Ordinales Modell

Modellübersicht

##  Family: cumulative 
##   Links: mu = probit; disc = log 
## Formula: trust ~ 1 + (1 | nuts2) 
##          disc ~ 1 + (1 | nuts2)
##    Data: cee18 (Number of observations: 6342) 
##   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)          0.55      0.18     0.28     0.98 1.00    24215    31540
## sd(disc_Intercept)     0.15      0.03     0.11     0.21 1.00    30292    33695
## 
## Population-Level Effects: 
##                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept[1]      -3.94      1.12    -6.48    -2.14 1.00    23266    31526
## Intercept[2]      -2.69      0.77    -4.44    -1.45 1.00    23271    31548
## Intercept[3]      -1.72      0.50    -2.86    -0.92 1.00    23202    30806
## Intercept[4]      -0.82      0.26    -1.43    -0.41 1.00    23426    30806
## Intercept[5]       0.06      0.12    -0.17     0.30 1.00    27848    33893
## Intercept[6]       1.07      0.32     0.56     1.81 1.00    24231    32502
## Intercept[7]       2.06      0.59     1.11     3.40 1.00    23722    32362
## Intercept[8]       3.19      0.90     1.74     5.25 1.00    23591    31930
## Intercept[9]       4.32      1.22     2.35     7.10 1.00    23568    32187
## Intercept[10]      5.51      1.56     3.00     9.05 1.00    23591    31935
## disc_Intercept    -0.69      0.28    -1.22    -0.11 1.00    23736    31431
## 
## 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: cee18 (Number of observations: 6342) 
##   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]      -3.62      1.05    -6.03    -1.92 1.00    12457    14197
## Intercept[2]      -2.51      0.73    -4.18    -1.33 1.00    12445    14327
## Intercept[3]      -1.63      0.47    -2.71    -0.86 1.00    12454    14506
## Intercept[4]      -0.79      0.23    -1.33    -0.42 1.00    12564    14805
## Intercept[5]       0.03      0.03    -0.03     0.10 1.00    36706    34095
## Intercept[6]       0.99      0.29     0.52     1.64 1.00    12561    14670
## Intercept[7]       1.91      0.55     1.01     3.17 1.00    12465    14284
## Intercept[8]       2.95      0.85     1.57     4.91 1.00    12449    14327
## Intercept[9]       3.96      1.14     2.10     6.58 1.00    12461    14304
## Intercept[10]      5.00      1.45     2.65     8.32 1.00    12458    14259
## disc_Intercept    -0.66      0.29    -1.21    -0.06 1.00    12440    14385
## 
## 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: cee18 (Number of observations: 6342) 
##   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)           0.56      0.09     0.41     0.76 1.00    21706    25948
## sd(sigma_Intercept)     0.12      0.02     0.08     0.17 1.00    24821    27933
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           4.36      0.11     4.13     4.58 1.00    16824    22805
## sigma_Intercept     0.72      0.03     0.67     0.77 1.00    22297    26651
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   164.54     51.83    85.73   287.22 1.00    29912    28970
## 
## 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: cee18 (Number of observations: 6342) 
##   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           4.38      0.03     4.33     4.44 1.00    22496    18324
## sigma_Intercept     0.77      0.01     0.75     0.79 1.00    21795    18345
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   177.46     53.17    94.91   301.80 1.00    19507    18243
## 
## 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).