Extract posterior draws for specified parameters and spread them into a wide format data frame. This function provides tidybayes-style syntax without requiring the tidybayes package.
Usage
spread_draws(object, ..., regex = FALSE, ndraws = NULL)
# S3 method for class 'ratiod_fit'
spread_draws(object, ..., regex = FALSE, ndraws = NULL)Examples
# \donttest{
# Generate synthetic data
set.seed(222)
n <- 50
df <- data.frame(
count = rpois(n, lambda = 20),
effort = rgamma(n, shape = 10, rate = 1),
depth = rnorm(n),
site = sample(letters[1:5], n, replace = TRUE)
)
# Fit model
fit <- tratio(
count | effort ~ depth + (1 | site),
data = df,
family = ratiod_poisson_gamma(),
mode = "hmc",
control = list(iter = 200, warmup = 100, chains = 1)
)
#> Inference: Exact (Tier 1)
#> Backend: hmc
#> Fitting ratio model...
#> Family: poisson_gamma
#> Observations: 50
#> Iterations: 200 (warmup: 100)
#> Running NUTS sampler...
#> Parameters: 11
#> Iterations: 200 (warmup: 100)
#> Chains: 1 (cores: 1)
# Extract fixed effects
spread_draws(fit, beta_num, beta_denom)
#> .chain .iteration .draw beta_num.1. beta_num.2. beta_denom.1.
#> 1 1 1 1 3.070224 -0.0496250660 2.277004
#> 2 1 2 2 3.091140 -0.0005273839 2.362341
#> 3 1 3 3 3.047892 -0.0286481257 2.296117
#> 4 1 4 4 3.076937 -0.0146300795 2.288608
#> 5 1 5 5 3.039080 -0.0100534421 2.293869
#> 6 1 6 6 3.018987 -0.0108732817 2.297061
#> 7 1 7 7 3.075552 -0.0147874112 2.262964
#> 8 1 8 8 3.049691 -0.0191760451 2.231092
#> 9 1 9 9 3.064574 0.0068357539 2.370713
#> 10 1 10 10 3.008138 0.0186376060 2.198892
#> 11 1 11 11 3.073350 0.0039656217 2.401639
#> 12 1 12 12 3.049242 -0.0217991928 2.239452
#> 13 1 13 13 3.058315 0.0283450766 2.309487
#> 14 1 14 14 3.046717 -0.0070611881 2.307831
#> 15 1 15 15 3.056470 0.0059328934 2.256209
#> 16 1 16 16 3.018799 -0.0255361077 2.306899
#> 17 1 17 17 3.016379 0.0448179133 2.305199
#> 18 1 18 18 3.007528 0.0146187537 2.325658
#> 19 1 19 19 3.022206 0.0415209383 2.277813
#> 20 1 20 20 3.099092 -0.0226355817 2.360671
#> 21 1 21 21 3.073815 -0.0150848018 2.260972
#> 22 1 22 22 3.010547 0.0190600952 2.226722
#> 23 1 23 23 2.962371 0.0184165080 2.279670
#> 24 1 24 24 2.967657 0.0146459622 2.250404
#> 25 1 25 25 3.030269 0.0295286722 2.253732
#> 26 1 26 26 3.056841 0.0147689983 2.289466
#> 27 1 27 27 3.027489 -0.0278435759 2.306991
#> 28 1 28 28 3.045444 0.0068378235 2.321546
#> 29 1 29 29 3.035732 -0.0146864439 2.311502
#> 30 1 30 30 3.040943 0.0528328805 2.282724
#> 31 1 31 31 3.028085 0.0470707697 2.281616
#> 32 1 32 32 2.978241 -0.0118719799 2.255897
#> 33 1 33 33 3.035785 0.0108523053 2.270597
#> 34 1 34 34 3.036271 0.0531886438 2.260748
#> 35 1 35 35 3.012053 0.0034972773 2.310537
#> 36 1 36 36 3.003369 -0.0301093670 2.297512
#> 37 1 37 37 3.134142 -0.0012902630 2.316594
#> 38 1 38 38 3.013788 -0.0224128388 2.334449
