Draw R from marginal posterior distribution
draw_R(
epsilon,
incid,
lambda,
priors,
shape_R_flat = NULL,
t_min = NULL,
t_max = nrow(incid),
seed = NULL
)a value or vector of values for the relative transmissibility of the "new" pathogen/strain/variant(s) compared to the reference pathogen/strain/variant
a multidimensional array containing values of the (local) incidence for each time step (1st dimension), location (2nd dimension) and pathogen/strain/variant (3rd dimension)
a multidimensional array containing values of the overall infectivity for each time step (1st dimension), location (2nd dimension) and pathogen/strain/variant (3rd dimension). The overall infectivity for a given location and pathogen/strain/variant represents the sum of the incidence for that location and that pathogen/strain/variant at all previous time steps, weighted by the current infectivity of those past incident cases. It can be calculated from the incidence `incid` and the distribution of the serial interval using function `compute_lambda`)
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
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), as obtained from function `get_shape_R_flat`
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).
an integer >`t_min` and <=`nrow(incid)` giving the maximum time step to consider in the estimation. Default value is `nrow(incid)`.
a numeric value used to fix the random seed
a matrix of the instantaneous reproduction number R for the reference pathogen/strain/variant for each time step (row) and each location (column) drawn from the marginal posterior distribution
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))
incid <- process_I_multivariant(incid)
# arbitrary serial interval, same for both variants
w_v <- c(0, 0.2, 0.5, 0.3)
si_distr <- cbind(w_v, w_v)
lambda <- compute_lambda(incid, si_distr)
# Epsilon = 1 i.e. no transmission advantage
epsilon <- 1
draw_R(epsilon, incid$local, lambda, priors, seed = 1, t_min = 2L)
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#> [2,] 4.1754778 6.4177517 5.2923993
#> [3,] 1.8419050 1.3249988 1.3698939
#> [4,] 1.2759194 0.7448514 1.2165933
#> [5,] 1.0686560 1.1385853 0.9931790
#> [6,] 0.6649529 0.9588906 0.9569318
#> [7,] 1.0855305 0.7220602 1.1054765
#> [8,] 1.1447094 0.9048344 1.0594580
#> [9,] 1.1061911 0.8729437 0.8835008
#> [10,] 0.9088510 1.0958110 1.1002343
#> [11,] 0.8067693 0.6688852 0.7408918
#> [12,] 0.8428318 0.7756092 1.1733477
#> [13,] 0.9101662 0.9097909 1.3912541
#> [14,] 0.9651637 0.7926315 1.0355282
#> [15,] 0.7882348 0.9625400 0.8841484
#> [16,] 1.1646074 0.9902207 0.6335370
#> [17,] 1.1104524 0.9049448 0.7787620
#> [18,] 1.1882926 0.8754851 1.3867636
#> [19,] 1.1552044 0.8130454 0.8797009
#> [20,] 0.9915667 1.4897800 1.6469520
#> [21,] 0.5856106 0.9788908 1.0097665
#> [22,] 1.0892993 0.7119596 1.2402854
#> [23,] 1.2292548 1.3263248 0.5354871
#> [24,] 0.8285957 0.7072327 0.9102313
#> [25,] 0.8724321 0.9061527 1.1884938
