Expectation model module
Usage
enw_expectation(
r = ~0 + (1 | day:.group),
generation_time = 1,
observation = ~1,
latent_reporting_delay = 1,
data,
...
)Arguments
- r
A formula (as implemented in
enw_formula()) describing the generative process used for expected incidence. This can use features defined by reference date as defined inmetareferenceas produced byenw_preprocess_data(). By default this is set to use a daily random effect by group. This parameterisation is highly flexible and so may not be the most appropriate choice when data is sparsely reported or reporting delays are substantially. These settings an alternative could be a group specific weekly random walk (specified asrw(week, by = .group).- generation_time
A numeric vector that sums to 1 and defaults to 1. Describes the weighting to apply to previous generations (i.e as part of a renewal equation). When set to 1 (the default) this corresponds to modelling the daily growth rate.
- observation
A formula (as implemented in
enw_formula()) describing the modifiers used to adjust expected observations. This can use features defined by reference date as defined inmetareferenceas produced byenw_preprocess_data(). By default no modifiers are used but a common choice might be to adjust for the day of the week. Note as the baseline is no modification an intercept is always used and it is set to 0.- latent_reporting_delay
A numeric vector that defaults to 1. Describes the weighting to apply to past and current latent expected observations (from most recent to least). This can be used both to convolve based on some assumed reporting delay and to rescale observations (by multiplying a probability mass function by some fraction) to account ascertainment etc. A list of PMFs can be provided to allow for time-varying PMFs. This should be the same length as the modelled time period plus the length of the generation time if supplied.
- data
Output from
enw_preprocess_data().- ...
Additional parameters passed to
enw_add_metaobs_features(). The same arguments as passed toenw_preprocess_data()should be used here.
Value
A list containing the supplied formulas, data passed into a list
describing the models, a data.frame describing the priors used, and a
function that takes the output data and priors and returns a function that
can be used to sample from a tightened version of the prior distribution.
See also
Model modules
enw_fit_opts(),
enw_missing(),
enw_obs(),
enw_reference(),
enw_report()
Examples
enw_expectation(data = enw_example("preprocessed"))
#> A random effect using .group is not possible as this variable has fewer than 2
#> unique values.
#> $formula
