Extract posterior distributions of temporal effects from a fitted tulpaRatio model with temporal specification.
Usage
temporal(
object,
component = "all",
summary = FALSE,
probs = c(0.025, 0.5, 0.975),
...
)
# S3 method for class 'ratiod_fit'
temporal(
object,
component = "all",
summary = FALSE,
probs = c(0.025, 0.5, 0.975),
...
)
# S3 method for class 'ratiod_temporal_posterior'
print(x, ...)
# S3 method for class 'ratiod_temporal_posterior'
summary(object, probs = c(0.025, 0.5, 0.975), ...)
# S3 method for class 'ratiod_temporal_posterior'
plot(x, component = NULL, type = "ribbon", ...)Arguments
- object
A
ratiod_fitobject fitted withtemporalargument- component
Which component to extract for multi-scale models:
"all"(default),"trend","seasonal", or"short_term".- summary
Logical; if TRUE, return summary statistics instead of full posterior draws.
- probs
Quantiles to compute if
summary = TRUE.- ...
Ignored
- x
A ratiod_temporal_posterior object
- type
Plot type: "ribbon" (default) or "line"
Examples
# \donttest{
# Fit model with multi-scale temporal effects
set.seed(131)
year <- 1:40
# Trend plus a 12-year cycle, shared by count and effort
eff <- 0.4 * sin(year / 8) + 0.3 * sin(2 * pi * year / 12)
x <- rnorm(40)
df <- data.frame(
year = year,
x = x,
count = rpois(40, exp(3 + 0.3 * x + eff)),
effort = rgamma(40, shape = 4.5, rate = exp(-eff))
)
fit <- tratio(
count | effort ~ x,
data = df,
family = ratiod_poisson_gamma(),
temporal = temporal_multiscale("year", trend = "rw2", seasonal = 12),
control = list(iter = 400, chains = 1, seed = 1)
)
#> Warning: 9 divergent transition(s) after warmup; raise `adapt_delta` or reparameterise.
# Extract all temporal effects
temp_post <- temporal(fit)
summary(temp_post)
#> component time_idx time mean sd q2.5
#> trend.1 trend 1 1 1.295180e-01 0.11475308 -0.092845425
#> trend.2 trend 2 2 1.591434e-01 0.08096569 0.007319224
#> trend.3 trend 3 3 1.944180e-01 0.06800347 0.079805254
#> trend.4 trend 4 4 2.277850e-01 0.07070217 0.104311435
#> trend.5 trend 5 5 2.591476e-01 0.07579776 0.132956210
#> trend.6 trend 6 6 2.785572e-01 0.07071609 0.166995334
#> trend.7 trend 7 7 2.893865e-01 0.06823473 0.169515013
#> trend.8 trend 8 8 2.997646e-01 0.07244097 0.152937062
#> trend.9 trend 9 9 3.144883e-01 0.07397010 0.160210085
#> trend.10 trend 10 10 3.293628e-01 0.07247122 0.165088641
#> trend.11 trend 11 11 3.399681e-01 0.06900366 0.225358293
#> trend.12 trend 12 12 3.419770e-01 0.06414479 0.239606100
#> trend.13 trend 13 13 3.412035e-01 0.06266473 0.226956901
#> trend.14 trend 14 14 3.310500e-01 0.05967847 0.225118008
#> trend.15 trend 15 15 3.082892e-01 0.05752771 0.209708370
#> trend.16 trend 16 16 2.742334e-01 0.05297805 0.194693199
#> trend.17 trend 17 17 2.348962e-01 0.05670049 0.115917538
#> trend.18 trend 18 18 1.951775e-01 0.05899585 0.058784762
