Hi,
I am wondering if it is possible to obtain response value estimates (estimate__ column) that you obtain when doing conditional_effects(brmsfit) but if instead of a brmsfit object, I only have a stanfit object. I am wondering this because I want to modify a model by modifying the STAN code directly, but I don’t know how to obtain a plot once I have run the model.
Here is the code using brms
library(brms)
N <- 10
y <- rnorm(10)
x <- rnorm(10)
df <- data.frame(x, y)
fit <- brm(y ~ x, data = df)
data <- conditional_effects(fit)
print(data[["x"]])
Which yields the output:
x y cond__ effect1__ estimate__ se__
1 -1.777412243 0.1417486 1 -1.777412243 0.08445399 0.5013894
2 -1.747889444 0.1417486 1 -1.747889444 0.08592914 0.4919022
3 -1.718366646 0.1417486 1 -1.718366646 0.08487412 0.4840257
4 -1.688843847 0.1417486 1 -1.688843847 0.08477227 0.4744689
5 -1.659321048 0.1417486 1 -1.659321048 0.08637019 0.4671830
6 -1.629798249 0.1417486 1 -1.629798249 0.08853233 0.4612196
7 -1.600275450 0.1417486 1 -1.600275450 0.08993511 0.4566040
8 -1.570752651 0.1417486 1 -1.570752651 0.08987979 0.4501722
9 -1.541229852 0.1417486 1 -1.541229852 0.09079337 0.4415650
10 -1.511707053 0.1417486 1 -1.511707053 0.09349952 0.4356073
11 -1.482184255 0.1417486 1 -1.482184255 0.09382594 0.4292237
12 -1.452661456 0.1417486 1 -1.452661456 0.09406637 0.4229115
13 -1.423138657 0.1417486 1 -1.423138657 0.09537000 0.4165933
14 -1.393615858 0.1417486 1 -1.393615858 0.09626168 0.4126735
15 -1.364093059 0.1417486 1 -1.364093059 0.09754818 0.4060894
16 -1.334570260 0.1417486 1 -1.334570260 0.09737763 0.3992320
17 -1.305047461 0.1417486 1 -1.305047461 0.09646332 0.3929951
18 -1.275524662 0.1417486 1 -1.275524662 0.09713718 0.3870211
19 -1.246001864 0.1417486 1 -1.246001864 0.09915170 0.3806628
20 -1.216479065 0.1417486 1 -1.216479065 0.10046754 0.3738948
21 -1.186956266 0.1417486 1 -1.186956266 0.10192677 0.3675363
22 -1.157433467 0.1417486 1 -1.157433467 0.10329695 0.3613282
23 -1.127910668 0.1417486 1 -1.127910668 0.10518868 0.3533583
24 -1.098387869 0.1417486 1 -1.098387869 0.10533191 0.3484098
25 -1.068865070 0.1417486 1 -1.068865070 0.10582833 0.3442075
26 -1.039342271 0.1417486 1 -1.039342271 0.10864510 0.3370518
27 -1.009819473 0.1417486 1 -1.009819473 0.10830692 0.3325785
28 -0.980296674 0.1417486 1 -0.980296674 0.11107417 0.3288747
29 -0.950773875 0.1417486 1 -0.950773875 0.11229667 0.3249769
30 -0.921251076 0.1417486 1 -0.921251076 0.11420108 0.3216303
31 -0.891728277 0.1417486 1 -0.891728277 0.11533604 0.3160908
32 -0.862205478 0.1417486 1 -0.862205478 0.11671013 0.3099456
33 -0.832682679 0.1417486 1 -0.832682679 0.11934724 0.3059504
34 -0.803159880 0.1417486 1 -0.803159880 0.12031792 0.3035792
35 -0.773637082 0.1417486 1 -0.773637082 0.12114301 0.2985330
36 -0.744114283 0.1417486 1 -0.744114283 0.12149371 0.2949334
37 -0.714591484 0.1417486 1 -0.714591484 0.12259197 0.2915398
38 -0.685068685 0.1417486 1 -0.685068685 0.12308763 0.2905327
39 -0.655545886 0.1417486 1 -0.655545886 0.12409683 0.2861451
40 -0.626023087 0.1417486 1 -0.626023087 0.12621634 0.2834400
41 -0.596500288 0.1417486 1 -0.596500288 0.12898609 0.2838938
42 -0.566977489 0.1417486 1 -0.566977489 0.12925969 0.2802667
43 -0.537454691 0.1417486 1 -0.537454691 0.13050938 0.2782553
44 -0.507931892 0.1417486 1 -0.507931892 0.12968382 0.2765127
