Ordinales Modell

Modellübersicht

##  Family: cumulative 
##   Links: mu = probit; disc = log 
## Formula: trust ~ 1 + (1 | nuts2) 
##          disc ~ 1 + (1 | nuts2)
##    Data: cee12 (Number of observations: 7335) 
##   Draws: 3 chains, each with iter = 84000; warmup = 6000; thin = 6;
##          total post-warmup draws = 39000
## 
## Group-Level Effects: 
## ~nuts2 (Number of levels: 26) 
##                    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)          0.51      0.17     0.25     0.92 1.00    23815    30328
## sd(disc_Intercept)     0.10      0.02     0.07     0.14 1.00    33492    36063
## 
## Population-Level Effects: 
##                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept[1]      -3.26      0.94    -5.41    -1.75 1.00    22288    29556
## Intercept[2]      -1.88      0.55    -3.14    -1.00 1.00    22324    29777
## Intercept[3]      -0.83      0.26    -1.44    -0.42 1.00    22620    30061
## Intercept[4]       0.14      0.12    -0.06     0.39 1.00    25148    32610
## Intercept[5]       0.97      0.29     0.50     1.64 1.00    22317    30480
## Intercept[6]       1.86      0.54     0.99     3.10 1.00    22303    29821
## Intercept[7]       2.72      0.78     1.45     4.51 1.00    22315    29970
## Intercept[8]       3.64      1.04     1.95     6.01 1.00    22360    29440
## Intercept[9]       4.68      1.33     2.51     7.74 1.00    22323    29483
## Intercept[10]      5.68      1.62     3.05     9.38 1.00    22314    29886
## disc_Intercept    -0.65      0.29    -1.19    -0.07 1.00    22296    28497
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Trace-Plots

Trace-Plot

Autokorrelation-Plots

Autokorrelation-Plot

Posterior-Verteilung

Posterior-Plot

Ordinales Nullmodell

Modellübersicht

##  Family: cumulative 
##   Links: mu = probit; disc = log 
## Formula: trust ~ 1 
##          disc ~ 1
##    Data: cee12 (Number of observations: 7335) 
##   Draws: 3 chains, each with iter = 84000; warmup = 6000; thin = 6;
##          total post-warmup draws = 39000
## 
## Population-Level Effects: 
##                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept[1]      -3.04      0.88    -5.04    -1.61 1.00    13228    15733
## Intercept[2]      -1.74      0.51    -2.89    -0.92 1.00    13237    15871
## Intercept[3]      -0.75      0.22    -1.25    -0.40 1.00    13323    16040
## Intercept[4]       0.17      0.06     0.08     0.30 1.00    16168    22694
## Intercept[5]       0.94      0.27     0.50     1.56 1.00    13390    16090
## Intercept[6]       1.76      0.51     0.93     2.92 1.00    13296    15660
## Intercept[7]       2.54      0.74     1.35     4.20 1.00    13268    15870
## Intercept[8]       3.36      0.97     1.78     5.56 1.00    13248    15594
## Intercept[9]       4.28      1.24     2.27     7.10 1.00    13251    15808
## Intercept[10]      5.18      1.50     2.74     8.58 1.00    13238    16068
## disc_Intercept    -0.62      0.29    -1.17    -0.03 1.00    13229    15566
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Trace-Plots

Trace-Plot

Autokorrelation-Plots

Autokorrelation-Plot

Posterior-Verteilung

Posterior-Plot

Metrisches Modell

Modellübersicht

##  Family: student 
##   Links: mu = identity; sigma = log; nu = identity 
## Formula: trust ~ 1 + (1 | nuts2) 
##          sigma ~ 1 + (1 | nuts2)
##    Data: cee12 (Number of observations: 7335) 
##   Draws: 3 chains, each with iter = 66000; warmup = 6000; thin = 6;
##          total post-warmup draws = 30000
## 
## Group-Level Effects: 
## ~nuts2 (Number of levels: 26) 
##                     Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept)           0.53      0.08     0.39     0.73 1.00    22580    27499
## sd(sigma_Intercept)     0.10      0.02     0.07     0.14 1.00    26031    28490
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           3.52      0.11     3.30     3.73 1.00    16389    23517
## sigma_Intercept     0.70      0.02     0.66     0.74 1.00    25772    27610
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   152.16     49.40    77.26   268.50 1.00    29984    28652
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Trace-Plots

Trace-Plot

Autokorrelation-Plots

Autokorrelation-Plot

Posterior-Verteilung

Posterior-Plot

Metrisches Nullmodell

Modellübersicht

##  Family: student 
##   Links: mu = identity; sigma = log; nu = identity 
## Formula: trust ~ 1 
##          sigma ~ 1
##    Data: cee12 (Number of observations: 7335) 
##   Draws: 3 chains, each with iter = 11500; warmup = 1500; thin = 1;
##          total post-warmup draws = 30000
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           3.52      0.02     3.47     3.56 1.00    22007    18445
## sigma_Intercept     0.74      0.01     0.73     0.76 1.00    21834    17925
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   158.91     50.43    82.14   278.22 1.00    20998    18408
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Trace-Plots

Trace-Plot

Autokorrelation-Plots

Autokorrelation-Plot

Posterior-Verteilung

Posterior-Plot

Copyright:
Benedikt Philipp Kleer, 2022 (Online-Appendix zur Promotion, eingereicht am 14. September 2022, Fachbereich 03, Justus-Liebig-Universität Gießen).