#> trend.19 trend 19 19 1.551433e-01 0.05984695 0.009932790
#> trend.20 trend 20 20 1.182076e-01 0.06100983 0.002703626
#> trend.21 trend 21 21 8.745329e-02 0.06471092 -0.046113501
#> trend.22 trend 22 22 5.746564e-02 0.06787205 -0.076556481
#> trend.23 trend 23 23 2.963770e-02 0.06964818 -0.116489803
#> trend.24 trend 24 24 -1.777612e-03 0.06935595 -0.141791521
#> trend.25 trend 25 25 -3.695238e-02 0.06655329 -0.158873485
#> trend.26 trend 26 26 -7.207618e-02 0.06470828 -0.193069802
#> trend.27 trend 27 27 -1.095929e-01 0.06169922 -0.232336703
#> trend.28 trend 28 28 -1.532730e-01 0.06100423 -0.264006001
#> trend.29 trend 29 29 -1.988425e-01 0.06683605 -0.319277901
#> trend.30 trend 30 30 -2.520611e-01 0.07837275 -0.380402825
#> trend.31 trend 31 31 -3.026979e-01 0.08632678 -0.470644466
#> trend.32 trend 32 32 -3.457986e-01 0.08719603 -0.522839846
#> trend.33 trend 33 33 -3.814630e-01 0.08120009 -0.553083747
#> trend.34 trend 34 34 -4.101282e-01 0.07139247 -0.556898202
#> trend.35 trend 35 35 -4.374127e-01 0.06710532 -0.561027096
#> trend.36 trend 36 36 -4.624747e-01 0.06932884 -0.616361665
#> trend.37 trend 37 37 -4.884243e-01 0.07028927 -0.658172551
#> trend.38 trend 38 38 -5.146823e-01 0.07136052 -0.675508463
#> trend.39 trend 39 39 -5.457629e-01 0.08791721 -0.743603108
#> trend.40 trend 40 40 -5.828536e-01 0.13619593 -0.904038951
#> seasonal.1 seasonal 1 1 4.430455e-02 0.08633408 -0.131820147
#> seasonal.2 seasonal 2 2 2.405359e-01 0.07549321 0.089090250
#> seasonal.3 seasonal 3 3 2.634974e-01 0.07118529 0.128486358
#> seasonal.4 seasonal 4 4 6.508145e-02 0.08755466 -0.069332233
#> seasonal.5 seasonal 5 5 9.445882e-02 0.08781836 -0.063981478
#> seasonal.6 seasonal 6 6 -6.588303e-02 0.08324934 -0.215455687
#> seasonal.7 seasonal 7 7 -2.267755e-01 0.07791329 -0.384050213
#> seasonal.8 seasonal 8 8 -2.558050e-01 0.09346742 -0.442846453
#> seasonal.9 seasonal 9 9 -2.408791e-01 0.09446799 -0.449890270
#> seasonal.10 seasonal 10 10 -8.580430e-02 0.07920730 -0.241609423
#> seasonal.11 seasonal 11 11 8.518852e-02 0.09461260 -0.076566431
#> seasonal.12 seasonal 12 12 8.208024e-02 0.08141787 -0.108707928
#> short_term.1 short_term 1 1 2.890086e-03 0.02376478 -0.039460459
#> short_term.2 short_term 2 2 2.576543e-03 0.02469870 -0.046394284
#> short_term.3 short_term 3 3 -3.187854e-04 0.02440199 -0.051462187
#> short_term.4 short_term 4 4 4.359219e-05 0.03017858 -0.083039710
#> short_term.5 short_term 5 5 5.340669e-03 0.03013267 -0.053543684
#> short_term.6 short_term 6 6 3.392049e-03 0.02079534 -0.036436417
#> short_term.7 short_term 7 7 3.948761e-03 0.02206919 -0.033524743
#> short_term.8 short_term 8 8 -4.169867e-03 0.02557586 -0.060380138
#> short_term.9 short_term 9 9 -3.006976e-03 0.02408296 -0.054162220
#> short_term.10 short_term 10 10 2.515408e-03 0.02965779 -0.057561160
#> short_term.11 short_term 11 11 -6.950250e-03 0.03055273 -0.069381381
