Ordinales Modell

Modellübersicht

##  Family: cumulative 
##   Links: mu = probit; disc = log 
## Formula: stfgov ~ 1 + (1 | nuts2) 
##          disc ~ 1 + (1 | nuts2)
##    Data: cee12 (Number of observations: 7500) 
##   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)          0.60      0.20     0.29     1.06 1.00    19431    28141
## sd(disc_Intercept)     0.15      0.02     0.10     0.20 1.00    31250    37020
## 
## Population-Level Effects: 
##                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept[1]      -2.62      0.77    -4.39    -1.38 1.00    17673    26833
## Intercept[2]      -1.70      0.51    -2.87    -0.88 1.00    17744    27344
## Intercept[3]      -0.81      0.27    -1.44    -0.39 1.00    18303    28416
## Intercept[4]       0.10      0.13    -0.14     0.38 1.00    25206    32264
## Intercept[5]       0.89      0.28     0.44     1.53 1.00    19495    27905
## Intercept[6]       2.01      0.59     1.06     3.35 1.00    18314    27151
## Intercept[7]       2.79      0.81     1.48     4.63 1.00    18081    27019
## Intercept[8]       3.67      1.06     1.96     6.09 1.00    18008    26506
## Intercept[9]       4.78      1.37     2.55     7.91 1.00    17911    26159
## Intercept[10]      5.83      1.68     3.12     9.68 1.00    17834    26721
## disc_Intercept    -0.89      0.29    -1.44    -0.31 1.00    17761    26601
## 
## 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: cee12 (Number of observations: 7500) 
##   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]      -2.53      0.74    -4.19    -1.32 1.00    10336    12775
## Intercept[2]      -1.67      0.49    -2.76    -0.87 1.00    10345    13009
## Intercept[3]      -0.83      0.25    -1.39    -0.43 1.00    10506    13407
## Intercept[4]       0.03      0.04    -0.04     0.11 1.00    34575    33494
## Intercept[5]       0.77      0.23     0.40     1.28 1.00    10359    12888
## Intercept[6]       1.82      0.54     0.95     3.01 1.00    10262    12926
## Intercept[7]       2.55      0.75     1.33     4.21 1.00    10278    12746
## Intercept[8]       3.35      0.98     1.75     5.53 1.00    10292    12904
## Intercept[9]       4.32      1.27     2.25     7.14 1.00    10297    12732
## Intercept[10]      5.24      1.54     2.74     8.67 1.00    10330    12854
## disc_Intercept    -0.85      0.30    -1.40    -0.24 1.00    10312    12668
## 
## 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: cee12 (Number of observations: 7500) 
##   Draws: 3 chains, each with iter = 33000; warmup = 3000; thin = 3;
##          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)           0.58      0.09     0.42     0.79 1.00    14460    21925
## sd(sigma_Intercept)     0.10      0.02     0.07     0.15 1.00    17485    21436
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           3.52      0.12     3.29     3.75 1.00     9807    15941
## sigma_Intercept     0.91      0.02     0.87     0.96 1.00    13915    20265
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   204.01     56.64   114.91   333.89 1.00    29304    29393
## 
## 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: cee12 (Number of observations: 7500) 
##   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           3.62      0.03     3.56     3.68 1.00    20312    18103
## sigma_Intercept     0.95      0.01     0.93     0.96 1.00    23987    18645
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   210.01     57.85   119.13   344.19 1.00    23483    17546
## 
## 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).