get_parameters() and find_parameters() should indicate fixed and random effects for `BayesFactor::anovaBF` #169

IndrajeetPatil · 2019-12-17T14:06:06Z

library(BayesFactor)
#> Loading required package: coda
#> Loading required package: Matrix
#> ************
#> Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey ([email protected]).
#> 
#> Type BFManual() to open the manual.
#> ************
library(parameters)

model <- anovaBF(extra ~ group + ID, data = sleep, whichRandom = "ID",
        progress = FALSE)

model_parameters(model) 
#> Error in UseMethod("point_estimate"): no applicable method for 'point_estimate' applied to an object of class "NULL"

^{Created on 2019-12-17 by the reprex package (v0.3.0)}

Traceback:

> traceback()
8: point_estimate(insight::get_parameters(x), centrality = centrality, 
       dispersion = dispersion, ...)
7: point_estimate.BFBayesFactor(x, centrality = centrality, dispersion = dispersion, 
       ...)
6: point_estimate(x, centrality = centrality, dispersion = dispersion, 
       ...)
5: .describe_posterior(posteriors, centrality = centrality, dispersion = dispersion, 
       ci = ci, ci_method = ci_method, test = test, rope_range = rope_range, 
       rope_ci = rope_ci, ...)
4: describe_posterior.BFBayesFactor(model, centrality = centrality, 
       dispersion = dispersion, ci = ci, ci_method = ci_method, 
       test = test, rope_range = rope_range, rope_ci = rope_ci, 
       priors = priors, ...)
3: bayestestR::describe_posterior(model, centrality = centrality, 
       dispersion = dispersion, ci = ci, ci_method = ci_method, 
       test = test, rope_range = rope_range, rope_ci = rope_ci, 
       priors = priors, ...)
2: model_parameters.BFBayesFactor(model)
1: model_parameters(model)

Session info

devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                       
#>  version  R version 3.6.2 (2019-12-12)
#>  os       macOS Mojave 10.14.6        
#>  system   x86_64, darwin15.6.0        
#>  ui       X11                         
#>  language (EN)                        
#>  collate  en_US.UTF-8                 
#>  ctype    en_US.UTF-8                 
#>  tz       Europe/Berlin               
#>  date     2019-12-17                  
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package      * version    date       lib source                               
#>  assertthat     0.2.1      2019-03-21 [1] CRAN (R 3.6.0)                       
#>  backports      1.1.5      2019-10-02 [1] CRAN (R 3.6.0)                       
#>  BayesFactor  * 0.9.12-4.2 2018-05-19 [1] CRAN (R 3.6.0)                       
#>  bayestestR     0.4.9      2019-12-17 [1] Github (easystats/bayestestR@392c78c)
#>  callr          3.4.0      2019-12-09 [1] CRAN (R 3.6.0)                       
#>  cli            2.0.0      2019-12-09 [1] CRAN (R 3.6.0)                       
#>  coda         * 0.19-3     2019-07-05 [1] CRAN (R 3.6.0)                       
#>  crayon         1.3.4      2017-09-16 [1] CRAN (R 3.6.0)                       
#>  desc           1.2.0      2018-05-01 [1] CRAN (R 3.6.0)                       
#>  devtools       2.2.1      2019-09-24 [1] CRAN (R 3.6.0)                       
#>  digest         0.6.23     2019-11-23 [1] CRAN (R 3.6.0)                       
#>  ellipsis       0.3.0      2019-09-20 [1] CRAN (R 3.6.0)                       
#>  evaluate       0.14       2019-05-28 [1] CRAN (R 3.6.0)                       
#>  fansi          0.4.0      2018-10-05 [1] CRAN (R 3.6.0)                       
#>  fs             1.3.1      2019-05-06 [1] CRAN (R 3.6.0)                       
#>  glue           1.3.1      2019-03-12 [1] CRAN (R 3.6.0)                       
#>  gtools         3.8.1      2018-06-26 [1] CRAN (R 3.6.0)                       
#>  highr          0.8        2019-03-20 [1] CRAN (R 3.6.0)                       
#>  htmltools      0.4.0      2019-10-04 [1] CRAN (R 3.6.0)                       
#>  insight        0.7.1.1    2019-12-17 [1] Github (easystats/insight@5be7b4d)   
#>  knitr          1.26       2019-11-12 [1] CRAN (R 3.6.0)                       
#>  lattice        0.20-38    2018-11-04 [2] CRAN (R 3.6.2)                       
#>  magrittr       1.5        2014-11-22 [1] CRAN (R 3.6.0)                       
#>  Matrix       * 1.2-18     2019-11-27 [2] CRAN (R 3.6.2)                       
#>  MatrixModels   0.4-1      2015-08-22 [1] CRAN (R 3.6.0)                       
#>  memoise        1.1.0      2017-04-21 [1] CRAN (R 3.6.0)                       
#>  mvtnorm        1.0-11     2019-06-19 [1] CRAN (R 3.6.0)                       
#>  parameters   * 0.3.0.1    2019-12-17 [1] Github (easystats/parameters@29d23a4)
#>  pbapply        1.4-2      2019-08-31 [1] CRAN (R 3.6.0)                       
#>  pkgbuild       1.0.6      2019-10-09 [1] CRAN (R 3.6.0)                       
#>  pkgload        1.0.2      2018-10-29 [1] CRAN (R 3.6.0)                       
#>  prettyunits    1.0.2      2015-07-13 [1] CRAN (R 3.6.0)                       
#>  processx       3.4.1      2019-07-18 [1] CRAN (R 3.6.0)                       
#>  ps             1.3.0      2018-12-21 [1] CRAN (R 3.6.0)                       
#>  R6             2.4.1      2019-11-12 [1] CRAN (R 3.6.0)                       
#>  Rcpp           1.0.3      2019-11-08 [1] CRAN (R 3.6.1)                       
#>  remotes        2.1.0      2019-06-24 [1] CRAN (R 3.6.0)                       
#>  rlang          0.4.2.9000 2019-12-16 [1] Github (r-lib/rlang@ec7c1ed)         
#>  rmarkdown      2.0        2019-12-12 [1] CRAN (R 3.6.0)                       
#>  rprojroot      1.3-2      2018-01-03 [1] CRAN (R 3.6.0)                       
#>  sessioninfo    1.1.1      2018-11-05 [1] CRAN (R 3.6.0)                       
#>  stringi        1.4.3      2019-03-12 [1] CRAN (R 3.6.0)                       
#>  stringr        1.4.0      2019-02-10 [1] CRAN (R 3.6.0)                       
#>  testthat       2.3.1      2019-12-01 [1] CRAN (R 3.6.0)                       
#>  usethis        1.5.1      2019-07-04 [1] CRAN (R 3.6.0)                       
#>  withr          2.1.2      2018-03-15 [1] CRAN (R 3.6.0)                       
#>  xfun           0.11       2019-11-12 [1] CRAN (R 3.6.0)                       
#>  yaml           2.2.0      2018-07-25 [1] CRAN (R 3.6.0)                       
#> 
#> [1] /Users/patil/Library/R/3.6/library
#> [2] /Library/Frameworks/R.framework/Versions/3.6/Resources/library