#> short_term.12 short_term 12 12 -1.745551e-03 0.02614252 -0.058284003
#> short_term.13 short_term 13 13 1.345462e-03 0.02553587 -0.053390090
#> short_term.14 short_term 14 14 3.580631e-03 0.02777160 -0.043240041
#> short_term.15 short_term 15 15 -2.633754e-03 0.02631563 -0.057497415
#> short_term.16 short_term 16 16 2.715356e-03 0.03448074 -0.056479933
#> short_term.17 short_term 17 17 -5.683761e-03 0.02829627 -0.069833237
#> short_term.18 short_term 18 18 1.990624e-03 0.03110731 -0.046132301
#> short_term.19 short_term 19 19 -5.974810e-03 0.03345223 -0.109536213
#> short_term.20 short_term 20 20 -1.095722e-03 0.02684312 -0.056543797
#> short_term.21 short_term 21 21 2.108357e-03 0.02750283 -0.042474443
#> short_term.22 short_term 22 22 -5.325873e-03 0.02708757 -0.072323968
#> short_term.23 short_term 23 23 6.965044e-03 0.02446205 -0.040412606
#> short_term.24 short_term 24 24 -6.034075e-04 0.02269270 -0.053792425
#> short_term.25 short_term 25 25 -8.965514e-04 0.02730992 -0.059989606
#> short_term.26 short_term 26 26 -4.200266e-03 0.02738679 -0.069257473
#> short_term.27 short_term 27 27 6.066154e-03 0.02275930 -0.037732714
#> short_term.28 short_term 28 28 -4.810832e-05 0.02650086 -0.054115121
#> short_term.29 short_term 29 29 4.204097e-03 0.02464667 -0.036242709
#> short_term.30 short_term 30 30 1.575451e-05 0.02601733 -0.043117596
#> short_term.31 short_term 31 31 3.062751e-03 0.03157779 -0.044896420
#> short_term.32 short_term 32 32 -9.363564e-04 0.02373634 -0.038804812
#> short_term.33 short_term 33 33 -4.337792e-03 0.02710677 -0.061471376
#> short_term.34 short_term 34 34 -1.724890e-03 0.02716512 -0.054424442
#> short_term.35 short_term 35 35 -3.971320e-03 0.02908797 -0.074793852
#> short_term.36 short_term 36 36 -1.508324e-03 0.02637651 -0.066090880
#> short_term.37 short_term 37 37 -4.701491e-03 0.02240467 -0.055322366
#> short_term.38 short_term 38 38 9.627035e-03 0.03391478 -0.037014941
#> short_term.39 short_term 39 39 3.053885e-03 0.02554179 -0.049591706
#> short_term.40 short_term 40 40 -8.075178e-04 0.02357806 -0.045191377
#> q50 q97.5
#> trend.1 1.375387e-01 0.35816499
#> trend.2 1.575449e-01 0.30707722
#> trend.3 1.883662e-01 0.32883562
#> trend.4 2.197136e-01 0.39015790
#> trend.5 2.625226e-01 0.42518251
#> trend.6 2.763400e-01 0.40707963
#> trend.7 2.851497e-01 0.41066405
#> trend.8 3.017601e-01 0.43583947
#> trend.9 3.248231e-01 0.44687277
#> trend.10 3.336741e-01 0.46009656
#> trend.11 3.431672e-01 0.47515791
#> trend.12 3.375603e-01 0.46822246
#> trend.13 3.426748e-01 0.45158639
#> trend.14 3.283659e-01 0.43914016
#> trend.15 3.006032e-01 0.43287679
#> trend.16 2.731406e-01 0.39774340
#> trend.17 2.318522e-01 0.35106326
#> trend.18 1.992381e-01 0.30228311
#> trend.19 1.554247e-01 0.26087638
#> trend.20 1.176717e-01 0.23718088
#> trend.21 8.679125e-02 0.20482259
