Precompute shape of posterior distribution for R

get_shape_R_flat(incid, priors, t_min = 2L, t_max = nrow(incid))

Arguments

incid: a multidimensional array containing values of the (local) incidence for each time step (1st dimension), location (2nd dimension) and pathogen/strain/variant (3rd dimension)
priors: a list of prior parameters (shape and scale of a gamma distribution) for epsilon and R; can be obtained from the function default_priors. The prior for R is assumed to be the same for all time steps and all locations
t_min: an integer > 1 giving the minimum time step to consider in the estimation. Default value is 2 (as the estimation is conditional on observations at time step 1 and can therefore only start at time step 2).
t_max: an integer > t_min and <= nrow(incid) giving the maximum time step to consider in the estimation. Default value is nrow(incid).

Value

a vector of the shape of the posterior distribution of R for each time step t and each location l (stored in element (l-1)*(t_max - t_min + 1) + t of the vector)

Examples

n_v <- 2
n_loc <- 3 # 3 locations
T <- 100 # 100 time steps
priors <- default_priors()
# constant incidence 10 per day everywhere
incid <- array(10, dim = c(T, n_loc, n_v))
get_shape_R_flat(incid, priors)
#>   [1] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#>  [13] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#>  [25] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#>  [37] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#>  [49] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#>  [61] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#>  [73] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#>  [85] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#>  [97] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [109] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [121] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [133] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [145] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [157] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [169] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [181] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [193] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [205] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [217] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [229] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [241] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [253] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [265] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [277] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04
#> [289] 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04 20.04