strengejacke · 2019-12-17T14:29:24Z

This errors due to insight::get_parameters(). What kind of parameters would make sense in this case?

library(insight)
library(BayesFactor)
#> Loading required package: coda
#> Loading required package: Matrix
#> ************
#> Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey ([email protected]).
#> 
#> Type BFManual() to open the manual.
#> ************

model <- anovaBF(extra ~ group + ID, data = sleep, whichRandom = "ID", progress = FALSE)

head(get_parameters(model))
#>         mu    group-1   group-2        ID-1       ID-2       ID-3      ID-4
#> 1 1.349286 -0.6724627 0.6724627  0.05559608 -2.1598342 -0.6760759 -1.047968
#> 2 1.692076 -0.7261666 0.7261666  0.04946193 -1.3462277 -1.2668723 -1.251121
#> 3 2.110250 -0.8819996 0.8819996 -0.12487609 -0.7093962 -1.1001693 -2.061756
#> 4 1.656555 -0.8587893 0.8587893  0.79552914 -1.1983304 -0.1355185 -1.472216
#> 5 1.895765 -0.4498286 0.4498286  0.95904814 -1.9519772 -0.9481232 -1.507108
#> 6 1.631278 -0.8018071 0.8018071  0.21136784 -1.0153058 -1.1680062 -1.009764
#>         ID-5     ID-6     ID-7       ID-8        ID-9       ID-10      sig2
#> 1 -0.7875713 1.568064 1.651395  0.3938788  1.65393017  1.15372957 0.9994526
#> 2 -0.7433016 2.044083 1.709569 -0.4861043  0.92190880  0.48956802 1.3592481
#> 3 -2.1731418 1.148342 1.158459 -0.2194751  0.48845869 -0.02081864 0.9389592
#> 4 -1.4777671 1.743779 1.229966 -0.5381937  0.32094642  1.06300767 0.8146430
#> 5 -1.6224734 1.504265 2.105043 -0.8283088 -0.03518938  1.02143941 0.9147814
#> 6 -1.4372970 2.115739 3.690448 -1.1648361 -0.06883875  0.99407781 1.0693874
#>     g_group      g_ID
#> 1 0.5444569 0.9252051
#> 2 0.8492134 1.3423385
#> 3 6.6524189 1.0730355
#> 4 0.7506118 1.1278043
#> 5 0.8147826 2.4118328
#> 6 1.8295035 2.0901768

find_parameters(model)
#> $conditional
#>  [1] "mu"      "group-1" "group-2" "ID-1"    "ID-2"    "ID-3"    "ID-4"   
#>  [8] "ID-5"    "ID-6"    "ID-7"    "ID-8"    "ID-9"    "ID-10"   "sig2"   
#> [15] "g_group" "g_ID"

