format.timestamps | R Documentation |

Tools for checking if a series of timestamps is 'regular' meaning that
it has no duplicates, and no gaps. Checking for regularity can be
tricky. For example, if you have monthly observations with
`Date`

or `POSIXt`

timestamps then gaps
between timestamps can be 28, 29, 30, or 31 days, but the series is
still "regular".

NoDuplicates(timestamps) NoGaps(timestamps) IsRegular(timestamps) HasDuplicateTimestamps(bsts.object)

`timestamps` |
A set of (possibly irregular or non-unique)
timestamps. This could be a set of integers (like 1, 2, , 3...), a
set of numeric like (1945, 1945.083, 1945.167, ...) indicating years
and fractions of years, a |

`bsts.object` |
A bsts model object. |

All four functions return scalar logical values. `NoDuplicates`

returns `TRUE`

if all elements of `timestamps`

are unique.

`NoGaps`

examines the smallest nonzero gap between time points.
As long as no gaps between time points are more than twice as wide as
the smallest gap, it returns `TRUE`

, indicating that there are no
missing timestamps. Otherwise it returns `FALSE`

.

`IsRegular`

returns `TRUE`

if `NoDuplicates`

and
`NoGaps`

both return `TRUE`

.

`HasDuplicateTimestamps`

returns `FALSE`

if the data used to
fit bsts.model either has NULL timestamps, or if the timestamps
contain no duplicate values.

Steven L. Scott steve.the.bayesian@gmail.com

first <- as.POSIXct("2015-04-19 08:00:04") monthly <- seq(from = first, length.out = 24, by = "month") IsRegular(monthly) ## TRUE skip.one <- monthly[-8] IsRegular(skip.one) ## FALSE has.duplicates <- monthly has.duplicates[1] <- has.duplicates[2] IsRegular(has.duplicates) ## FALSE

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