 12 ++++++++++--
 R/summary.tsfm.r        |  6 ++++++
 R/summary.tsfmUpDn.r    | 12 +++++++++---
 man/fmEsDecomp.Rd       | 21 +++++++--------------
 man/fmSdDecomp.Rd       | 13 +++++++------
 man/fmVaRDecomp.Rd      | 26 +++++++++++---------------
 man/fmmcSemiParam.Rd    | 10 +++++++++-
 man/portEsDecomp.Rd     | 17 +++++++++++++----
 man/portSdDecomp.Rd     | 11 ++++++++++-
 man/portVaRDecomp.Rd    | 12 +++++++++++-
 man/portVolDecomp.Rd    | 11 ++++++++++-
 man/predict.tsfmUpDn.Rd | 15 +++++++++++----
 man/print.tsfmUpDn.Rd   | 12 ++++++++++--
 man/summary.tsfm.Rd     |  6 ++++++
 man/summary.tsfmUpDn.Rd | 12 +++++++++---
 24 files changed, 229 insertions(+), 106 deletions(-)

diff --git a/R/fmEsDecomp.R b/R/fmEsDecomp.R
index 9dc45dba..56fe23e8 100644
--- a/R/fmEsDecomp.R
+++ b/R/fmEsDecomp.R
@@ -63,27 +63,20 @@
 #' \code{\link{fmVaRDecomp}} for factor model VaR decomposition.
 #' 
 #' @examples
-#' #' # Time Series Factor Model
+#'  # Time Series Factor Model
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                      factor.names=colnames(managers[,(7:8)]), data=managers)
 #' ES.decomp <- fmEsDecomp(fit.macro)
 #' # get the component contributions
 #' ES.decomp$cES
 #' 
-#' # Statistical Factor Model
-#' data(StockReturns)
-#' sfm.pca.fit <- fitSfm(r.M, k=2)
-#' ES.decomp <- fmEsDecomp(sfm.pca.fit, type="normal")
-#' ES.decomp$cES
-#' 
-#' # Fundamental Factor Model
-#' data(Stocks.df)
-#' exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP")
-#' fit <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN", 
-#'               date.var="DATE", exposure.vars=exposure.vars)
-#' ES.decomp <- fmEsDecomp(fit, type="normal")
-#' head(ES.decomp$cES)
 #' 
 #' @export
 
diff --git a/R/fmSdDecomp.R b/R/fmSdDecomp.R
index 5db4d2c6..2686a3e5 100644
--- a/R/fmSdDecomp.R
+++ b/R/fmSdDecomp.R
@@ -55,7 +55,14 @@
 #' 
 #' @examples
 #' # Time Series Factor Model
+#' 
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                      factor.names=colnames(managers[,(7:9)]),
 #'                      rf.name="US.3m.TR", data=managers)
@@ -63,13 +70,7 @@
 #' # get the percentage component contributions
 #' decomp$pcSd
 #' 
-#' # Statistical Factor Model
-#' data(StockReturns)
-#' sfm.pca.fit <- fitSfm(r.M, k=2)
-#' decomp <- fmSdDecomp(sfm.pca.fit)
-#' decomp$pcSd
-#'  
-#' @export                                       
+#' @export  
 
