chart.RollingQuantileRegression {PerformanceAnalytics} | R Documentation

## A wrapper to create charts of relative regression performance through time

### Description

A wrapper to create a chart of relative regression performance through time

### Usage

    chart.RollingQuantileRegression(Ra, Rb, width = 12, Rf = 0,
      attribute = c("Beta", "Alpha", "R-Squared"), main = NULL,
      na.pad = TRUE, ...)

    chart.RollingRegression(Ra, Rb, width = 12, Rf = 0,
      attribute = c("Beta", "Alpha", "R-Squared"), main = NULL,
      na.pad = TRUE, ...)

    charts.RollingRegression(Ra, Rb, width = 12, Rf = 0, main = NULL,
      legend.loc = NULL, event.labels = NULL, ...)

### Arguments

`Ra` | an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
`Rb` | return vector of the benchmark asset
`width` | number of periods to apply rolling function window over
`Rf` | risk free rate, in same period as your returns
`attribute` | one of "Beta","Alpha","R-Squared" for which attribute to show
`main` | set the chart title, same as in `plot`
`na.pad` | TRUE/FALSE If TRUE it adds any times that would not otherwise have been in the result with a value of NA. If FALSE those times are dropped.
`...` | any other passthru parameters to `chart.TimeSeries`
`legend.loc` | used to set the position of the legend
`event.labels` | if not null and event.lines is not null, this will apply a list of text labels to the vertical lines drawn

### Details

A group of charts in `charts.RollingRegression` displays alpha, beta, and R-squared estimates in three aligned charts in a single device.

The attribute parameter is probably the most confusing. In mathematical terms, the different choices yield the following:

Alpha - shows the y-intercept
Beta - shows the slope of the regression line
R-Squared - shows the degree of fit of the regression to the data

`chart.RollingQuantileRegression` uses `rq` rather than `lm` for the regression, and may be more robust to outliers in the data.

### Note

Most inputs are the same as "`plot`" and are principally included so that some sensible defaults could be set.

### Author(s)

Peter Carl

### See Also

`lm`
`rq`

### Examples

    # First we load the data
    data(managers)
    chart.RollingRegression(managers[, 1, drop=FALSE],
    		managers[, 8, drop=FALSE], Rf = .04/12)
    charts.RollingRegression(managers[, 1:6],
    		managers[, 8, drop=FALSE], Rf = .04/12,
    		colorset = rich6equal, legend.loc="topleft")
    dev.new()
    chart.RollingQuantileRegression(managers[, 1, drop=FALSE],
    		managers[, 8, drop=FALSE], Rf = .04/12)
    # not implemented yet
    #charts.RollingQuantileRegression(managers[, 1:6],
    #		managers[, 8, drop=FALSE], Rf = .04/12,
    #		colorset = rich6equal, legend.loc="topleft")

[Package PerformanceAnalytics version 1.5.3 Index]