45 -0.478409093 0.1417486 1 -0.478409093 0.13252478 0.2735946
46 -0.448886294 0.1417486 1 -0.448886294 0.13414535 0.2727640
47 -0.419363495 0.1417486 1 -0.419363495 0.13453109 0.2710725
48 -0.389840696 0.1417486 1 -0.389840696 0.13526957 0.2683500
49 -0.360317897 0.1417486 1 -0.360317897 0.13675913 0.2665745
50 -0.330795098 0.1417486 1 -0.330795098 0.13987067 0.2658021
51 -0.301272300 0.1417486 1 -0.301272300 0.14111051 0.2668740
52 -0.271749501 0.1417486 1 -0.271749501 0.14382292 0.2680711
53 -0.242226702 0.1417486 1 -0.242226702 0.14531118 0.2662193
54 -0.212703903 0.1417486 1 -0.212703903 0.14656473 0.2670958
55 -0.183181104 0.1417486 1 -0.183181104 0.14689102 0.2677249
56 -0.153658305 0.1417486 1 -0.153658305 0.14749250 0.2698547
57 -0.124135506 0.1417486 1 -0.124135506 0.14880275 0.2711767
58 -0.094612707 0.1417486 1 -0.094612707 0.15072864 0.2719037
59 -0.065089909 0.1417486 1 -0.065089909 0.15257772 0.2720895
60 -0.035567110 0.1417486 1 -0.035567110 0.15434018 0.2753563
61 -0.006044311 0.1417486 1 -0.006044311 0.15556588 0.2783308
62 0.023478488 0.1417486 1 0.023478488 0.15481341 0.2802336
63 0.053001287 0.1417486 1 0.053001287 0.15349716 0.2833364
64 0.082524086 0.1417486 1 0.082524086 0.15432904 0.2868926
65 0.112046885 0.1417486 1 0.112046885 0.15637411 0.2921039
66 0.141569684 0.1417486 1 0.141569684 0.15793097 0.2979247
67 0.171092482 0.1417486 1 0.171092482 0.15952338 0.3022751
68 0.200615281 0.1417486 1 0.200615281 0.15997047 0.3048768
69 0.230138080 0.1417486 1 0.230138080 0.16327957 0.3087545
70 0.259660879 0.1417486 1 0.259660879 0.16372900 0.3125599
71 0.289183678 0.1417486 1 0.289183678 0.16395417 0.3185642
72 0.318706477 0.1417486 1 0.318706477 0.16414444 0.3240570
73 0.348229276 0.1417486 1 0.348229276 0.16570600 0.3273931
74 0.377752075 0.1417486 1 0.377752075 0.16556032 0.3316680
75 0.407274873 0.1417486 1 0.407274873 0.16815162 0.3391713
76 0.436797672 0.1417486 1 0.436797672 0.16817144 0.3465403
77 0.466320471 0.1417486 1 0.466320471 0.16790241 0.3514764
78 0.495843270 0.1417486 1 0.495843270 0.16941330 0.3590708
79 0.525366069 0.1417486 1 0.525366069 0.17068468 0.3662851
80 0.554888868 0.1417486 1 0.554888868 0.17238535 0.3738123
81 0.584411667 0.1417486 1 0.584411667 0.17358253 0.3796033
82 0.613934466 0.1417486 1 0.613934466 0.17521059 0.3869863
83 0.643457264 0.1417486 1 0.643457264 0.17617046 0.3939509
84 0.672980063 0.1417486 1 0.672980063 0.17710931 0.3967577
85 0.702502862 0.1417486 1 0.702502862 0.17816611 0.4026686
86 0.732025661 0.1417486 1 0.732025661 0.17998354 0.4094216
87 0.761548460 0.1417486 1 0.761548460 0.18085939 0.4165644
88 0.791071259 0.1417486 1 0.791071259 0.18114271 0.4198687
89 0.820594058 0.1417486 1 0.820594058 0.18294576 0.4255245
90 0.850116857 0.1417486 1 0.850116857 0.18446785 0.4333511
91 0.879639655 0.1417486 1 0.879639655 0.18498697 0.4407155
92 0.909162454 0.1417486 1 0.909162454 0.18729221 0.4472631
93 0.938685253 0.1417486 1 0.938685253 0.18952720 0.4529227
94 0.968208052 0.1417486 1 0.968208052 0.19203126 0.4579841
95 0.997730851 0.1417486 1 0.997730851 0.19408999 0.4671136
96 1.027253650 0.1417486 1 1.027253650 0.19551024 0.4751111
97 1.056776449 0.1417486 1 1.056776449 0.19700981 0.4804208
98 1.086299247 0.1417486 1 1.086299247 0.19756573 0.4850098