#> $formula$r
#> [1] "~0 + (1 | day:.group)"
#>
#> $formula$observation
#> [1] "~1"
#>
#>
#> $data_raw
#> $data_raw$r
#> Key: <.group, date>
#> date .group location age_group delay day_of_week day week month
#> <IDat> <num> <fctr> <fctr> <num> <fctr> <num> <num> <num>
#> 1: 2021-07-14 1 DE 00+ 0 Wednesday 1 0 0
#> 2: 2021-07-15 1 DE 00+ 0 Thursday 2 0 0
#> 3: 2021-07-16 1 DE 00+ 0 Friday 3 0 0
#> 4: 2021-07-17 1 DE 00+ 0 Saturday 4 0 0
#> 5: 2021-07-18 1 DE 00+ 0 Sunday 5 0 0
#> 6: 2021-07-19 1 DE 00+ 0 Monday 6 0 0
#> 7: 2021-07-20 1 DE 00+ 0 Tuesday 7 1 0
#> 8: 2021-07-21 1 DE 00+ 0 Wednesday 8 1 0
#> 9: 2021-07-22 1 DE 00+ 0 Thursday 9 1 0
#> 10: 2021-07-23 1 DE 00+ 0 Friday 10 1 0
#> 11: 2021-07-24 1 DE 00+ 0 Saturday 11 1 0
#> 12: 2021-07-25 1 DE 00+ 0 Sunday 12 1 0
#> 13: 2021-07-26 1 DE 00+ 0 Monday 13 1 0
#> 14: 2021-07-27 1 DE 00+ 0 Tuesday 14 2 0
#> 15: 2021-07-28 1 DE 00+ 0 Wednesday 15 2 0
#> 16: 2021-07-29 1 DE 00+ 0 Thursday 16 2 0
#> 17: 2021-07-30 1 DE 00+ 0 Friday 17 2 0
#> 18: 2021-07-31 1 DE 00+ 0 Saturday 18 2 0
#> 19: 2021-08-01 1 DE 00+ 0 Sunday 19 2 1
#> 20: 2021-08-02 1 DE 00+ 0 Monday 20 2 1
#> 21: 2021-08-03 1 DE 00+ 0 Tuesday 21 3 1
#> 22: 2021-08-04 1 DE 00+ 0 Wednesday 22 3 1
#> 23: 2021-08-05 1 DE 00+ 0 Thursday 23 3 1
#> 24: 2021-08-06 1 DE 00+ 0 Friday 24 3 1
#> 25: 2021-08-07 1 DE 00+ 0 Saturday 25 3 1
#> 26: 2021-08-08 1 DE 00+ 0 Sunday 26 3 1
#> 27: 2021-08-09 1 DE 00+ 0 Monday 27 3 1
#> 28: 2021-08-10 1 DE 00+ 0 Tuesday 28 4 1
#> 29: 2021-08-11 1 DE 00+ 0 Wednesday 29 4 1
#> 30: 2021-08-12 1 DE 00+ 0 Thursday 30 4 1
#> 31: 2021-08-13 1 DE 00+ 0 Friday 31 4 1
#> 32: 2021-08-14 1 DE 00+ 0 Saturday 32 4 1
#> 33: 2021-08-15 1 DE 00+ 0 Sunday 33 4 1
#> 34: 2021-08-16 1 DE 00+ 0 Monday 34 4 1
#> 35: 2021-08-17 1 DE 00+ 0 Tuesday 35 5 1
#> 36: 2021-08-18 1 DE 00+ 0 Wednesday 36 5 1
#> 37: 2021-08-19 1 DE 00+ 0 Thursday 37 5 1
#> 38: 2021-08-20 1 DE 00+ 0 Friday 38 5 1
#> 39: 2021-08-21 1 DE 00+ 0 Saturday 39 5 1
#> 40: 2021-08-22 1 DE 00+ 0 Sunday 40 5 1
#> date .group location age_group delay day_of_week day week month
#>
#> $data_raw$observation
#> Key: <.group, date>
#> date .group location age_group delay day_of_week day week month
#> <IDat> <num> <fctr> <fctr> <num> <fctr> <num> <num> <num>
#> 1: 2021-07-13 1 DE 00+ 0 Tuesday 0 0 0
#> 2: 2021-07-14 1 DE 00+ 0 Wednesday 1 0 0
#> 3: 2021-07-15 1 DE 00+ 0 Thursday 2 0 0
#> 4: 2021-07-16 1 DE 00+ 0 Friday 3 0 0
#> 5: 2021-07-17 1 DE 00+ 0 Saturday 4 0 0
#> 6: 2021-07-18 1 DE 00+ 0 Sunday 5 0 0
#> 7: 2021-07-19 1 DE 00+ 0 Monday 6 0 0
#> 8: 2021-07-20 1 DE 00+ 0 Tuesday 7 1 0
#> 9: 2021-07-21 1 DE 00+ 0 Wednesday 8 1 0