#> trend.22 5.927479e-02 0.17985389
#> trend.23 3.273908e-02 0.17997830
#> trend.24 -4.763491e-03 0.11801433
#> trend.25 -4.005123e-02 0.09196197
#> trend.26 -6.918199e-02 0.07436145
#> trend.27 -1.129393e-01 0.01431592
#> trend.28 -1.571040e-01 -0.01121079
#> trend.29 -1.988060e-01 -0.06953203
#> trend.30 -2.438886e-01 -0.10609639
#> trend.31 -2.968852e-01 -0.16898022
#> trend.32 -3.370466e-01 -0.20732642
#> trend.33 -3.657050e-01 -0.24580386
#> trend.34 -4.016670e-01 -0.29503939
#> trend.35 -4.264205e-01 -0.32269135
#> trend.36 -4.580855e-01 -0.32217830
#> trend.37 -4.834584e-01 -0.34687647
#> trend.38 -5.098930e-01 -0.39018297
#> trend.39 -5.385403e-01 -0.39293228
#> trend.40 -5.725659e-01 -0.36792417
#> seasonal.1 4.115647e-02 0.21825369
#> seasonal.2 2.404472e-01 0.37984246
#> seasonal.3 2.639027e-01 0.38100049
#> seasonal.4 6.701928e-02 0.28604794
#> seasonal.5 8.866412e-02 0.28149267
#> seasonal.6 -7.488725e-02 0.09192609
#> seasonal.7 -2.247741e-01 -0.08184759
#> seasonal.8 -2.500673e-01 -0.08535637
#> seasonal.9 -2.398555e-01 -0.08275983
#> seasonal.10 -8.496388e-02 0.06310366
#> seasonal.11 7.642099e-02 0.28406793
#> seasonal.12 7.843251e-02 0.20835712
#> short_term.1 4.153832e-03 0.04828143
#> short_term.2 1.372506e-03 0.05685747
#> short_term.3 -2.217901e-03 0.04215233
#> short_term.4 2.954400e-03 0.05764138
#> short_term.5 3.875364e-03 0.07452240
#> short_term.6 7.835926e-04 0.04499666
#> short_term.7 2.950484e-03 0.04298287
#> short_term.8 -1.709007e-03 0.04254435
#> short_term.9 -1.684943e-03 0.04466790
#> short_term.10 1.476131e-03 0.06435569
#> short_term.11 -4.845923e-03 0.04274622
#> short_term.12 1.693415e-03 0.03863040
#> short_term.13 6.575105e-04 0.06809772
#> short_term.14 4.474431e-05 0.06955817
#> short_term.15 -2.000181e-03 0.05346401
#> short_term.16 7.532901e-04 0.08702000
#> short_term.17 -5.222519e-03 0.04125228
#> short_term.18 -1.401289e-03 0.07013730
#> short_term.19 3.393827e-04 0.04361016
#> short_term.20 -6.783430e-04 0.05136388
#> short_term.21 -1.727098e-03 0.05413462
#> short_term.22 -1.441050e-03 0.04075677
#> short_term.23 3.758418e-03 0.05814236
#> short_term.24 5.575185e-04 0.03967760
#> short_term.25 -2.344189e-03 0.04977653
#> short_term.26 -3.811857e-03 0.03987007
#> short_term.27 5.860256e-03 0.05470251
#> short_term.28 -3.243041e-03 0.07612144
#> short_term.29 2.232018e-03 0.05378083
#> short_term.30 -1.664081e-03 0.05759772
#> short_term.31 2.536698e-03 0.10104843
#> short_term.32 -1.874075e-03 0.05543258
#> short_term.33 -3.819134e-03 0.04314419
#> short_term.34 -4.729157e-04 0.03332655
#> short_term.35 -2.506099e-03 0.04688092
#> short_term.36 2.842733e-05 0.05226885
#> short_term.37 -3.566035e-03 0.03950155
#> short_term.38 3.346477e-03 0.10463210
#> short_term.39 2.867903e-03 0.06591704
#> short_term.40 -1.010922e-03 0.05132395
# }