^{Created on 2019-12-17 by the reprex package (v0.3.0)}

mattansb · 2019-12-17T16:18:30Z

@strengejacke note that BayesFactor::posterior needs index passed to it when there are more than one model (this is also true for directional ttests) - you might want to make this explicit in the documentation somehow?

library(insight)
library(BayesFactor)
#> Loading required package: coda
#> Loading required package: Matrix
#> ************
#> Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey ([email protected]).
#> 
#> Type BFManual() to open the manual.
#> ************
data("puzzles")

result <- anovaBF(
  RT ~ shape * color + ID,
  data = puzzles,
  whichRandom = "ID",
  progress = FALSE
)

ttest_res <-
  ttestBF(
    x = sleep$extra[sleep$group == 1],
    y = sleep$extra[sleep$group == 2],
    paired = TRUE,
    nullInterval = c(-Inf, 0)
  )

posterior(result, iterations = 10)
#> Error in posterior(result, iterations = 10): Index argument required for posterior with multiple numerators.
posterior(ttest_res, iterations = 10)
#> Error in posterior(ttest_res, iterations = 10): Index argument required for posterior with multiple numerators.

^{Created on 2019-12-17 by the reprex package (v0.3.0)}

Also, if you wan find_parameters() to separate the parameters into random, then you can find the random factors in each model:

result@numerator$`shape + ID`@dataTypes
#>       ID    shape 
#> "random"  "fixed"

(But this is a whole mess on-to itself...)

strengejacke · 2019-12-17T16:29:28Z

Also, if you wan find_parameters() to separate the parameters into random, then you can find the random factors in each model:

Would be nice, and I can even use insight::find_formula(result) to get fixed and random effects - but then I need to reconstruct the column names for the parameters... 😱

strengejacke · 2019-12-17T16:31:49Z

I think it's now mainly an insight issue, as it works with model_parameters() now.

Now we have to extract information on which parameter is random, and which is fixed, and make find_parameters() and get_parameters()´ work with the effects`-argument.

mattansb · 2019-12-17T17:43:11Z

I've been down that rabbit hole... Once you open that door...
Good luck!

strengejacke · 2019-12-18T09:57:40Z

library(insight)
library(parameters)
#> 
#> Attaching package: 'parameters'
#> The following object is masked from 'package:insight':
#> 
#>     format_ci
library(BayesFactor)
#> Loading required package: coda
#> Loading required package: Matrix
#> ************
#> Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey ([email protected]).
#> 
#> Type BFManual() to open the manual.
#> ************

model <- anovaBF(extra ~ group + ID, data = sleep, whichRandom = "ID", progress = FALSE)

head(get_parameters(model))
#>         mu    group-1   group-2       ID-1      ID-2       ID-3       ID-4
#> 1 1.388413 -0.9373772 0.9373772 -0.3358237 -1.249506 -0.6663588 -0.1712864
#> 2 2.092704 -1.0004152 1.0004152 -0.4030889 -0.802145 -2.0117398 -2.2269061
#> 3 1.862832 -0.8652697 0.8652697 -1.4377261 -1.754347 -1.4568715 -1.8716563
#> 4 1.784898 -0.8077692 0.8077692 -0.2117907 -2.042515 -0.6654858 -2.3811870
#> 5 2.685579 -0.7216999 0.7216999 -0.8455328 -2.992526 -1.7699278 -3.3533533
#> 6 1.304258 -0.9785429 0.9785429  0.1042850 -2.167270 -1.0909156 -0.9915559
#>         ID-5      ID-6     ID-7       ID-8        ID-9     ID-10      sig2
#> 1 -0.7255611 1.8931277 1.899339  0.2131550  0.56946258 1.5719684 0.9627126
#> 2 -2.2192400 0.9967290 2.697733 -1.7158945  0.48580661 0.6370042 1.1319091
#> 3 -1.8016265 0.4559148 2.257765 -1.0178970 -0.03012636 1.3148016 0.6901860
#> 4 -1.6334668 1.6687262 2.523719 -0.6269279  0.56978852 1.4394503 0.7274266
#> 5 -2.7867715 1.8316788 2.167131  0.3504694  0.26296890 0.9842533 0.7864430
#> 6 -0.7278862 1.9251050 2.924684 -0.2444156  1.68773453 0.9569678 0.6179961
#>     g_group     g_ID
#> 1 0.5071210 1.526206
#> 2 5.5938528 2.530137
#> 3 1.6020113 2.011088
#> 4 0.2738577 5.318503
#> 5 0.7436001 7.866674
#> 6 1.9593037 2.744540
find_parameters(model)
#> $conditional
#> [1] "group-1" "group-2"
#> 
#> $random
#>  [1] "ID-1"  "ID-2"  "ID-3"  "ID-4"  "ID-5"  "ID-6"  "ID-7"  "ID-8"  "ID-9" 
#> [10] "ID-10"
#> 
#> $extra
#> [1] "mu"      "sig2"    "g_group" "g_ID"