#> 10: 2021-07-22 1 DE 00+ 0 Thursday 9 1 0
#> 11: 2021-07-23 1 DE 00+ 0 Friday 10 1 0
#> 12: 2021-07-24 1 DE 00+ 0 Saturday 11 1 0
#> 13: 2021-07-25 1 DE 00+ 0 Sunday 12 1 0
#> 14: 2021-07-26 1 DE 00+ 0 Monday 13 1 0
#> 15: 2021-07-27 1 DE 00+ 0 Tuesday 14 2 0
#> 16: 2021-07-28 1 DE 00+ 0 Wednesday 15 2 0
#> 17: 2021-07-29 1 DE 00+ 0 Thursday 16 2 0
#> 18: 2021-07-30 1 DE 00+ 0 Friday 17 2 0
#> 19: 2021-07-31 1 DE 00+ 0 Saturday 18 2 0
#> 20: 2021-08-01 1 DE 00+ 0 Sunday 19 2 1
#> 21: 2021-08-02 1 DE 00+ 0 Monday 20 2 1
#> 22: 2021-08-03 1 DE 00+ 0 Tuesday 21 3 1
#> 23: 2021-08-04 1 DE 00+ 0 Wednesday 22 3 1
#> 24: 2021-08-05 1 DE 00+ 0 Thursday 23 3 1
#> 25: 2021-08-06 1 DE 00+ 0 Friday 24 3 1
#> 26: 2021-08-07 1 DE 00+ 0 Saturday 25 3 1
#> 27: 2021-08-08 1 DE 00+ 0 Sunday 26 3 1
#> 28: 2021-08-09 1 DE 00+ 0 Monday 27 3 1
#> 29: 2021-08-10 1 DE 00+ 0 Tuesday 28 4 1
#> 30: 2021-08-11 1 DE 00+ 0 Wednesday 29 4 1
#> 31: 2021-08-12 1 DE 00+ 0 Thursday 30 4 1
#> 32: 2021-08-13 1 DE 00+ 0 Friday 31 4 1
#> 33: 2021-08-14 1 DE 00+ 0 Saturday 32 4 1
#> 34: 2021-08-15 1 DE 00+ 0 Sunday 33 4 1
#> 35: 2021-08-16 1 DE 00+ 0 Monday 34 4 1
#> 36: 2021-08-17 1 DE 00+ 0 Tuesday 35 5 1
#> 37: 2021-08-18 1 DE 00+ 0 Wednesday 36 5 1
#> 38: 2021-08-19 1 DE 00+ 0 Thursday 37 5 1
#> 39: 2021-08-20 1 DE 00+ 0 Friday 38 5 1
#> 40: 2021-08-21 1 DE 00+ 0 Saturday 39 5 1
#> 41: 2021-08-22 1 DE 00+ 0 Sunday 40 5 1
#> date .group location age_group delay day_of_week day week month
#>
#>
#> $data
#> $data$expr_r_seed
#> [1] 1
#>
#> $data$expr_gt_n
#> [1] 1
#>
#> $data$expr_lrgt
#> [1] 0
#>
#> $data$expr_t
#> [1] 40
#>
#> $data$expr_obs
#> [1] 0
#>
#> $data$expr_g
#> [1] 0
#>
#> $data$expr_ft
#> [1] 41
#>
#> $data$expr_fdesign
#> day1 day2 day3 day4 day5 day6 day7 day8 day9 day10 day11 day12 day13 day14
#> 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 1 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 1 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 1 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 1 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 1 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 1 0 0 0 0
#> 11 0 0 0 0 0 0 0 0 0 0 1 0 0 0
#> 12 0 0 0 0 0 0 0 0 0 0 0 1 0 0
#> 13 0 0 0 0 0 0 0 0 0 0 0 0 1 0
#> 14 0 0 0 0 0 0 0 0 0 0 0 0 0 1
#> 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> day15 day16 day17 day18 day19 day20 day21 day22 day23 day24 day25 day26
#> 1 0 0 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0 0 0
#> 11 0 0 0 0 0 0 0 0 0 0 0 0
#> 12 0 0 0 0 0 0 0 0 0 0 0 0
#> 13 0 0 0 0 0 0 0 0 0 0 0 0
#> 14 0 0 0 0 0 0 0 0 0 0 0 0
#> 15 1 0 0 0 0 0 0 0 0 0 0 0
#> 16 0 1 0 0 0 0 0 0 0 0 0 0
