Dear all,
I can’t get my head around this.
I fitted a cumulative-link mixed model with factors situation
(levels: Internist, Surgeon), disfigurement
(levels: None, Strabism, Acne, Piercing, Tattoo) and face
(levels: Man_Average, Woman_Average, Man_Attractive, Woman_Attractive) effect coded (-1, 0, 1) with interactions between all factors. The answer is a likert scale with 11 items (numbers 0-10), hence 10 thresholds.
summary(model)
#> Loading required package: readr
#> Family: cumulative
#> Links: mu = logit; disc = identity
#> Formula: answer ~ 1 + situation * face * disfigurement + (1 | participant_id)
#> Data: original_data (Number of observations: 16036)
#> Samples: 4 chains, each with iter = 5000; warmup = 2500; thin = 1;
#> total post-warmup samples = 10000
#>
#> Group-Level Effects:
#> ~participant_id (Number of levels: 4009)
#> Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
#> sd(Intercept) 1.39 0.03 1.33 1.44 3058 1.00
#>
#> Population-Level Effects:
#> Estimate Est.Error l-95% CI u-95% CI
#> Intercept[1] -3.07 0.04 -3.15 -2.99
#> Intercept[2] -2.26 0.04 -2.33 -2.19
#> Intercept[3] -1.50 0.03 -1.56 -1.44
#> Intercept[4] -0.77 0.03 -0.83 -0.71
#> Intercept[5] -0.15 0.03 -0.21 -0.09
#> Intercept[6] 0.57 0.03 0.51 0.63
#> Intercept[7] 1.22 0.03 1.16 1.28
#> Intercept[8] 1.98 0.03 1.92 2.05
#> Intercept[9] 3.02 0.04 2.94 3.10
#> Intercept[10] 3.88 0.05 3.78 3.97
#> situation1 -0.23 0.03 -0.28 -0.17
#> face1 -0.75 0.03 -0.80 -0.70
#> face2 -0.08 0.03 -0.13 -0.03
#> face3 0.05 0.03 0.00 0.10
#> disfigurement1 0.68 0.05 0.58 0.78
#> disfigurement2 -0.66 0.03 -0.72 -0.60
#> disfigurement3 -0.54 0.03 -0.60 -0.48
#> disfigurement4 0.52 0.03 0.46 0.58
#> situation1:face1 0.10 0.02 0.05 0.15
#> situation1:face2 -0.01 0.02 -0.06 0.03
#> situation1:face3 -0.04 0.03 -0.09 0.00
#> situation1:disfigurement1 0.00 0.04 -0.07 0.08
#> situation1:disfigurement2 -0.37 0.03 -0.42 -0.31
#> situation1:disfigurement3 0.35 0.03 0.29 0.40
#> situation1:disfigurement4 0.00 0.02 -0.04 0.05
#> face1:disfigurement1 -0.19 0.05 -0.28 -0.09
#> face2:disfigurement1 -0.01 0.04 -0.09 0.06
#> face3:disfigurement1 -0.17 0.05 -0.27 -0.08
#> face1:disfigurement2 0.35 0.07 0.22 0.49
#> face2:disfigurement2 -0.24 0.07 -0.37 -0.11
#> face3:disfigurement2 0.20 0.07 0.06 0.33
#> face1:disfigurement3 0.13 0.07 0.00 0.26
#> face2:disfigurement3 0.46 0.07 0.33 0.59
#> face3:disfigurement3 0.05 0.06 -0.05 0.17
#> face1:disfigurement4 -0.17 0.07 -0.30 -0.03
#> face2:disfigurement4 0.05 0.06 -0.05 0.18
#> face3:disfigurement4 -0.15 0.07 -0.28 -0.01
#> situation1:face1:disfigurement1 -0.01 0.03 -0.08 0.06
#> situation1:face2:disfigurement1 0.00 0.03 -0.07 0.07
#> situation1:face3:disfigurement1 -0.02 0.04 -0.10 0.05
#> situation1:face1:disfigurement2 0.11 0.06 -0.00 0.23
#> situation1:face2:disfigurement2 0.03 0.05 -0.06 0.13
#> situation1:face3:disfigurement2 0.00 0.04 -0.08 0.09
#> situation1:face1:disfigurement3 -0.12 0.06 -0.25 -0.00
