Ordinales Modell

Modellübersicht

##  Family: cumulative 
##   Links: mu = probit; disc = log 
## Formula: stfgov ~ 1 + (1 | nuts2) 
##          disc ~ 1 + (1 | nuts2)
##    Data: cee18 (Number of observations: 6416) 
##   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)          0.67      0.22     0.33     1.19 1.00    18244    27237
## sd(disc_Intercept)     0.15      0.03     0.10     0.20 1.00    30529    34508
## 
## Population-Level Effects: 
##                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept[1]      -3.82      1.09    -6.31    -2.05 1.00    16142    24729
## Intercept[2]      -2.93      0.84    -4.86    -1.57 1.00    15998    24977
## Intercept[3]      -1.88      0.55    -3.14    -0.99 1.00    15913    25245
## Intercept[4]      -0.95      0.31    -1.66    -0.47 1.00    16398    26086
## Intercept[5]      -0.26      0.16    -0.63     0.01 1.00    20452    28746
## Intercept[6]       0.82      0.28     0.39     1.46 1.00    18016    26980
## Intercept[7]       1.70      0.50     0.89     2.85 1.00    16952    24291
## Intercept[8]       2.94      0.85     1.57     4.87 1.00    16664    24381
## Intercept[9]       4.29      1.23     2.30     7.08 1.00    16575    23624
## Intercept[10]      5.33      1.52     2.87     8.80 1.00    16458    24130
## disc_Intercept    -0.92      0.29    -1.47    -0.34 1.00    16416    24236
## 
## 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: stfgov ~ 1 
##          disc ~ 1
##    Data: cee18 (Number of observations: 6416) 
##   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]      -3.58      1.05    -5.96    -1.87 1.00     7586     9981
## Intercept[2]      -2.80      0.82    -4.66    -1.46 1.00     7614    10045
## Intercept[3]      -1.83      0.54    -3.05    -0.96 1.00     7602     9960
## Intercept[4]      -0.97      0.29    -1.62    -0.50 1.00     7663    10167
## Intercept[5]      -0.32      0.10    -0.55    -0.16 1.00     8458    12326
## Intercept[6]       0.71      0.21     0.36     1.18 1.00     7857    10662
## Intercept[7]       1.53      0.45     0.80     2.56 1.00     7642    10117
## Intercept[8]       2.69      0.79     1.40     4.49 1.00     7604    10034
## Intercept[9]       3.94      1.16     2.05     6.56 1.00     7593    10040
## Intercept[10]      4.89      1.44     2.55     8.14 1.00     7598     9887
## disc_Intercept    -0.88      0.29    -1.43    -0.27 1.00     7580     9914
## 
## 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: stfgov ~ 1 + (1 | nuts2) 
##          sigma ~ 1 + (1 | nuts2)
##    Data: cee18 (Number of observations: 6416) 
##   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)           0.63      0.10     0.46     0.87 1.00    31832    36105
## sd(sigma_Intercept)     0.09      0.02     0.06     0.13 1.00    35078    37206
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           4.59      0.13     4.33     4.84 1.00    26050    34059
## sigma_Intercept     0.92      0.02     0.88     0.96 1.00    36898    37900
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   206.96     57.79   116.38   340.48 1.00    39285    38846
## 
## 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: stfgov ~ 1 
##          sigma ~ 1
##    Data: cee18 (Number of observations: 6416) 
##   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           4.65      0.03     4.58     4.71 1.00    21649    17890
## sigma_Intercept     0.95      0.01     0.93     0.97 1.00    23560    18412
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   215.47     58.67   122.44   351.18 1.00    21288    19090
## 
## 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).