fix various code issues to run the example files.

remove dependencies on stocks.df
fix fitTfsm on missing beta rownames

R/fitTsfm.R

@@ -257,7 +257,8 @@ fitTsfm <- function(asset.names, factor.names, mkt.name=NULL, rf.name=NULL,
   tmp <- matrix(NA, length(asset.names), length(factor.names))
   colnames(tmp) <- factor.names
   rownames(tmp) <- asset.names
-  beta <- merge(beta, tmp, all.x=TRUE, sort=FALSE)[,factor.names, drop=FALSE]
+  beta <- merge(beta, tmp,all.x=TRUE, sort=FALSE)[,factor.names, drop=FALSE]
+  row.names(beta) = asset.names
   # extract r2 and residual sd
   r2 <- sapply(reg.list, function(x) summary(x)$r.squared)
   if (fit.method=="DLS") {

R/fmmcSemiParam.R

@@ -69,7 +69,7 @@
 #' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
 #'                factor.names=colnames(managers[,(7:9)]), 
 #'                data=managers)
-#' 
+#' \dontrun{
 #' # bootstrap returns using the fitted factor model, Normal dist. for residuals
 #' resid.par <- as.matrix(fit$resid.sd,1,6)
 #' fmmc.returns <- fmmcSemiParam(factor.ret=managers[,(7:9)], beta=fit$beta, 
@@ -101,7 +101,7 @@
 #' fmmc.returns.ffm <- fmmcSemiParam(factor.ret=fit.ffm$factor.returns,
 #'                                    beta=fit.ffm$beta, resid.par=resid.par,
 #'                                    resid.dist = "empirical", boot.method = "block")
-#' 
+#' }
 #' @export
 
 fmmcSemiParam <- function (B=1000, factor.ret, beta, alpha, resid.par, 

R/plot.ffm.R

@@ -103,26 +103,29 @@
 #' @examples
 #'
 #' # load data from the database
-#' data(Stocks.df)
-#' fit.style.sector <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN",
-#'                            exposure.vars=c("GICS.SECTOR","LOG.MARKETCAP",
-#'                                            "BOOK2MARKET"), date.var="DATE")
+#' data("factorDataSetDjia5Yrs")
+#' 
+#' # fit a fundamental factor model
+#' exposure.vars <- c("P2B", "MKTCAP")
+#' fit.style.sector <- fitFfm(data=factorDataSetDjia5Yrs, asset.var="TICKER", 
+#'                            ret.var="RETURN", date.var="DATE", 
+#'                            exposure.vars=exposure.vars)
 #'
 #' # for group plots (default), user can select plot option from menu prompt
 #' # menu is repeated to get multiple types of plots based on the same fit
 #' # plot(fit.style.sector)
 #'
 #' # choose specific plot option(s) using which
 #' # plot all factor exposures from the last time period for 1st 10 assets
-#' plot(fit.style.sector, which=2, f.sub=1:12, a.sub=1:10)
+#' plot(fit.style.sector, which=2, f.sub=1:2, a.sub=1:10)
 #'
 #' # plot factor model residuals scatterplot matrix, with histograms, density
 #' # overlays, correlations and significance stars
 #' plot(fit.style.sector, which=6)
 #'
 #' # for individual plots: set plot.single=TRUE and specify asset.name
 #' # histogram of residuals from an individual asset's factor model fit
-#' plot(fit.style.sector, plot.single=TRUE, asset.name="MSFT", which=12)
+#' plot(fit.style.sector, plot.single=TRUE, asset.name="AA", which=12)
 #'
 #' @method plot ffm
 #' @export

R/plot.tsfmUpDn.r

@@ -36,6 +36,7 @@
 #' 
 #' # load data from the database
 #'  data(managers, package = 'PerformanceAnalytics')
+#'  colnames(managers) = make.names(colnames(managers))
 #' # example: Up and down market factor model with  fit
 #'  fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),
 #'  mkt.name="SP500.TR", data=managers, fit.method="LS")

R/portEsDecomp.R

@@ -95,7 +95,7 @@
 #'                                                      
 #' # fit a fundamental factor model
 #' fit.cross <- fitFfm(data = dat, 
-#'               exposure.vars = c("SECTOR","ROE","BP","MOM121","SIZE","VOL121",
+#'               exposure.vars = c("SECTOR","ROE","BP","SIZE",
 #'               "EP"),date.var = "DATE", ret.var = "RETURN", asset.var = "TICKER", 
 #'               fit.method="WLS", z.score = "crossSection")
 #'               

R/print.ffm.R

@@ -18,10 +18,12 @@
 #' @seealso \code{\link{fitFfm}}, \code{\link{summary.ffm}}
 #' 
 #' @examples
-#' 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)
+#' data("factorDataSetDjia5Yrs")
+#' # fit a fundamental factor model
+#' exposure.vars <- c("P2B", "MKTCAP")
+#' fit.style.sector <- fitFfm(data=factorDataSetDjia5Yrs, asset.var="TICKER", 
+#'                            ret.var="RETURN", date.var="DATE", 
+#'                            exposure.vars=exposure.vars)
 #' print(fit)
 #' 
 #' @method print ffm