model_parameters(model)
#> # Fixed Effects (Count Model) 
#> 
#> Parameter | Median |         89% CI |     pd | % in ROPE |    BF
#> ----------------------------------------------------------------
#> group-1   |  -0.67 | [-1.06, -0.28] | 99.30% |        0% | 11.73
#> group-2   |   0.67 | [ 0.28,  1.06] | 99.30% |        0% | 11.73
#> 
#> # Extra Parameters 
#> 
#> Parameter | Median |       89% CI |     pd | % in ROPE |    BF
#> --------------------------------------------------------------
#> mu        |   1.55 | [0.62, 2.44] | 99.28% |        0% | 11.73
#> sig2      |   1.00 | [0.34, 1.98] |   100% |        0% | 11.73
#> g_group   |   0.84 | [0.02, 5.73] |   100% |     3.17% | 11.73
#> g_ID      |   2.61 | [0.16, 6.71] |   100% |        0% | 11.73
#> 
#> # Random Effects (Count Model) 
#> 
#> Parameter | Median |         89% CI |     pd | % in ROPE |    BF
#> ----------------------------------------------------------------
#> ID-1      |  -0.21 | [-1.58,  1.07] | 59.33% |    11.60% | 11.73
#> ID-2      |  -1.54 | [-2.90, -0.21] | 96.28% |        0% | 11.73
#> ID-3      |  -0.88 | [-2.25,  0.46] | 86.02% |     6.57% | 11.73
#> ID-4      |  -1.68 | [-3.05, -0.30] | 97.40% |        0% | 11.73
#> ID-5      |  -1.33 | [-2.65,  0.01] | 94.55% |     2.81% | 11.73
#> ID-6      |   1.87 | [ 0.39,  3.17] | 98.28% |        0% | 11.73
#> ID-7      |   2.45 | [ 0.91,  3.80] | 99.48% |        0% | 11.73
#> ID-8      |  -0.29 | [-1.58,  1.03] | 64.05% |    11.06% | 11.73
#> ID-9      |   0.61 | [-0.69,  1.91] | 78.62% |     7.78% | 11.73
#> ID-10     |   0.90 | [-0.38,  2.31] | 87.02% |     5.48% | 11.73

^{Created on 2019-12-18 by the reprex package (v0.3.0)}

IndrajeetPatil · 2019-12-18T09:59:34Z

Awesome, thanks for fixing this so quickly!

strengejacke referenced this issue Dec 17, 2019

https://github.com/easystats/parameters/issues/170

a1a1e98

strengejacke transferred this issue from easystats/parameters Dec 17, 2019

strengejacke changed the title ~~model_parameters doesn't work with BayesFactor::anovaBF~~ get_parameters() and find_parameters() should indicate fixed and random effects for BayesFactor::anovaBF Dec 17, 2019

strengejacke closed this as completed in 4c68755 Dec 18, 2019

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

get_parameters() and find_parameters() should indicate fixed and random effects for `BayesFactor::anovaBF` #169

get_parameters() and find_parameters() should indicate fixed and random effects for `BayesFactor::anovaBF` #169

IndrajeetPatil commented Dec 17, 2019

strengejacke commented Dec 17, 2019

mattansb commented Dec 17, 2019

strengejacke commented Dec 17, 2019

strengejacke commented Dec 17, 2019

mattansb commented Dec 17, 2019

strengejacke commented Dec 18, 2019

IndrajeetPatil commented Dec 18, 2019

get_parameters() and find_parameters() should indicate fixed and random effects for BayesFactor::anovaBF #169

get_parameters() and find_parameters() should indicate fixed and random effects for BayesFactor::anovaBF #169

Comments

IndrajeetPatil commented Dec 17, 2019

strengejacke commented Dec 17, 2019

mattansb commented Dec 17, 2019

strengejacke commented Dec 17, 2019

strengejacke commented Dec 17, 2019

mattansb commented Dec 17, 2019

strengejacke commented Dec 18, 2019

IndrajeetPatil commented Dec 18, 2019

get_parameters() and find_parameters() should indicate fixed and random effects for `BayesFactor::anovaBF` #169

get_parameters() and find_parameters() should indicate fixed and random effects for `BayesFactor::anovaBF` #169