Ordinales Modell

Modellübersicht

##  Family: cumulative 
##   Links: mu = probit; disc = log 
## Formula: stfgov ~ 1 + (1 | nuts2) 
##          disc ~ 1 + (1 | nuts2)
##    Data: cee04 (Number of observations: 7377) 
##   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)          0.49      0.16     0.24     0.88 1.00    21267    29218
## sd(disc_Intercept)     0.03      0.02     0.00     0.06 1.00    32400    33635
## 
## Population-Level Effects: 
##                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept[1]      -2.11      0.62    -3.56    -1.12 1.00    19969    26536
## Intercept[2]      -1.33      0.40    -2.26    -0.69 1.00    20031    26835
## Intercept[3]      -0.43      0.17    -0.81    -0.17 1.00    21413    29446
## Intercept[4]       0.57      0.19     0.27     1.01 1.00    22006    30974
## Intercept[5]       1.27      0.38     0.67     2.13 1.00    20649    28842
## Intercept[6]       2.41      0.69     1.28     3.99 1.00    20316    27804
## Intercept[7]       3.17      0.91     1.69     5.24 1.00    20225    27616
## Intercept[8]       3.94      1.13     2.11     6.53 1.00    20259    27596
## Intercept[9]       4.92      1.41     2.63     8.14 1.00    20201    27115
## Intercept[10]      5.65      1.62     3.02     9.35 1.00    20139    26975
## disc_Intercept    -0.71      0.29    -1.25    -0.12 1.00    20105    27283
## 
## 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: cee04 (Number of observations: 7377) 
##   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]      -1.96      0.58    -3.29    -1.03 1.00    14095    16304
## Intercept[2]      -1.23      0.36    -2.06    -0.64 1.00    14131    16353
## Intercept[3]      -0.40      0.12    -0.67    -0.20 1.00    14681    17307
## Intercept[4]       0.53      0.16     0.27     0.89 1.00    14358    17021
## Intercept[5]       1.18      0.35     0.62     1.98 1.00    14115    16595
## Intercept[6]       2.23      0.66     1.17     3.74 1.00    14108    16367
## Intercept[7]       2.93      0.86     1.54     4.90 1.00    14110    16247
## Intercept[8]       3.65      1.08     1.92     6.11 1.00    14110    16063
## Intercept[9]       4.55      1.34     2.38     7.63 1.00    14091    16466
## Intercept[10]      5.24      1.55     2.75     8.78 1.00    14084    16134
## disc_Intercept    -0.65      0.30    -1.21    -0.05 1.00    14082    16211
## 
## 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: cee04 (Number of observations: 7377) 
##   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)           0.50      0.08     0.37     0.68 1.00    25492    28024
## sd(sigma_Intercept)     0.05      0.01     0.03     0.07 1.00    28079    28756
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           3.05      0.10     2.84     3.25 1.00    21862    26139
## sigma_Intercept     0.77      0.01     0.74     0.79 1.00    29818    29314
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   153.83     49.75    79.39   272.03 1.00    28577    29274
## 
## 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: cee04 (Number of observations: 7377) 
##   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           3.07      0.03     3.02     3.12 1.00    22469    18998
## sigma_Intercept     0.79      0.01     0.78     0.81 1.00    21374    18725
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   163.67     51.37    85.57   285.28 1.00    21068    18553
## 
## 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).