#> 39 1 39 39 3.050755 -0.0089753160 2.379286
#> 40 1 40 40 3.050755 -0.0089753160 2.379286
#> 41 1 41 41 3.088536 -0.0216017769 2.278100
#> 42 1 42 42 3.062848 -0.0255651630 2.339149
#> 43 1 43 43 3.016873 -0.0481015896 2.343745
#> 44 1 44 44 3.016853 -0.0138596968 2.373974
#> 45 1 45 45 3.010280 -0.0132379970 2.258908
#> 46 1 46 46 3.034366 0.0121066736 2.313612
#> 47 1 47 47 3.034366 0.0121066736 2.313612
#> 48 1 48 48 3.023764 -0.0397783700 2.334184
#> 49 1 49 49 3.061580 0.0312509388 2.362555
#> 50 1 50 50 3.061580 0.0312509388 2.362555
#> 51 1 51 51 3.071059 0.0385050986 2.280937
#> 52 1 52 52 3.093930 -0.0202519864 2.330243
#> 53 1 53 53 3.085851 -0.0131631394 2.305518
#> 54 1 54 54 3.047552 -0.0205831288 2.283348
#> 55 1 55 55 3.021533 -0.0468925450 2.325361
#> 56 1 56 56 3.048761 0.0058746412 2.308400
#> 57 1 57 57 3.086259 -0.0213012044 2.348754
#> 58 1 58 58 3.067979 0.0162271926 2.225288
#> 59 1 59 59 3.054093 -0.0232809517 2.292216
#> 60 1 60 60 3.054473 -0.0345348363 2.300185
#> 61 1 61 61 3.041888 0.0363117492 2.268884
#> 62 1 62 62 3.023070 0.0085966544 2.256429
#> 63 1 63 63 2.993082 0.0319598264 2.171183
#> 64 1 64 64 2.930494 0.0498483904 2.327715
#> 65 1 65 65 3.120002 -0.0141375288 2.257912
#> 66 1 66 66 2.963636 0.0389566116 2.313121
#> 67 1 67 67 3.104280 0.0440478565 2.286893
#> 68 1 68 68 3.011221 0.0004574254 2.323548
#> 69 1 69 69 3.092979 0.0304905552 2.264257
#> 70 1 70 70 3.047569 0.0048926410 2.317834
#> 71 1 71 71 3.033686 -0.0431229050 2.313142
#> 72 1 72 72 3.093883 -0.0341420549 2.333799
#> 73 1 73 73 3.093883 -0.0341420549 2.333799
#> 74 1 74 74 3.069523 -0.0460690129 2.325759
#> 75 1 75 75 3.091897 -0.0095743738 2.418706
#> 76 1 76 76 3.065459 -0.0026882617 2.184014
#> 77 1 77 77 3.066223 0.0253800411 2.247524
#> 78 1 78 78 3.027824 -0.0041286370 2.284701
#> 79 1 79 79 3.015897 -0.0227615747 2.306595
#> 80 1 80 80 3.003392 -0.0267842063 2.227561
#> 81 1 81 81 3.022300 -0.0216512180 2.300297
#> 82 1 82 82 3.022594 0.0100649852 2.299942
#> 83 1 83 83 3.007334 0.0088155616 2.328942
#> 84 1 84 84 2.967047 0.0070286099 2.362238
#> 85 1 85 85 3.071455 0.0101214355 2.281202
#> 86 1 86 86 3.039532 -0.0050095016 2.247681
#> 87 1 87 87 3.097515 -0.0006533426 2.249625
#> 88 1 88 88 3.058373 0.0192203060 2.318310
#> 89 1 89 89 3.017995 -0.0385909464 2.228858
#> 90 1 90 90 3.060231 -0.0380638036 2.324792
#> 91 1 91 91 3.059300 0.0205049538 2.372701
#> 92 1 92 92 3.064517 -0.0648102284 2.427101
#> 93 1 93 93 3.128933 0.0067571763 2.416071
#> 94 1 94 94 3.104549 0.0287602405 2.320671
#> 95 1 95 95 3.071079 -0.0318210885 2.314203
#> 96 1 96 96 3.002131 -0.0068074446 2.281281
#> 97 1 97 97 3.024617 -0.0060118742 2.284238
#> 98 1 98 98 3.033392 -0.0155922365 2.337058
#> 99 1 99 99 3.046349 -0.0062987150 2.381068
#> 100 1 100 100 3.052079 0.0581786084 2.410583
#> beta_denom.2.
