Creates an array of time series graphs–one for each accrual lag–and displays a running median and MAD-based bounds on each graph.

1 | ```
laggedTSarray(x, daysOfHistory, lags, ...)
``` |

`x` |
Object of the |

`daysOfHistory` |
An integer greater or equal to 1. If |

`lags` |
A vector of nonnegative integers specifying which lagged time series will be plotted. If NULL, all lags are used. |

`...` |
Parameters to pass to |

An array of graphs is produced, with the *i*th graph showing for each encounter date the cumulative counts received *i-1* days after the encounter date, with the final graph showing the final counts. A running median and error bars are disiplayed by default. Error bars are calculated as *\pm (2)(MAD)* (median absolute deviations) from the running median, calculated using the same running window.

None.

Julie Eaton and Ian Painter

`data.accrued`

,
`stackedUploadData`

,
`uploadPattern`

,
`plot.accrued`

,
`plot.summary.accrued`

,
`lagHistogram`

,
`asOf`

1 2 3 | ```
data(accruedDataExample) # simulated accrued data
dat <- data.accrued(accruedDataExample) # Convert to data.accrued object
laggedTSarray(dat)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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