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#43 fix examples related managers data containing spaces in names, …
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…which get filled in by "." during merge.xts.
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@JustinMShea
JustinMShea committed Aug 22, 2021
1 parent 80246e3 commit 40bef56
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21 changes: 7 additions & 14 deletions R/fmEsDecomp.R
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#' \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

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15 changes: 8 additions & 7 deletions R/fmSdDecomp.R
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#'
#' @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)
#' 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
#'
#' @export
#' @export

fmSdDecomp <- function(object, ...){
# check input object validity
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26 changes: 11 additions & 15 deletions R/fmVaRDecomp.R
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#'
#' @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$cVaR
#'
#' @export
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10 changes: 9 additions & 1 deletion R/fmmcSemiParam.R
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#'
#' @examples
#' # fit a time series factor model for all assets
#'
#' # load data
#' data(managers, package = 'PerformanceAnalytics')
#' colnames(managers)
#' # Make syntactically valid column names
#' colnames(managers) <- make.names( colnames(managers))
#' colnames(managers)
#'
#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
#' factor.names=colnames(managers[,(7:9)]), data=managers)
#' factor.names=colnames(managers[,(7:9)]),
#' data=managers)
#'
#' # bootstrap returns using the fitted factor model, Normal dist. for residuals
#' resid.par <- as.matrix(fit$resid.sd,1,6)
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17 changes: 13 additions & 4 deletions R/portEsDecomp.R
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#'
#' @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$cES
#'
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#' data("stocks145scores6")
#' dat = stocks145scores6
#' dat$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$DATE <= as.yearmon("2012-12-31"),]
#'
#' # Load long-only GMV weights for the return data
#' data("wtsStocks145GmvLo")
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11 changes: 10 additions & 1 deletion R/portSdDecomp.R
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#'
#' @examples
#' # Time Series Factor Model
#'
#' # load data
#' data(managers, package = 'PerformanceAnalytics')
#' colnames(managers)
#' # Make syntactically valid column names
#' colnames(managers) <- make.names( colnames(managers))
#' colnames(managers)
#'
#' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
#' factor.names=colnames(managers[,(7:9)]),
#' rf.name=colnames(managers[,10]), data=managers)
#' rf.name=colnames(managers[,10]),
#' data=managers)
#'
#' decomp <- portSdDecomp(fit.macro)
#' # get the factor contributions of risk
#' decomp$cSd
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12 changes: 11 additions & 1 deletion R/portVaRDecomp.R
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#'
#' @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
#'
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11 changes: 10 additions & 1 deletion R/portVolDecomp.R
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#'
#' @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
#'
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16 changes: 11 additions & 5 deletions R/predict.tsfmUpDn.r
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#' @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)
#'
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12 changes: 10 additions & 2 deletions R/print.tsfmUpDn.r
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#' @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)
#'
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6 changes: 6 additions & 0 deletions R/summary.tsfm.r
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#' @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)
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12 changes: 9 additions & 3 deletions R/summary.tsfmUpDn.r
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#' @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)
#'
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21 changes: 7 additions & 14 deletions man/fmEsDecomp.Rd
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13 changes: 7 additions & 6 deletions man/fmSdDecomp.Rd
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1 comment on commit 40bef56

@martinrd3D
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Justin, Very nice uniform fix to this problem caused by inappropriate names of some of the columns of the managers xts data object.

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