#> 1 -0.0556623894
#> 2 -0.1648648327
#> 3 0.1330470792
#> 4 0.0455439148
#> 5 0.0476631301
#> 6 -0.0279501367
#> 7 0.0099163847
#> 8 -0.0244856602
#> 9 -0.0214337411
#> 10 0.0173597031
#> 11 0.0386203510
#> 12 -0.0115268622
#> 13 0.1108441418
#> 14 0.1120527570
#> 15 -0.0613730707
#> 16 0.0676712488
#> 17 -0.0631800353
#> 18 -0.0300624393
#> 19 0.0007300507
#> 20 0.0081692809
#> 21 -0.0199235824
#> 22 -0.0390679834
#> 23 -0.1153973211
#> 24 -0.1374640645
#> 25 0.0225189927
#> 26 -0.0265208946
#> 27 -0.0092398688
#> 28 0.0084572600
#> 29 -0.0251195804
#> 30 0.0025496558
#> 31 -0.0060997233
#> 32 0.0268757971
#> 33 0.0290834122
#> 34 0.0225017815
#> 35 -0.0810491254
#> 36 -0.0692180143
#> 37 -0.0680103977
#> 38 -0.0381104042
#> 39 0.0422078661
#> 40 0.0422078661
#> 41 -0.0603650995
#> 42 0.0382076695
#> 43 -0.0210955503
#> 44 -0.0478984044
#> 45 0.0303633027
#> 46 0.0028398676
#> 47 0.0028398676
#> 48 0.0193681488
#> 49 -0.0836040954
#> 50 -0.0836040954
#> 51 0.0864015014
#> 52 -0.0131423582
#> 53 -0.0034499291
#> 54 -0.0276155749
#> 55 0.0124935583
#> 56 0.0751788064
#> 57 -0.0448502222
#> 58 0.0162819848
#> 59 -0.0286309454
#> 60 -0.0084741016
#> 61 -0.0479273893
#> 62 -0.0429286858
#> 63 -0.0623287726
#> 64 0.0134092029
#> 65 -0.0306820953
#> 66 0.0169693241
#> 67 -0.0276412627
#> 68 -0.0062884772
#> 69 -0.0533802281
#> 70 -0.0603977552
#> 71 0.0103664880
#> 72 -0.0379166230
#> 73 -0.0379166230
#> 74 -0.0002416884
#> 75 -0.0507168537
#> 76 0.0042033947
#> 77 -0.0326784346
#> 78 -0.0292394059
#> 79 -0.0037563336
#> 80 -0.0433964922
#> 81 0.0172352973
#> 82 -0.0249980550
#> 83 -0.0140043589
#> 84 -0.0114684785
#> 85 -0.0132953674
#> 86 0.0116570299
#> 87 -0.0674848120
#> 88 0.0420371263
#> 89 0.0625430549
#> 90 -0.0219545570
#> 91 -0.0089632771
#> 92 -0.0273910941
#> 93 0.0026667042
#> 94 -0.0597093232
#> 95 -0.0080021168
#> 96 -0.0379468408
#> 97 -0.0455088326
#> 98 0.0543568879
#> 99 -0.0071952009
#> 100 -0.0153868752
# Subsample draws
spread_draws(fit, sigma_re, ndraws = 100)
#> .chain .iteration .draw sigma_re
#> 1 1 1 1 0.006320962
#> 2 1 2 2 0.114242352
#> 3 1 3 3 0.012815100
#> 4 1 4 4 0.012829838
#> 5 1 5 5 0.025820559
#> 6 1 6 6 0.030684354
#> 7 1 7 7 0.100390021
#> 8 1 8 8 0.096375332
#> 9 1 9 9 0.032058525
#> 10 1 10 10 0.061305487
#> 11 1 11 11 0.021907251