#> 17 0 0 1 0 0 0 0 0 0 0 0 0
#> 18 0 0 0 1 0 0 0 0 0 0 0 0
#> 19 0 0 0 0 1 0 0 0 0 0 0 0
#> 20 0 0 0 0 0 1 0 0 0 0 0 0
#> 21 0 0 0 0 0 0 1 0 0 0 0 0
#> 22 0 0 0 0 0 0 0 1 0 0 0 0
#> 23 0 0 0 0 0 0 0 0 1 0 0 0
#> 24 0 0 0 0 0 0 0 0 0 1 0 0
#> 25 0 0 0 0 0 0 0 0 0 0 1 0
#> 26 0 0 0 0 0 0 0 0 0 0 0 1
#> 27 0 0 0 0 0 0 0 0 0 0 0 0
#> 28 0 0 0 0 0 0 0 0 0 0 0 0
#> 29 0 0 0 0 0 0 0 0 0 0 0 0
#> 30 0 0 0 0 0 0 0 0 0 0 0 0
#> 31 0 0 0 0 0 0 0 0 0 0 0 0
#> 32 0 0 0 0 0 0 0 0 0 0 0 0
#> 33 0 0 0 0 0 0 0 0 0 0 0 0
#> 34 0 0 0 0 0 0 0 0 0 0 0 0
#> 35 0 0 0 0 0 0 0 0 0 0 0 0
#> 36 0 0 0 0 0 0 0 0 0 0 0 0
#> 37 0 0 0 0 0 0 0 0 0 0 0 0
#> 38 0 0 0 0 0 0 0 0 0 0 0 0
#> 39 0 0 0 0 0 0 0 0 0 0 0 0
#> 40 0 0 0 0 0 0 0 0 0 0 0 0
#> day27 day28 day29 day30 day31 day32 day33 day34 day35 day36 day37 day38
#> 1 0 0 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0 0 0
#> 11 0 0 0 0 0 0 0 0 0 0 0 0
#> 12 0 0 0 0 0 0 0 0 0 0 0 0
#> 13 0 0 0 0 0 0 0 0 0 0 0 0
#> 14 0 0 0 0 0 0 0 0 0 0 0 0
#> 15 0 0 0 0 0 0 0 0 0 0 0 0
#> 16 0 0 0 0 0 0 0 0 0 0 0 0
#> 17 0 0 0 0 0 0 0 0 0 0 0 0
#> 18 0 0 0 0 0 0 0 0 0 0 0 0
#> 19 0 0 0 0 0 0 0 0 0 0 0 0
#> 20 0 0 0 0 0 0 0 0 0 0 0 0
#> 21 0 0 0 0 0 0 0 0 0 0 0 0
#> 22 0 0 0 0 0 0 0 0 0 0 0 0
#> 23 0 0 0 0 0 0 0 0 0 0 0 0
#> 24 0 0 0 0 0 0 0 0 0 0 0 0
#> 25 0 0 0 0 0 0 0 0 0 0 0 0
#> 26 0 0 0 0 0 0 0 0 0 0 0 0
#> 27 1 0 0 0 0 0 0 0 0 0 0 0
#> 28 0 1 0 0 0 0 0 0 0 0 0 0
#> 29 0 0 1 0 0 0 0 0 0 0 0 0
#> 30 0 0 0 1 0 0 0 0 0 0 0 0
#> 31 0 0 0 0 1 0 0 0 0 0 0 0
#> 32 0 0 0 0 0 1 0 0 0 0 0 0
#> 33 0 0 0 0 0 0 1 0 0 0 0 0
#> 34 0 0 0 0 0 0 0 1 0 0 0 0
#> 35 0 0 0 0 0 0 0 0 1 0 0 0
#> 36 0 0 0 0 0 0 0 0 0 1 0 0
#> 37 0 0 0 0 0 0 0 0 0 0 1 0
#> 38 0 0 0 0 0 0 0 0 0 0 0 1
#> 39 0 0 0 0 0 0 0 0 0 0 0 0
#> 40 0 0 0 0 0 0 0 0 0 0 0 0
#> day39 day40
#> 1 0 0
#> 2 0 0
#> 3 0 0
#> 4 0 0
#> 5 0 0
#> 6 0 0
#> 7 0 0
#> 8 0 0
#> 9 0 0
#> 10 0 0
#> 11 0 0
#> 12 0 0
#> 13 0 0
#> 14 0 0
#> 15 0 0
#> 16 0 0
#> 17 0 0
#> 18 0 0
#> 19 0 0
#> 20 0 0
#> 21 0 0
#> 22 0 0
#> 23 0 0
#> 24 0 0
#> 25 0 0
#> 26 0 0
#> 27 0 0
#> 28 0 0
#> 29 0 0
#> 30 0 0
#> 31 0 0
#> 32 0 0
#> 33 0 0
#> 34 0 0
#> 35 0 0
#> 36 0 0
#> 37 0 0
#> 38 0 0
#> 39 1 0
#> 40 0 1
#> attr(,"assign")
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1
#> attr(,"contrasts")
#> attr(,"contrasts")$day
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 11 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 12 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 13 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 14 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
#> 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