 fmSdDecomp <- function(object, ...){
   # check input object validity
diff --git a/R/fmVaRDecomp.R b/R/fmVaRDecomp.R
index 221d8d72..f661aae5 100644
--- a/R/fmVaRDecomp.R
+++ b/R/fmVaRDecomp.R
@@ -63,25 +63,21 @@
 #' 
 #' @examples
 #' # Time Series Factor Model
+#' 
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-#'                      factor.names=colnames(managers[,(7:8)]), data=managers)
+#'                      factor.names=colnames(managers[,(7:8)]), 
+#'                      data=managers)
+#'                      
 #' VaR.decomp <- fmVaRDecomp(fit.macro)
-#' # get the component contributions
-#' VaR.decomp$cVaR
 #' 
-#' # Statistical Factor Model
-#' data(StockReturns)
-#' sfm.pca.fit <- fitSfm(r.M, k=2)
-#' VaR.decomp <- fmVaRDecomp(sfm.pca.fit, type="normal")
-#' VaR.decomp$cVaR
-#' 
-#' # Fundamental Factor Model
-#' data(Stocks.df)
-#' exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP")
-#' fit <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN", 
-#'               date.var="DATE", exposure.vars=exposure.vars)
-#' VaR.decomp <- fmVaRDecomp(fit, type="normal")
+#' # get the component contributions
 #' VaR.decomp$cVaR
 #' 
 #' @export
diff --git a/R/fmmcSemiParam.R b/R/fmmcSemiParam.R
index 750505a2..2c6a1546 100644
--- a/R/fmmcSemiParam.R
+++ b/R/fmmcSemiParam.R
@@ -58,9 +58,17 @@
 #' 
 #' @examples
 #' # fit a time series factor model for all assets
+#' 
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-#'                factor.names=colnames(managers[,(7:9)]), data=managers)
+#'                factor.names=colnames(managers[,(7:9)]), 
+#'                data=managers)
 #' 
 #' # bootstrap returns using the fitted factor model, Normal dist. for residuals
 #' resid.par <- as.matrix(fit$resid.sd,1,6)
diff --git a/R/portEsDecomp.R b/R/portEsDecomp.R
index 37298704..5a06d8ee 100644
--- a/R/portEsDecomp.R
+++ b/R/portEsDecomp.R
@@ -59,11 +59,21 @@
 #' 
 #' @examples
 #' # Time Series Factor Model
+#' 
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                      factor.names=colnames(managers[,(7:9)]),
-#'                      rf.name=colnames(managers[,10]), data=managers)
-#' ES.decomp <- portEsDecomp(fit.macro,invert = TRUE)
+#'                      rf.name=colnames(managers[,10]), 
+#'                      data=managers)
+#'                      
+#' ES.decomp <- portEsDecomp(fit.macro, invert = TRUE)
+#' 
 #' # get the component contributions
 #' ES.decomp$cES
 #' 
@@ -77,8 +87,7 @@
 #' data("stocks145scores6")
 #' dat = stocks145scores6
 #' dat$DATE = as.yearmon(dat$DATE)
-#' dat = dat[dat$DATE >=as.yearmon("2008-01-01") & 
-#'           dat$DATE <= as.yearmon("2012-12-31"),]
+#' dat = dat[dat$DATE >=as.yearmon("2008-01-01") & dat$DATE <= as.yearmon("2012-12-31"),]
 #'
 #' # Load long-only GMV weights for the return data
 #' data("wtsStocks145GmvLo")
diff --git a/R/portSdDecomp.R b/R/portSdDecomp.R
index 9ace0272..45c1ee2b 100644
--- a/R/portSdDecomp.R
+++ b/R/portSdDecomp.R
@@ -53,10 +53,19 @@
 #' 
 #' @examples
 #' # Time Series Factor Model
+#' 
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                      factor.names=colnames(managers[,(7:9)]),
-#'                      rf.name=colnames(managers[,10]), data=managers)
+#'                      rf.name=colnames(managers[,10]), 
+#'                      data=managers)
+#'                      
 #' decomp <- portSdDecomp(fit.macro)
 #' # get the factor contributions of risk
 #' decomp$cSd
diff --git a/R/portVaRDecomp.R b/R/portVaRDecomp.R
index be5d222b..f9483158 100644
--- a/R/portVaRDecomp.R
+++ b/R/portVaRDecomp.R
@@ -59,11 +59,21 @@
 #' 
 #' @examples
 #' # Time Series Factor Model
+#' 
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                      factor.names=colnames(managers[,(7:9)]),
-#'                      rf.name=colnames(managers[,10]), data=managers)
+#'                      rf.name=colnames(managers[,10]), 
+#'                      data=managers)
+#'                      
 #' decomp <- portVaRDecomp(fit.macro,invert = TRUE)
+#' 
 #' # get the factor contributions of risk
 #' decomp$cVaR
 #' 
diff --git a/R/portVolDecomp.R b/R/portVolDecomp.R
index 7672aa13..ca45162d 100644
