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 may not be the most appropriate choice when data is sparsely reported or reporting delays are substantial. In these settings an alternative could be a group-specific weekly random walk (specified asrw(week, by = .group)). Setting to~0will produce an error as an expectation model is required. Seeenw_formula()for details on formula syntax.- 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 that as the baseline is no modification, an intercept is always used and is set to 0. Set to~0to disable observation modifiers (internally converted to~1and flagged as inactive). Seeenw_formula()for details on formula syntax.- 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.
#> ℹ The r design matrix is sparse (>90% zeros). Consider using `sparse_design = TRUE` in `enw_fit_opts()` to potentially reduce memory usage and computation time.
#> $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: 2021-07-15 1 DE 00+ 0 Thursday 1 0 0
#> 2: 2021-07-16 1 DE 00+ 0 Friday 2 0 0
#> 3: 2021-07-17 1 DE 00+ 0 Saturday 3 0 0
#> 4: 2021-07-18 1 DE 00+ 0 Sunday 4 0 0
#> 5: 2021-07-19 1 DE 00+ 0 Monday 5 0 0
#> 6: 2021-07-20 1 DE 00+ 0 Tuesday 6 0 0
#> 7: 2021-07-21 1 DE 00+ 0 Wednesday 7 1 0
#> 8: 2021-07-22 1 DE 00+ 0 Thursday 8 1 0
#> 9: 2021-07-23 1 DE 00+ 0 Friday 9 1 0
#> 10: 2021-07-24 1 DE 00+ 0 Saturday 10 1 0
#> 11: 2021-07-25 1 DE 00+ 0 Sunday 11 1 0
#> 12: 2021-07-26 1 DE 00+ 0 Monday 12 1 0
#> 13: 2021-07-27 1 DE 00+ 0 Tuesday 13 1 0
#> 14: 2021-07-28 1 DE 00+ 0 Wednesday 14 2 0
#> 15: 2021-07-29 1 DE 00+ 0 Thursday 15 2 0
#> 16: 2021-07-30 1 DE 00+ 0 Friday 16 2 0
#> 17: 2021-07-31 1 DE 00+ 0 Saturday 17 2 0
#> 18: 2021-08-01 1 DE 00+ 0 Sunday 18 2 1
#> 19: 2021-08-02 1 DE 00+ 0 Monday 19 2 1
#> 20: 2021-08-03 1 DE 00+ 0 Tuesday 20 2 1
#> 21: 2021-08-04 1 DE 00+ 0 Wednesday 21 3 1
#> 22: 2021-08-05 1 DE 00+ 0 Thursday 22 3 1
#> 23: 2021-08-06 1 DE 00+ 0 Friday 23 3 1
#> 24: 2021-08-07 1 DE 00+ 0 Saturday 24 3 1
#> 25: 2021-08-08 1 DE 00+ 0 Sunday 25 3 1
#> 26: 2021-08-09 1 DE 00+ 0 Monday 26 3 1
#> 27: 2021-08-10 1 DE 00+ 0 Tuesday 27 3 1
#> 28: 2021-08-11 1 DE 00+ 0 Wednesday 28 4 1
#> 29: 2021-08-12 1 DE 00+ 0 Thursday 29 4 1
#> 30: 2021-08-13 1 DE 00+ 0 Friday 30 4 1
#> 31: 2021-08-14 1 DE 00+ 0 Saturday 31 4 1
#> 32: 2021-08-15 1 DE 00+ 0 Sunday 32 4 1
#> 33: 2021-08-16 1 DE 00+ 0 Monday 33 4 1
#> 34: 2021-08-17 1 DE 00+ 0 Tuesday 34 4 1
#> 35: 2021-08-18 1 DE 00+ 0 Wednesday 35 5 1
#> 36: 2021-08-19 1 DE 00+ 0 Thursday 36 5 1
#> 37: 2021-08-20 1 DE 00+ 0 Friday 37 5 1
#> 38: 2021-08-21 1 DE 00+ 0 Saturday 38 5 1
#> 39: 2021-08-22 1 DE 00+ 0 Sunday 39 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: 2021-07-14 1 DE 00+ 0 Wednesday 0 0 0
#> 2: 2021-07-15 1 DE 00+ 0 Thursday 1 0 0
#> 3: 2021-07-16 1 DE 00+ 0 Friday 2 0 0
#> 4: 2021-07-17 1 DE 00+ 0 Saturday 3 0 0
#> 5: 2021-07-18 1 DE 00+ 0 Sunday 4 0 0
#> 6: 2021-07-19 1 DE 00+ 0 Monday 5 0 0
#> 7: 2021-07-20 1 DE 00+ 0 Tuesday 6 0 0
#> 8: 2021-07-21 1 DE 00+ 0 Wednesday 7 1 0
#> 9: 2021-07-22 1 DE 00+ 0 Thursday 8 1 0
#> 10: 2021-07-23 1 DE 00+ 0 Friday 9 1 0
#> 11: 2021-07-24 1 DE 00+ 0 Saturday 10 1 0
#> 12: 2021-07-25 1 DE 00+ 0 Sunday 11 1 0
#> 13: 2021-07-26 1 DE 00+ 0 Monday 12 1 0
#> 14: 2021-07-27 1 DE 00+ 0 Tuesday 13 1 0
#> 15: 2021-07-28 1 DE 00+ 0 Wednesday 14 2 0
#> 16: 2021-07-29 1 DE 00+ 0 Thursday 15 2 0
#> 17: 2021-07-30 1 DE 00+ 0 Friday 16 2 0
#> 18: 2021-07-31 1 DE 00+ 0 Saturday 17 2 0
#> 19: 2021-08-01 1 DE 00+ 0 Sunday 18 2 1