#> 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
#> 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
#> 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
#> 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
#> 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
#> 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
#> 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
#> 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
#> 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
#> 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
#> 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
#> 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 29 30 31 32 33 34 35 36 37 38 39 40
#> 1 0 0 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0 0 0
#> 11 0 0 0 0 0 0 0 0 0 0 0 0
#> 12 0 0 0 0 0 0 0 0 0 0 0 0
#> 13 0 0 0 0 0 0 0 0 0 0 0 0
#> 14 0 0 0 0 0 0 0 0 0 0 0 0
#> 15 0 0 0 0 0 0 0 0 0 0 0 0
#> 16 0 0 0 0 0 0 0 0 0 0 0 0
#> 17 0 0 0 0 0 0 0 0 0 0 0 0
#> 18 0 0 0 0 0 0 0 0 0 0 0 0
#> 19 0 0 0 0 0 0 0 0 0 0 0 0
#> 20 0 0 0 0 0 0 0 0 0 0 0 0
#> 21 0 0 0 0 0 0 0 0 0 0 0 0
#> 22 0 0 0 0 0 0 0 0 0 0 0 0
#> 23 0 0 0 0 0 0 0 0 0 0 0 0
#> 24 0 0 0 0 0 0 0 0 0 0 0 0
#> 25 0 0 0 0 0 0 0 0 0 0 0 0
#> 26 0 0 0 0 0 0 0 0 0 0 0 0
#> 27 0 0 0 0 0 0 0 0 0 0 0 0
#> 28 0 0 0 0 0 0 0 0 0 0 0 0
#> 29 1 0 0 0 0 0 0 0 0 0 0 0
#> 30 0 1 0 0 0 0 0 0 0 0 0 0
#> 31 0 0 1 0 0 0 0 0 0 0 0 0
#> 32 0 0 0 1 0 0 0 0 0 0 0 0
#> 33 0 0 0 0 1 0 0 0 0 0 0 0
#> 34 0 0 0 0 0 1 0 0 0 0 0 0
#> 35 0 0 0 0 0 0 1 0 0 0 0 0
#> 36 0 0 0 0 0 0 0 1 0 0 0 0
#> 37 0 0 0 0 0 0 0 0 1 0 0 0
#> 38 0 0 0 0 0 0 0 0 0 1 0 0
#> 39 0 0 0 0 0 0 0 0 0 0 1 0
#> 40 0 0 0 0 0 0 0 0 0 0 0 1
#>
#>
#> $data$expr_fintercept
#> [1] 0
#>
#> $data$expr_fnrow
#> [1] 40
#>
#> $data$expr_findex
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
#>
#> $data$expr_fnindex
#> [1] 40
#>
#> $data$expr_fncol
#> [1] 40
#>
#> $data$expr_rdesign
#> fixed day
#> 1 0 1
#> 2 0 1
#> 3 0 1
#> 4 0 1
#> 5 0 1
#> 6 0 1
#> 7 0 1
#> 8 0 1
#> 9 0 1
#> 10 0 1
#> 11 0 1
#> 12 0 1
#> 13 0 1
#> 14 0 1
#> 15 0 1
#> 16 0 1
#> 17 0 1
#> 18 0 1
#> 19 0 1
#> 20 0 1
#> 21 0 1
#> 22 0 1
#> 23 0 1
#> 24 0 1
#> 25 0 1
#> 26 0 1
#> 27 0 1
#> 28 0 1
#> 29 0 1
#> 30 0 1
#> 31 0 1
#> 32 0 1
#> 33 0 1
#> 34 0 1
#> 35 0 1
#> 36 0 1
#> 37 0 1
#> 38 0 1
#> 39 0 1
#> 40 0 1
#> attr(,"assign")
#> [1] 1 2
#>
#> $data$expr_rncol
#> [1] 1
#>
#> $data$expl_lrd_n
#> [1] 1
#>
#> $data$expl_lrd_nw
#> [1] 41
#>
#> $data$expl_lrd_w
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1
#>
#> $data$expl_lrd_nv
#> [1] 41
#>
#> $data$expl_lrd_v
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
#>
