This function can be used to bootstrap `incidence`

objects. Bootstrapping is
done by sampling with replacement the original input dates. See `details`

for
more information on how this is implemented.

bootstrap(x, randomise_groups = FALSE)

## Arguments

x |
An `incidence` object. |

randomise_groups |
A `logical` indicating whether groups should be
randomised as well in the resampling procedure; respective group sizes will
be preserved, but this can be used to remove any group-specific temporal
dynamics. If `FALSE` (default), data are resampled within groups. |

## Value

An `incidence`

object.

## Details

As original data are not stored in `incidence`

objects, the
bootstrapping is achieved by multinomial sampling of date bins weighted by
their relative incidence.

## See also

find_peak to use estimate peak date using bootstrap

## Examples

#> Loading required package: outbreaks

#> Loading required package: ggplot2

#> > i <- incidence(fluH7N9_china_2013$date_of_onset)

#> 10 missing observations were removed.

#> > i
#> <incidence object>
#> [126 cases from days 2013-02-19 to 2013-07-27]
#>
#> $counts: matrix with 159 rows and 1 columns
#> $n: 126 cases in total
#> $dates: 159 dates marking the left-side of bins
#> $interval: 1 day
#> $timespan: 159 days
#> $cumulative: FALSE
#>
#> > plot(i)

#> > x <- bootstrap(i)
#> > x
#> <incidence object>
#> [126 cases from days 2013-02-19 to 2013-05-21]
#>
#> $counts: matrix with 92 rows and 1 columns
#> $n: 126 cases in total
#> $dates: 92 dates marking the left-side of bins
#> $interval: 1 day
#> $timespan: 92 days
#> $cumulative: FALSE
#>
#> > plot(x)