--- a/R/portVolDecomp.R
+++ b/R/portVolDecomp.R
@@ -32,10 +32,19 @@
 #' 
 #' @examples
 #' # Time Series Factor Model
+#' 
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                      factor.names=colnames(managers[,(7:9)]),
-#'                      rf.name=colnames(managers[,10]), data=managers)
+#'                      rf.name=colnames(managers[,10]), 
+#'                      data=managers)
+#'                      
 #' decomp <- portVolDecomp(fit.macro)
 #' decomp
 #' 
diff --git a/R/predict.tsfmUpDn.r b/R/predict.tsfmUpDn.r
index a035c93b..8a85747f 100644
--- a/R/predict.tsfmUpDn.r
+++ b/R/predict.tsfmUpDn.r
@@ -17,12 +17,18 @@
 #' @seealso \code{\link{predict.tsfm}},\code{\link{fitTsfmUpDn}}, \code{\link{summary.tsfmUpDn}}
 #' 
 #' @examples
-#' # load data from the database
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
-#' # fit the factor model with LS
-#  example: Up and down market factor model with LS fit
-#' fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),mkt.name="SP500.TR",
-#'                        data=managers, fit.method="LS")
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
+#' # fit the factor model with LS. example: Up and down market factor model with LS fit
+#' fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
+#'                        mkt.name="SP500.TR",
+#'                        data=managers, 
+#'                        fit.method="LS")
 #'  
 #' predict(fitUpDn)
 #' 
diff --git a/R/print.tsfmUpDn.r b/R/print.tsfmUpDn.r
index 10188a62..240c4902 100644
--- a/R/print.tsfmUpDn.r
+++ b/R/print.tsfmUpDn.r
@@ -14,10 +14,18 @@
 #' @seealso \code{\link{fitTsfmUpDn}}, \code{\link{summary.tsfmUpDn}}
 #' 
 #' @examples
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' # example: Up and down market factor model with LS fit
-#' fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),mkt.name="SP500.TR",
-#'                        data=managers, fit.method="LS",control=NULL)
+#' fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
+#'                        mkt.name="SP500.TR",
+#'                        data=managers, 
+#'                        fit.method="LS")
 #'  
 #' print(fitUpDn)
 #' 
diff --git a/R/summary.tsfm.r b/R/summary.tsfm.r
index f85fa979..1c716d46 100644
--- a/R/summary.tsfm.r
+++ b/R/summary.tsfm.r
@@ -43,7 +43,13 @@
 #' @seealso \code{\link{fitTsfm}}, \code{\link[stats]{summary.lm}}
 #' 
 #' @examples
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
+#' 
 #' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                factor.names=colnames(managers[,7:9]), 
 #'                data=managers)
diff --git a/R/summary.tsfmUpDn.r b/R/summary.tsfmUpDn.r
index fb0def0d..57caa8b5 100644
--- a/R/summary.tsfmUpDn.r
+++ b/R/summary.tsfmUpDn.r
@@ -32,12 +32,18 @@
 #' @seealso \code{\link{fitTsfmUpDn}}, \code{\link{summary.tsfm}}
 #' 
 #' @examples
-#' # load data from the database
+#'  # load data
 #' data(managers, package = 'PerformanceAnalytics')
+#' colnames(managers)
+#'  # Make syntactically valid column names
+#' colnames(managers) <- make.names( colnames(managers))
+#' colnames(managers)
 #' 
 #' # example: Up and down market factor model with LS fit
-#' fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),mkt.name="SP500.TR",
-#'                        data=managers, fit.method="LS",control=NULL)
+#' fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
+#'                        mkt.name="SP500.TR",
+#'                        data=managers, 
+#'                        fit.method="LS")
 #'  
 #'  summary(fitUpDn)
 #' 
diff --git a/man/fmEsDecomp.Rd b/man/fmEsDecomp.Rd
index 1980e542..786055d3 100644
--- a/man/fmEsDecomp.Rd
+++ b/man/fmEsDecomp.Rd
@@ -86,27 +86,20 @@ Refer to Eric Zivot's slides (referenced) for formulas pertaining to the
 calculation of Normal ES (adapted from a portfolio context to factor models).
 }
 \examples{
-#' # Time Series Factor Model
+ # Time Series Factor Model
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
                      factor.names=colnames(managers[,(7:8)]), data=managers)
 ES.decomp <- fmEsDecomp(fit.macro)
 # get the component contributions
 ES.decomp$cES
 