#> $data$expl_lrd_nu
#> [1] 42
#>
#> $data$expl_lrd_u
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
#>
#> $data$expl_obs
#> [1] 0
#>
#> $data$expl_fdesign
#> (Intercept)
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 1
#> 7 1
#> 8 1
#> 9 1
#> 10 1
#> 11 1
#> 12 1
#> 13 1
#> 14 1
#> 15 1
#> 16 1
#> 17 1
#> 18 1
#> 19 1
#> 20 1
#> 21 1
#> 22 1
#> 23 1
#> 24 1
#> 25 1
#> 26 1
#> 27 1
#> 28 1
#> 29 1
#> 30 1
#> 31 1
#> 32 1
#> 33 1
#> 34 1
#> 35 1
#> 36 1
#> 37 1
#> 38 1
#> 39 1
#> 40 1
#> 41 1
#> attr(,"assign")
#> [1] 0
#>
#> $data$expl_fintercept
#> [1] 1
#>
#> $data$expl_fnrow
#> [1] 41
#>
#> $data$expl_findex
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
#>
#> $data$expl_fnindex
#> [1] 41
#>
#> $data$expl_fncol
#> [1] 0
#>
#> $data$expl_rdesign
#> (Intercept)
#> attr(,"assign")
#> [1] 0
#>
#> $data$expl_rncol
#> [1] 0
#>
#>
#> $priors
#> variable dimension
#> <char> <num>
#> 1: expr_r_int 1
#> 2: expr_beta_sd 1
#> 3: expr_lelatent_int 1
#> 4: expl_beta_sd 1
#> description
#> <char>
#> 1: Intercept of the log growth rate
#> 2: Standard deviation of scaled pooled log growth rate effects
#> 3: Intercept for initial log observations (ordered by group and then\n time)
#> 4: Standard deviation of scaled pooled log growth rate effects
#> distribution mean sd
#> <char> <num> <num>
#> 1: Normal 0.0 0.2
#> 2: Zero truncated normal 0.0 1.0
#> 3: Normal 4.1 1.0
#> 4: Zero truncated normal 0.0 1.0
#>
#> $inits
#> function (data, priors)
#> {
#> priors <- enw_priors_as_data_list(priors)
#> fn <- function() {
#> init <- list(expr_beta = numeric(0), expr_beta_sd = numeric(0),
#> expr_lelatent_int = matrix(purrr::map2_dbl(as.vector(priors$expr_lelatent_int_p[1]),
#> as.vector(priors$expr_lelatent_int_p[2]), function(x,
#> y) {
#> rnorm(1, x, y * 0.1)
#> }), nrow = data$expr_gt_n, ncol = data$g), expr_r_int = numeric(0),
#> expl_beta = numeric(0), expl_beta_sd = numeric(0))
#> if (data$expr_fncol > 0) {
#> init$expr_beta <- array(rnorm(data$expr_fncol, 0,
#> 0.01))
#> }
#> if (data$expr_rncol > 0) {
#> init$expr_beta_sd <- array(abs(rnorm(data$expr_rncol,
#> priors$expr_beta_sd_p[1], priors$expr_beta_sd_p[2]/10)))
#> }
#> if (data$expr_fintercept > 0) {
#> init$expr_r_int <- array(rnorm(1, priors$expr_r_int[1],
#> priors$expr_r_int[2] * 0.1))
#> }
#> if (data$expl_fncol > 0) {
#> init$expl_beta <- array(rnorm(data$expl_fncol, 0,
#> 0.01))
#> }
#> if (data$expl_rncol > 0) {
#> init$expl_beta_sd <- array(abs(rnorm(data$expl_rncol,
#> priors$expl_beta_sd_p[1], priors$expl_beta_sd_p[2]/10)))
#> }
#> return(init)
#> }
#> return(fn)
#> }
#> <bytecode: 0x561795bc11a0>
#> <environment: 0x561795bba7b8>
#>