#> 12 1 12 12 0.055890660
#> 13 1 13 13 0.033227940
#> 14 1 14 14 0.082589597
#> 15 1 15 15 0.088329368
#> 16 1 16 16 0.012216207
#> 17 1 17 17 0.023146378
#> 18 1 18 18 0.022159602
#> 19 1 19 19 0.090730108
#> 20 1 20 20 0.048972617
#> 21 1 21 21 0.045597107
#> 22 1 22 22 0.038492631
#> 23 1 23 23 0.029390719
#> 24 1 24 24 0.014819806
#> 25 1 25 25 0.040597492
#> 26 1 26 26 0.030000705
#> 27 1 27 27 0.097132700
#> 28 1 28 28 0.096258240
#> 29 1 29 29 0.033032950
#> 30 1 30 30 0.014093029
#> 31 1 31 31 0.019836283
#> 32 1 32 32 0.019724880
#> 33 1 33 33 0.010338487
#> 34 1 34 34 0.064865364
#> 35 1 35 35 0.033813944
#> 36 1 36 36 0.040805170
#> 37 1 37 37 0.023723696
#> 38 1 38 38 0.027747359
#> 39 1 39 39 0.024198386
#> 40 1 40 40 0.024198386
#> 41 1 41 41 0.039149648
#> 42 1 42 42 0.047626967
#> 43 1 43 43 0.011128766
#> 44 1 44 44 0.005106663
#> 45 1 45 45 0.002358207
#> 46 1 46 46 0.030381412
#> 47 1 47 47 0.030381412
#> 48 1 48 48 0.027578360
#> 49 1 49 49 0.039985463
#> 50 1 50 50 0.039985463
#> 51 1 51 51 0.029405769
#> 52 1 52 52 0.027869910
#> 53 1 53 53 0.014128252
#> 54 1 54 54 0.013829996
#> 55 1 55 55 0.017112193
#> 56 1 56 56 0.017797644
#> 57 1 57 57 0.019048195
#> 58 1 58 58 0.020847803
#> 59 1 59 59 0.036523011
#> 60 1 60 60 0.029128357
#> 61 1 61 61 0.052774933
#> 62 1 62 62 0.053472083
#> 63 1 63 63 0.068915037
#> 64 1 64 64 0.044882749
#> 65 1 65 65 0.070438049
#> 66 1 66 66 0.067517153
#> 67 1 67 67 0.078054899
#> 68 1 68 68 0.075184675
#> 69 1 69 69 0.144792895
#> 70 1 70 70 0.058917399
#> 71 1 71 71 0.056233840
#> 72 1 72 72 0.054902917
#> 73 1 73 73 0.054902917
#> 74 1 74 74 0.067829701
#> 75 1 75 75 0.069881112
#> 76 1 76 76 0.012242129
#> 77 1 77 77 0.010066751
#> 78 1 78 78 0.012782021
#> 79 1 79 79 0.011853657
#> 80 1 80 80 0.036875039
#> 81 1 81 81 0.038457887
#> 82 1 82 82 0.054093539
#> 83 1 83 83 0.054047037
#> 84 1 84 84 0.011485119
#> 85 1 85 85 0.010804594
#> 86 1 86 86 0.020002652
#> 87 1 87 87 0.022388405
#> 88 1 88 88 0.058189350
#> 89 1 89 89 0.106354630
#> 90 1 90 90 0.131453019
#> 91 1 91 91 0.054139142
#> 92 1 92 92 0.069613858
#> 93 1 93 93 0.067267081
#> 94 1 94 94 0.043001383
#> 95 1 95 95 0.062602488
#> 96 1 96 96 0.043615944
#> 97 1 97 97 0.042220044
#> 98 1 98 98 0.044996566
#> 99 1 99 99 0.044879763
#> 100 1 100 100 0.038344241
# }