99 1.115822046 0.1417486 1 1.115822046 0.20044626 0.4915511
100 1.145344845 0.1417486 1 1.145344845 0.20250046 0.4996890
lower__ upper__
1 -1.0567858 1.1982199
2 -1.0438136 1.1831539
3 -1.0228641 1.1707170
4 -1.0072313 1.1596104
5 -0.9864567 1.1438521
6 -0.9689320 1.1282532
7 -0.9505741 1.1173943
8 -0.9357609 1.0983966
9 -0.9230198 1.0859565
10 -0.9104617 1.0757511
11 -0.8874429 1.0631791
12 -0.8687644 1.0467475
13 -0.8513190 1.0348922
14 -0.8290140 1.0236083
15 -0.8126063 1.0166800
16 -0.7975146 1.0011153
17 -0.7869631 0.9873863
18 -0.7760327 0.9721754
19 -0.7551183 0.9585837
20 -0.7427828 0.9479480
21 -0.7269582 0.9405559
22 -0.7072756 0.9284436
23 -0.6975987 0.9161489
24 -0.6884648 0.9040642
25 -0.6684576 0.8923201
26 -0.6535668 0.8811996
27 -0.6517693 0.8714208
28 -0.6394743 0.8652541
29 -0.6235719 0.8542377
30 -0.6127188 0.8433206
31 -0.6017256 0.8346912
32 -0.5845027 0.8192662
33 -0.5701008 0.8098853
34 -0.5596900 0.7982326
35 -0.5473666 0.7980605
36 -0.5340069 0.7908127
37 -0.5239994 0.7826979
38 -0.5124559 0.7811926
39 -0.4986325 0.7786670
40 -0.5044564 0.7745791
41 -0.4940340 0.7699341
42 -0.4871297 0.7698303
43 -0.4808839 0.7678166
44 -0.4790951 0.7662335
45 -0.4711604 0.7576184
46 -0.4690302 0.7577330
47 -0.4675442 0.7567887
48 -0.4673520 0.7554134
49 -0.4649256 0.7499373
50 -0.4600178 0.7494690
51 -0.4500426 0.7500552
52 -0.4475863 0.7505488
53 -0.4437339 0.7513191
54 -0.4429276 0.7564214
55 -0.4427087 0.7578937
56 -0.4451014 0.7613821
57 -0.4418548 0.7706546
58 -0.4377409 0.7787030
59 -0.4397108 0.7882644
60 -0.4462651 0.8026011
61 -0.4538979 0.8069187
62 -0.4542826 0.8163290
63 -0.4557042 0.8285206
64 -0.4572005 0.8335650
65 -0.4638491 0.8413812
66 -0.4681885 0.8539095
67 -0.4775714 0.8633141
68 -0.4888333 0.8698490
69 -0.4952363 0.8791527
70 -0.4975383 0.8833882
71 -0.5088667 0.8863114
72 -0.5197474 0.8951534
73 -0.5316745 0.9085101
74 -0.5409388 0.9207023
75 -0.5572803 0.9282691
76 -0.5643576 0.9357900
77 -0.5751774 0.9517092
78 -0.5855919 0.9625510
79 -0.5995727 0.9781417
80 -0.6115650 0.9946185
81 -0.6198287 1.0071916
82 -0.6297608 1.0208370
83 -0.6447637 1.0357034
84 -0.6511860 1.0506364
85 -0.6659993 1.0608813
86 -0.6794852 1.0702993
87 -0.6893830 1.0801824
88 -0.7040491 1.1026626
89 -0.7183266 1.1196308
90 -0.7387399 1.1401544
91 -0.7541057 1.1561184
92 -0.7608552 1.1701851
93 -0.7783620 1.1855296
94 -0.7920760 1.2014060
95 -0.8063188 1.2157463
96 -0.8224106 1.2307841
97 -0.8377605 1.2484814
98 -0.8530954 1.2580503
99 -0.8684646 1.2731355
100 -0.8840083 1.2891893
Here is the equivalent code using stanfit:
library(rstan)
N <- 10
y <- rnorm(10)
x <- rnorm(10)
df <- data.frame(x, y)
fit <- stan('stan_test.stan', data = list(y = y, x = x, N = N))
print(fit)
Which yields the output:
Inference for Stan model: stan_test.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
alpha -0.35 0.01 0.43 -1.23 -0.62 -0.35 -0.09 0.50 2185 1
beta -0.26 0.01 0.57 -1.41 -0.60 -0.25 0.08 0.86 2075 1
sigma 1.26 0.01 0.41 0.74 0.99 1.17 1.43 2.27 1824 1
lp__ -6.19 0.04 1.50 -10.18 -6.87 -5.79 -5.07 -4.48 1282 1
Samples were drawn using NUTS(diag_e) at Fri Jun 03 10:08:50 2022.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
Is it possible to obtain the estimate__ column using this stanfit?
Thank you very much !
- Operating System: Windows 10
- brms Version: 2.17.0