-# Statistical Factor Model
-data(StockReturns)
-sfm.pca.fit <- fitSfm(r.M, k=2)
-ES.decomp <- fmEsDecomp(sfm.pca.fit, type="normal")
-ES.decomp$cES
-
-# Fundamental Factor Model
-data(Stocks.df)
-exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP")
-fit <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN", 
-              date.var="DATE", exposure.vars=exposure.vars)
-ES.decomp <- fmEsDecomp(fit, type="normal")
-head(ES.decomp$cES)
 
 }
 \references{
diff --git a/man/fmSdDecomp.Rd b/man/fmSdDecomp.Rd
index b2609a12..e570d24b 100644
--- a/man/fmSdDecomp.Rd
+++ b/man/fmSdDecomp.Rd
@@ -57,7 +57,14 @@ sample covariance. \cr \cr
 }
 \examples{
 # Time Series Factor Model
+
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
                      factor.names=colnames(managers[,(7:9)]),
                      rf.name="US.3m.TR", data=managers)
@@ -65,12 +72,6 @@ decomp <- fmSdDecomp(fit.macro)
 # get the percentage component contributions
 decomp$pcSd
 
-# Statistical Factor Model
-data(StockReturns)
-sfm.pca.fit <- fitSfm(r.M, k=2)
-decomp <- fmSdDecomp(sfm.pca.fit)
-decomp$pcSd
- 
 }
 \references{
 Hallerback (2003). Decomposing Portfolio Value-at-Risk: A General Analysis. 
diff --git a/man/fmVaRDecomp.Rd b/man/fmVaRDecomp.Rd
index 5dd29fe4..3906f08d 100644
--- a/man/fmVaRDecomp.Rd
+++ b/man/fmVaRDecomp.Rd
@@ -89,25 +89,21 @@ calculation of Normal VaR (adapted from a portfolio context to factor models)
 }
 \examples{
 # Time Series Factor Model
+
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-                     factor.names=colnames(managers[,(7:8)]), data=managers)
+                     factor.names=colnames(managers[,(7:8)]), 
+                     data=managers)
+                     
 VaR.decomp <- fmVaRDecomp(fit.macro)
-# get the component contributions
-VaR.decomp$cVaR
 
-# Statistical Factor Model
-data(StockReturns)
-sfm.pca.fit <- fitSfm(r.M, k=2)
-VaR.decomp <- fmVaRDecomp(sfm.pca.fit, type="normal")
-VaR.decomp$cVaR
-
-# Fundamental Factor Model
-data(Stocks.df)
-exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP")
-fit <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN", 
-              date.var="DATE", exposure.vars=exposure.vars)
-VaR.decomp <- fmVaRDecomp(fit, type="normal")
+# get the component contributions
 VaR.decomp$cVaR
 
 }
diff --git a/man/fmmcSemiParam.Rd b/man/fmmcSemiParam.Rd
index 662dbfd7..c644d1d5 100644
--- a/man/fmmcSemiParam.Rd
+++ b/man/fmmcSemiParam.Rd
@@ -77,9 +77,17 @@ and "block" corresponds to stationary block bootstrap-- Politis and Romano
 }
 \examples{
 # fit a time series factor model for all assets
+
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-               factor.names=colnames(managers[,(7:9)]), data=managers)
+               factor.names=colnames(managers[,(7:9)]), 
+               data=managers)
 
 # bootstrap returns using the fitted factor model, Normal dist. for residuals
 resid.par <- as.matrix(fit$resid.sd,1,6)
diff --git a/man/portEsDecomp.Rd b/man/portEsDecomp.Rd
index ffa559ac..f8659f3b 100644
--- a/man/portEsDecomp.Rd
+++ b/man/portEsDecomp.Rd
@@ -86,11 +86,21 @@ average of the observations in that data window.
 }
 \examples{
 # Time Series Factor Model
+
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
                      factor.names=colnames(managers[,(7:9)]),
-                     rf.name=colnames(managers[,10]), data=managers)
-ES.decomp <- portEsDecomp(fit.macro,invert = TRUE)
+                     rf.name=colnames(managers[,10]), 
+                     data=managers)
+                     
+ES.decomp <- portEsDecomp(fit.macro, invert = TRUE)
+
 # get the component contributions
 ES.decomp$cES
 
@@ -104,8 +114,7 @@ portEsDecomp(fit.macro, wts)
 data("stocks145scores6")
 dat = stocks145scores6
 dat$DATE = as.yearmon(dat$DATE)
-dat = dat[dat$DATE >=as.yearmon("2008-01-01") & 
-          dat$DATE <= as.yearmon("2012-12-31"),]
+dat = dat[dat$DATE >=as.yearmon("2008-01-01") & dat$DATE <= as.yearmon("2012-12-31"),]
 