#> [26,] 0.9237510 1.1813990 1.0648465
#> [27,] 0.9525089 0.9202169 0.8872294
#> [28,] 0.8370521 1.2081112 1.0916053
#> [29,] 0.6949556 0.8428264 1.0582477
#> [30,] 1.2061801 0.8941177 1.3923786
#> [31,] 0.8826097 1.0710726 0.9301088
#> [32,] 1.2327881 0.9231198 1.1946829
#> [33,] 1.1506565 1.3809056 1.0730621
#> [34,] 0.9390971 1.1545916 1.2093712
#> [35,] 0.8777327 1.1387221 0.8908964
#> [36,] 1.1017039 0.8589781 0.6169645
#> [37,] 0.8290427 0.6861502 0.6858241
#> [38,] 0.6606188 0.7653708 1.1000737
#> [39,] 1.1519406 1.5505936 0.8274025
#> [40,] 0.9504261 0.9985273 1.1971140
#> [41,] 1.0346379 1.0784597 0.9639281
#> [42,] 0.8937274 0.9581058 0.9477953
#> [43,] 1.0517454 1.1116471 0.7577208
#> [44,] 0.7418473 0.6679318 1.4543461
#> [45,] 1.0310064 1.4865314 0.9166080
#> [46,] 1.1161898 1.2148392 1.3371404
#> [47,] 0.9373445 1.2596910 0.9934363
#> [48,] 1.1047674 0.7223597 1.1041808
#> [49,] 0.9454872 0.8487927 0.7621509
#> [50,] 0.9726150 0.6782616 0.6581815
#> [51,] 1.1331269 0.9551992 0.9970270
#> [52,] 0.9812364 1.3248666 0.8774496
#> [53,] 0.8179912 0.8133895 1.0525876
#> [54,] 0.6860385 1.2333217 1.3442366
#> [55,] 1.3251154 0.9713591 0.9037205
#> [56,] 1.0091474 1.0657415 0.9485403
#> [57,] 1.5131681 0.8203213 0.6695261
#> [58,] 1.0827581 1.1984967 1.2848857
#> [59,] 0.8247382 1.4099866 1.3332242
#> [60,] 0.9489409 0.7544445 1.1630366
#> [61,] 0.7181302 0.7638487 0.6062275
#> [62,] 1.0070208 0.8697879 1.3527410
#> [63,] 0.9492489 1.0674464 1.2099130
#> [64,] 0.9915170 1.3831627 0.9157278
#> [65,] 0.8493494 1.3564213 0.7937749
#> [66,] 1.2712944 0.9034245 1.2361158
#> [67,] 1.1528061 1.3488304 1.1685096
#> [68,] 0.9272305 0.8961155 1.0456598
#> [69,] 1.1104629 1.0981111 1.0246826
#> [70,] 1.0498748 0.9720965 0.7978442
#> [71,] 1.2236467 1.1900186 1.1433775
#> [72,] 0.9091075 0.9832146 1.1950250
#> [73,] 1.1229097 0.7540497 0.9760204
#> [74,] 1.2827117 0.6962321 0.8988835
#> [75,] 0.7919397 1.2507439 0.8486861
#> [76,] 1.2478087 0.6800184 0.7545819
#> [77,] 1.1356190 0.7181952 0.9782834
#> [78,] 1.3564850 0.6496650 0.9343853
#> [79,] 1.1021314 0.6302136 0.6890480
#> [80,] 0.7137906 1.0374863 1.0307920
#> [81,] 1.0262212 0.9327329 1.0082427
#> [82,] 1.2072557 0.7366215 0.7741553
#> [83,] 1.1551378 0.9434132 1.3272573
#> [84,] 0.8112364 0.6530636 1.1301635
#> [85,] 0.7844711 1.5287749 1.0606628
#> [86,] 0.7416506 0.6695974 0.9329863
#> [87,] 1.4038401 1.3822027 1.1024619
#> [88,] 1.1395487 1.0876609 0.9585883
#> [89,] 1.1861500 1.3852849 0.9410206
#> [90,] 1.0616345 0.9183850 0.7569440
#> [91,] 1.3814038 1.2319826 0.4870707
#> [92,] 0.8398616 0.9738715 1.0986567
#> [93,] 0.9806569 0.7642993 1.2182067
#> [94,] 0.9811198 1.0249681 1.0191023
#> [95,] 0.6396538 0.7908576 0.6487266
#> [96,] 0.8903295 0.7461245 1.1304180
#> [97,] 1.0504853 0.8585829 1.1043524
#> [98,] 1.0872457 0.9527889 0.8528483
#> [99,] 1.0057392 1.2131339 0.6324443
#> [100,] 0.9490229 1.0053291 1.0373155