Ordinales Modell

Modellübersicht

##  Family: cumulative 
##   Links: mu = probit; disc = log 
## Formula: stfdem ~ 1 + (1 | nuts2) 
##          disc ~ 1 + (1 | nuts2)
##    Data: cee18 (Number of observations: 6446) 
##   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)          0.75      0.24     0.37     1.31 1.00    21616    29771
## sd(disc_Intercept)     0.13      0.02     0.09     0.19 1.00    32854    36414
## 
## Population-Level Effects: 
##                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept[1]      -4.09      1.16    -6.72    -2.20 1.00    20988    29181
## Intercept[2]      -3.23      0.93    -5.33    -1.74 1.00    21024    29286
## Intercept[3]      -2.36      0.68    -3.91    -1.26 1.00    21137    29487
## Intercept[4]      -1.41      0.43    -2.39    -0.73 1.00    21305    30122
## Intercept[5]      -0.71      0.25    -1.30    -0.31 1.00    22112    31872
## Intercept[6]       0.42      0.20     0.11     0.88 1.00    23575    31643
## Intercept[7]       1.26      0.39     0.63     2.15 1.00    21726    29573
## Intercept[8]       2.43      0.71     1.29     4.03 1.00    21372    28908
## Intercept[9]       4.03      1.16     2.17     6.63 1.00    21294    29158
## Intercept[10]      5.15      1.47     2.78     8.46 1.00    21244    29578
## disc_Intercept    -0.87      0.28    -1.41    -0.29 1.00    21314    29507
## 
## 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).

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Posterior-Verteilung

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Ordinales Nullmodell

Modellübersicht

##  Family: cumulative 
##   Links: mu = probit; disc = log 
## Formula: stfdem ~ 1 
##          disc ~ 1
##    Data: cee18 (Number of observations: 6446) 
##   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]      -3.81      1.08    -6.28    -2.03 1.00     8262    10502
## Intercept[2]      -3.06      0.87    -5.05    -1.63 1.00     8252    10501
## Intercept[3]      -2.26      0.64    -3.73    -1.20 1.00     8275    10548
## Intercept[4]      -1.39      0.40    -2.29    -0.74 1.00     8307    10709
## Intercept[5]      -0.74      0.21    -1.22    -0.39 1.00     8475    10922
## Intercept[6]       0.33      0.10     0.17     0.56 1.00     9328    13247
## Intercept[7]       1.11      0.32     0.59     1.85 1.00     8360    10822
## Intercept[8]       2.22      0.63     1.19     3.68 1.00     8273    10624
## Intercept[9]       3.71      1.06     1.98     6.13 1.00     8262    10464
## Intercept[10]      4.73      1.35     2.52     7.82 1.00     8269    10520
## disc_Intercept    -0.85      0.29    -1.39    -0.26 1.00     8258    10466
## 
## 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).

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Autokorrelation-Plots

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Autokorrelation-Plot

Autokorrelation-Plot

Posterior-Verteilung

Posterior-Plot

Posterior-Plot

Metrisches Modell

Modellübersicht

##  Family: student 
##   Links: mu = identity; sigma = log; nu = identity 
## Formula: stfdem ~ 1 + (1 | nuts2) 
##          sigma ~ 1 + (1 | nuts2)
##    Data: cee18 (Number of observations: 6446) 
##   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)           0.72      0.11     0.53     0.97 1.00    22313    26608
## sd(sigma_Intercept)     0.09      0.02     0.06     0.14 1.00    25395    28284
## 
## Population-Level Effects: 
##                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept           4.99      0.15     4.70     5.28 1.00    15476    22624
## sigma_Intercept     0.87      0.02     0.83     0.92 1.00    26358    28513
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   185.69     55.17   100.23   312.54 1.00    29581    29188
## 
## 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).

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Posterior-Verteilung

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Metrisches Nullmodell

Modellübersicht

##  Family: student 
##   Links: mu = identity; sigma = log; nu = identity 
## Formula: stfdem ~ 1 
##          sigma ~ 1
##    Data: cee18 (Number of observations: 6446) 
##   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           5.04      0.03     4.98     5.10 1.00    21410    18716
## sigma_Intercept     0.92      0.01     0.90     0.94 1.00    23332    19295
## 
## Family Specific Parameters: 
##    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## nu   200.46     56.91   110.75   332.32 1.00    21133    18542
## 
## 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).

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Autokorrelation-Plots

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Posterior-Verteilung

Posterior-Plot




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