#> 20: 2021-08-02 1 DE 00+ 0 Monday 19 2 1
#> 21: 2021-08-03 1 DE 00+ 0 Tuesday 20 2 1
#> 22: 2021-08-04 1 DE 00+ 0 Wednesday 21 3 1
#> 23: 2021-08-05 1 DE 00+ 0 Thursday 22 3 1
#> 24: 2021-08-06 1 DE 00+ 0 Friday 23 3 1
#> 25: 2021-08-07 1 DE 00+ 0 Saturday 24 3 1
#> 26: 2021-08-08 1 DE 00+ 0 Sunday 25 3 1
#> 27: 2021-08-09 1 DE 00+ 0 Monday 26 3 1
#> 28: 2021-08-10 1 DE 00+ 0 Tuesday 27 3 1
#> 29: 2021-08-11 1 DE 00+ 0 Wednesday 28 4 1
#> 30: 2021-08-12 1 DE 00+ 0 Thursday 29 4 1
#> 31: 2021-08-13 1 DE 00+ 0 Friday 30 4 1
#> 32: 2021-08-14 1 DE 00+ 0 Saturday 31 4 1
#> 33: 2021-08-15 1 DE 00+ 0 Sunday 32 4 1
#> 34: 2021-08-16 1 DE 00+ 0 Monday 33 4 1
#> 35: 2021-08-17 1 DE 00+ 0 Tuesday 34 4 1
#> 36: 2021-08-18 1 DE 00+ 0 Wednesday 35 5 1
#> 37: 2021-08-19 1 DE 00+ 0 Thursday 36 5 1
#> 38: 2021-08-20 1 DE 00+ 0 Friday 37 5 1
#> 39: 2021-08-21 1 DE 00+ 0 Saturday 38 5 1
#> 40: 2021-08-22 1 DE 00+ 0 Sunday 39 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] 39
#>
#> $data$expr_obs
#> [1] 0
#>
#> $data$expr_g
#> [1] 0
#>
#> $data$expr_ft
#> [1] 40
#>
#> $data$expr_fintercept
#> [1] 0
#>
#> $data$expr_fnrow
#> [1] 39
#>
#> $data$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
#>
#> $data$expr_fnindex
#> [1] 39
#>
#> $data$expr_fncol
#> [1] 39
#>
#> $data$expr_rncol
#> [1] 1
#>
#> $data$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
#> 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
#> 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
#> day39
#> 1 0
#> 2 0
#> 3 0
#> 4 0
#> 5 0
#> 6 0
#> 7 0
#> 8 0
#> 9 0
#> 10 0
#> 11 0
#> 12 0
#> 13 0
#> 14 0
#> 15 0
#> 16 0
#> 17 0
#> 18 0
#> 19 0
#> 20 0
#> 21 0
#> 22 0
#> 23 0
#> 24 0
#> 25 0
#> 26 0
#> 27 0
#> 28 0
#> 29 0
#> 30 0
#> 31 0
#> 32 0
#> 33 0
#> 34 0
#> 35 0
#> 36 0
#> 37 0
#> 38 0
#> 39 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
#> 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
#> 29 30 31 32 33 34 35 36 37 38 39
#> 1 0 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0 0
#> 11 0 0 0 0 0 0 0 0 0 0 0
#> 12 0 0 0 0 0 0 0 0 0 0 0
#> 13 0 0 0 0 0 0 0 0 0 0 0
#> 14 0 0 0 0 0 0 0 0 0 0 0
#> 15 0 0 0 0 0 0 0 0 0 0 0
#> 16 0 0 0 0 0 0 0 0 0 0 0
#> 17 0 0 0 0 0 0 0 0 0 0 0
#> 18 0 0 0 0 0 0 0 0 0 0 0
#> 19 0 0 0 0 0 0 0 0 0 0 0
#> 20 0 0 0 0 0 0 0 0 0 0 0
#> 21 0 0 0 0 0 0 0 0 0 0 0
#> 22 0 0 0 0 0 0 0 0 0 0 0
#> 23 0 0 0 0 0 0 0 0 0 0 0
#> 24 0 0 0 0 0 0 0 0 0 0 0
#> 25 0 0 0 0 0 0 0 0 0 0 0
#> 26 0 0 0 0 0 0 0 0 0 0 0
#> 27 0 0 0 0 0 0 0 0 0 0 0
#> 28 0 0 0 0 0 0 0 0 0 0 0
#> 29 1 0 0 0 0 0 0 0 0 0 0
#> 30 0 1 0 0 0 0 0 0 0 0 0
#> 31 0 0 1 0 0 0 0 0 0 0 0
#> 32 0 0 0 1 0 0 0 0 0 0 0
#> 33 0 0 0 0 1 0 0 0 0 0 0
#> 34 0 0 0 0 0 1 0 0 0 0 0
#> 35 0 0 0 0 0 0 1 0 0 0 0
#> 36 0 0 0 0 0 0 0 1 0 0 0
#> 37 0 0 0 0 0 0 0 0 1 0 0
#> 38 0 0 0 0 0 0 0 0 0 1 0
#> 39 0 0 0 0 0 0 0 0 0 0 1
#>
#>
#> $data$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
#> attr(,"assign")
#> [1] 1 2
#>
#> $data$expl_lrd_n
#> [1] 1
#>
#> $data$expl_lrd
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 1 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
#> [3,] 0 0 1 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 0 0 1 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 1 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 1 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 1 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 1 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 0 0 1 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 1 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 1 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 1 0