#> situation1:face2:disfigurement3 0.08 0.06 -0.01 0.20
#> situation1:face3:disfigurement3 0.01 0.04 -0.08 0.11
#> situation1:face1:disfigurement4 0.03 0.05 -0.05 0.14
#> situation1:face2:disfigurement4 -0.04 0.05 -0.15 0.05
#> situation1:face3:disfigurement4 -0.01 0.04 -0.11 0.07
#> Eff.Sample Rhat
#> Intercept[1] 5053 1.00
#> Intercept[2] 5241 1.00
#> Intercept[3] 5107 1.00
#> Intercept[4] 5012 1.00
#> Intercept[5] 5077 1.00
#> Intercept[6] 5321 1.00
#> Intercept[7] 5520 1.00
#> Intercept[8] 5834 1.00
#> Intercept[9] 6429 1.00
#> Intercept[10] 6855 1.00
#> situation1 4373 1.00
#> face1 8374 1.00
#> face2 8222 1.00
#> face3 6520 1.00
#> disfigurement1 4736 1.00
#> disfigurement2 8346 1.00
#> disfigurement3 7682 1.00
#> disfigurement4 8303 1.00
#> situation1:face1 8468 1.00
#> situation1:face2 10101 1.00
#> situation1:face3 7254 1.00
#> situation1:disfigurement1 5276 1.00
#> situation1:disfigurement2 9401 1.00
#> situation1:disfigurement3 8789 1.00
#> situation1:disfigurement4 9774 1.00
#> face1:disfigurement1 9936 1.00
#> face2:disfigurement1 12024 1.00
#> face3:disfigurement1 9456 1.00
#> face1:disfigurement2 4408 1.00
#> face2:disfigurement2 5068 1.00
#> face3:disfigurement2 4457 1.00
#> face1:disfigurement3 4173 1.00
#> face2:disfigurement3 4164 1.00
#> face3:disfigurement3 5064 1.00
#> face1:disfigurement4 4027 1.00
#> face2:disfigurement4 3905 1.00
#> face3:disfigurement4 3645 1.00
#> situation1:face1:disfigurement1 11025 1.00
#> situation1:face2:disfigurement1 11474 1.00
#> situation1:face3:disfigurement1 9777 1.00
#> situation1:face1:disfigurement2 3942 1.00
#> situation1:face2:disfigurement2 6539 1.00
#> situation1:face3:disfigurement2 7183 1.00
#> situation1:face1:disfigurement3 3990 1.00
#> situation1:face2:disfigurement3 3861 1.00
#> situation1:face3:disfigurement3 7052 1.00
#> situation1:face1:disfigurement4 5240 1.00
#> situation1:face2:disfigurement4 4725 1.00
#> situation1:face3:disfigurement4 5493 1.00
#>
#> Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
#> is a crude measure of effective sample size, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
Created on 2019-06-18 by the reprex package (v0.2.1)
Now I would like to calculate the grand mean but I don’t get the idea how it is done. Could someone please explain how I can achieve this?
Thank you very much in advance!
Kindest regards,
Pascal
Session info
devtools::session_info()
#> ─ Session info ──────────────────────────────────────────────────────────
#> setting value
#> version R version 3.5.1 (2018-07-02)
#> os CentOS Linux 7 (Core)
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Europe/Zurich
#> date 2019-06-18
#>
#> ─ Packages ──────────────────────────────────────────────────────────────
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#> source
Please also provide the following information in addition to your question:
- Operating System: CentOS 7.0
- brms Version: 2.9.0