plot.TimeSeriesFactorModel: plot TimeSeriesFactorModel object.
In R-Finance/FactorAnalytics: factor analysis

Description Usage Arguments Author(s) Examples

Generic function of plot method for fitTimeSeriesFactorModel. Either plot all assets or choose a single asset to plot.

  ## S3 method for class 'TimeSeriesFactorModel'
 plot(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")

`x`	fit object created by `fitTimeSeriesFactorModel`.
`colorset`	Defualt colorset the same as `barplot`.
`legend.loc`	Plot legend or not. Defualt is `NULL`.
`which.plot`	Integer indicates which plot to create: "none" will create a menu to choose. Defualt is none. 1 = "Fitted factor returns", 2 = "R square", 3 = "Variance of Residuals", 4 = "FM Correlation", 5 = "Factor Contributions to SD", 6 = "Factor Contributions to ES", 7 = "Factor Contributions to VaR"
`max.show`	Maximum assets to plot. Default is 6.
`plot.single`	Plot a single asset of lm class. Defualt is `FALSE`.
`asset.name`	Name of the asset to be plotted.
`which.plot.single`	Integer indicates which plot to create: "none" will create a menu to choose. Defualt is none. 1 = time series plot of actual and fitted values, 2 = time series plot of residuals with standard error bands, 3 = time series plot of squared residuals, 4 = time series plot of absolute residuals, 5 = SACF and PACF of residuals, 6 = SACF and PACF of squared residuals, 7 = SACF and PACF of absolute residuals, 8 = histogram of residuals with normal curve overlayed, 9 = normal qq-plot of residuals, 10= CUSUM plot of recursive residuals, 11= CUSUM plot of OLS residuals, 12= CUSUM plot of recursive estimates relative to full sample estimates, 13= rolling estimates over 24 month window.
`VaR.method`	Character, method for computing VaR. Valid choices are either "modified","gaussian","historical", "kernel". computation is done with the `VaR` in the PerformanceAnalytics package. Default is "historical".

Eric Zivot and Yi-An Chen.

## Not run: 
# 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")

## End(Not run)