#> [13,] 0 0 0 0 0 0 0 0 0 0 0 0 1
#> [14,] 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
#> [16,] 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
#> [18,] 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
#> [20,] 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
#> [22,] 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
#> [24,] 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
#> [26,] 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
#> [28,] 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
#> [30,] 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
#> [32,] 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
#> [34,] 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
#> [36,] 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
#> [38,] 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
#> [40,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#> [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,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 1 0 0 0 0 0 0 0 0 0 0
#> [16,] 0 0 1 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [18,] 0 0 0 0 1 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 1 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0 1 0 0 0 0 0
#> [21,] 0 0 0 0 0 0 0 1 0 0 0 0
#> [22,] 0 0 0 0 0 0 0 0 1 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [24,] 0 0 0 0 0 0 0 0 0 0 1 0
#> [25,] 0 0 0 0 0 0 0 0 0 0 0 1
#> [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,] 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
#> [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
#> [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,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 1 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 1 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [30,] 0 0 0 0 1 0 0 0 0 0 0 0
#> [31,] 0 0 0 0 0 1 0 0 0 0 0 0
#> [32,] 0 0 0 0 0 0 1 0 0 0 0 0
#> [33,] 0 0 0 0 0 0 0 1 0 0 0 0
#> [34,] 0 0 0 0 0 0 0 0 1 0 0 0
#> [35,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [36,] 0 0 0 0 0 0 0 0 0 0 1 0
#> [37,] 0 0 0 0 0 0 0 0 0 0 0 1
#> [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
#> [,38] [,39] [,40]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 0 0 0
#> [12,] 0 0 0
#> [13,] 0 0 0
#> [14,] 0 0 0
#> [15,] 0 0 0
#> [16,] 0 0 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#> [19,] 0 0 0
#> [20,] 0 0 0
#> [21,] 0 0 0
#> [22,] 0 0 0
#> [23,] 0 0 0
#> [24,] 0 0 0
#> [25,] 0 0 0
#> [26,] 0 0 0
#> [27,] 0 0 0
#> [28,] 0 0 0
#> [29,] 0 0 0
#> [30,] 0 0 0
#> [31,] 0 0 0
#> [32,] 0 0 0
#> [33,] 0 0 0
#> [34,] 0 0 0
#> [35,] 0 0 0
#> [36,] 0 0 0
#> [37,] 0 0 0
#> [38,] 1 0 0
#> [39,] 0 1 0
#> [40,] 0 0 1
#>
#> $data$expl_obs
#> [1] 0
#>
#> $data$expl_fintercept
#> [1] 1
#>
#> $data$expl_fnrow
#> [1] 40
#>
#> $data$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
#>
#> $data$expl_fnindex
#> [1] 40
#>
#> $data$expl_fncol
#> [1] 0
#>
#> $data$expl_rncol
#> [1] 0
#>
#> $data$expl_fdesign
#>
#> 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
#> 29
#> 30
#> 31
#> 32
#> 33
#> 34
#> 35
#> 36
#> 37
#> 38
#> 39
#> 40
#>
#> $data$expl_rdesign
#> (Intercept)
#> attr(,"assign")
#> [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 4.3 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_p[1],
#> priors$expr_r_int_p[2] * 0.1))
#> }
#> if (data$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: 0x557243f0d230>
#> <environment: 0x557243f00f90>
#>