 # Load long-only GMV weights for the return data
 data("wtsStocks145GmvLo")
diff --git a/man/portSdDecomp.Rd b/man/portSdDecomp.Rd
index c47da970..bc1ba9c1 100644
--- a/man/portSdDecomp.Rd
+++ b/man/portSdDecomp.Rd
@@ -65,10 +65,19 @@ sample covariance. \cr \cr
 }
 \examples{
 # Time Series Factor Model
+
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
                      factor.names=colnames(managers[,(7:9)]),
-                     rf.name=colnames(managers[,10]), data=managers)
+                     rf.name=colnames(managers[,10]), 
+                     data=managers)
+                     
 decomp <- portSdDecomp(fit.macro)
 # get the factor contributions of risk
 decomp$cSd
diff --git a/man/portVaRDecomp.Rd b/man/portVaRDecomp.Rd
index ece76d37..98ee9a39 100644
--- a/man/portVaRDecomp.Rd
+++ b/man/portVaRDecomp.Rd
@@ -87,11 +87,21 @@ Epperlein & Smillie (2006); a triangular smoothing kernel is used here.
 }
 \examples{
 # Time Series Factor Model
+
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
                      factor.names=colnames(managers[,(7:9)]),
-                     rf.name=colnames(managers[,10]), data=managers)
+                     rf.name=colnames(managers[,10]), 
+                     data=managers)
+                     
 decomp <- portVaRDecomp(fit.macro,invert = TRUE)
+
 # get the factor contributions of risk
 decomp$cVaR
 
diff --git a/man/portVolDecomp.Rd b/man/portVolDecomp.Rd
index 3bc0fe6b..2b012440 100644
--- a/man/portVolDecomp.Rd
+++ b/man/portVolDecomp.Rd
@@ -45,10 +45,19 @@ Decompose portfolio variance risk into factor/residual risk
 }
 \examples{
 # Time Series Factor Model
+
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
                      factor.names=colnames(managers[,(7:9)]),
-                     rf.name=colnames(managers[,10]), data=managers)
+                     rf.name=colnames(managers[,10]), 
+                     data=managers)
+                     
 decomp <- portVolDecomp(fit.macro)
 decomp
 
diff --git a/man/predict.tsfmUpDn.Rd b/man/predict.tsfmUpDn.Rd
index 3f119b47..a4d50111 100644
--- a/man/predict.tsfmUpDn.Rd
+++ b/man/predict.tsfmUpDn.Rd
@@ -22,11 +22,18 @@ S3 \code{predict} method for object of class \code{tsfmUpDn}. It
 calls the \code{predict.tsfm} method for a list object of \code{Up} and \code{Dn}
 }
 \examples{
-# load data from the database
+ # load data
 data(managers, package = 'PerformanceAnalytics')
-# fit the factor model with LS
-fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),mkt.name="SP500.TR",
-                       data=managers, fit.method="LS")
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
+# fit the factor model with LS. example: Up and down market factor model with LS fit
+fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
+                       mkt.name="SP500.TR",
+                       data=managers, 
+                       fit.method="LS")
  
 predict(fitUpDn)
 
diff --git a/man/print.tsfmUpDn.Rd b/man/print.tsfmUpDn.Rd
index 62639e35..762a9200 100644
--- a/man/print.tsfmUpDn.Rd
+++ b/man/print.tsfmUpDn.Rd
@@ -20,10 +20,18 @@ the call, factor model dimension, regression coefficients, r-squared and
 residual volatilities from the fitted object.
 }
 \examples{
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 # example: Up and down market factor model with LS fit
-fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),mkt.name="SP500.TR",
-                       data=managers, fit.method="LS",control=NULL)
+fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
+                       mkt.name="SP500.TR",
+                       data=managers, 
+                       fit.method="LS")
  
 print(fitUpDn)
 
diff --git a/man/summary.tsfm.Rd b/man/summary.tsfm.Rd
index f96cafa8..906c4abd 100644
--- a/man/summary.tsfm.Rd
+++ b/man/summary.tsfm.Rd
@@ -57,7 +57,13 @@ statistically valid method of calculating standard errors for the lasso
 predictions.
 }
 \examples{
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
+
 fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
                factor.names=colnames(managers[,7:9]), 
                data=managers)
diff --git a/man/summary.tsfmUpDn.Rd b/man/summary.tsfmUpDn.Rd
index 99a35e66..025e28ce 100644
--- a/man/summary.tsfmUpDn.Rd
+++ b/man/summary.tsfmUpDn.Rd
@@ -43,12 +43,18 @@ Since \code{fitTsfmUpDn} fits both up market and down market,
 objects and combines the coefficients interested together.
 }
 \examples{
-# load data from the database
+ # load data
 data(managers, package = 'PerformanceAnalytics')
+colnames(managers)
+ # Make syntactically valid column names
+colnames(managers) <- make.names( colnames(managers))
+colnames(managers)
 
 # example: Up and down market factor model with LS fit
-fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),mkt.name="SP500.TR",
-                       data=managers, fit.method="LS",control=NULL)
+fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
+                       mkt.name="SP500.TR",
+                       data=managers, 
+                       fit.method="LS")
  
  summary(fitUpDn)