elab. territoriali 2026-01-03
2026-01-03
fasi dell’analisi
1. analisi “ecologica” per comune: modello con dati a livello comunale,
variabile dipendente: prcAst=frazione di astenuti,
variabili esplicative: Indice.di.Vecchiaia,
Indice.di.Dipendenza.Totale,
VA.add.2020=valore aggiunto per addetto,
Add.resid.2020=addetti per 100 residenti,
Add.micro.2020=addetti alle micro-imprese per 100 addetti,
Add.tecno.2020=addetti delle imprese ad alta tecnologia e intense in
conoscenza per 100 addetti,
Reddito.IRPEF.resid=reddito IRPEF per residente,
perc.stranieri=percentuale di residenti stranieri
Beta Regression (Analisi Aggregata):
Analisi della percentuale di astenuti per ogni comune.
Natura del dato (Bounded Data): La variabile dipendente (tasso di
astensione) è una proporzione definita nell’intervallo aperto (0,1)
analisi dell’ecologia del voto, obiettivo: identificare se esistono “aree geografiche” (comuni) con caratteristiche strutturali (reddito medio, indice di vecchiaia) che favoriscono l’astensione, trattando il comune come un’unità organica.
mod_beta_s.com <- betareg(
prcAst ~ codAreaRurale +
Indice.di.Vecchiaia+
Indice.di.Dipendenza.Totale +
VA.add.2020 +
Add.resid.2020 +
Add.micro.2020 +
Add.tecno.2020 +
Reddito.IRPEF.resid+
perc.stranieri,
data = tbcomuni.3_s,
link = "logit"
)##
## Call:
## betareg(formula = prcAst ~ codAreaRurale + Indice.di.Vecchiaia + Indice.di.Dipendenza.Totale +
## VA.add.2020 + Add.resid.2020 + Add.micro.2020 + Add.tecno.2020 +
## Reddito.IRPEF.resid + perc.stranieri, data = tbcomuni.3_s, link = "logit")
##
## Quantile residuals:
## Min 1Q Median 3Q Max
## -2.9639 -0.6509 -0.0484 0.5541 3.6947
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.42655 0.08635 4.940 7.82e-07 ***
## codAreaRuraleB -0.17735 0.09036 -1.963 0.04968 *
## codAreaRuraleC -0.13030 0.08778 -1.484 0.13772
## codAreaRuraleD -0.21179 0.09250 -2.290 0.02204 *
## Indice.di.Vecchiaia 0.12310 0.02842 4.332 1.48e-05 ***
## Indice.di.Dipendenza.Totale 0.01234 0.03071 0.402 0.68776
## VA.add.2020 0.01032 0.01586 0.651 0.51528
## Add.resid.2020 0.07766 0.02628 2.955 0.00312 **
## Add.micro.2020 0.01863 0.01883 0.989 0.32242
## Add.tecno.2020 -0.04399 0.01693 -2.598 0.00937 **
## Reddito.IRPEF.resid -0.13586 0.02225 -6.107 1.02e-09 ***
## perc.stranieri 0.00548 0.01498 0.366 0.71448
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 78.014 6.035 12.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 485.5 on 13 Df
## Pseudo R-squared: 0.3348
## Number of iterations: 22 (BFGS) + 2 (Fisher scoring)Una analisi multilivello per comune: problemi a causa dell’alto numero di comuni coinvolti nell’indagine (200 comuni) e dei molti comuni con meno di 5 osservazioni
ISTATn | n | n.weighted | n.ast | prcast.camp | prcast.w | prcAst.reale |
|---|---|---|---|---|---|---|
33006 | 2 | 0.3108030 | 0 | 0.00000 | 0.00000 | 63.98 |
33011 | 3 | 1.9891943 | 1 | 33.33333 | 79.41148 | 57.31 |
33012 | 2 | 2.5902516 | 1 | 50.00000 | 57.60314 | 57.87 |
33013 | 2 | 2.8677117 | 0 | 0.00000 | 0.00000 | 60.75 |
33016 | 1 | 1.4118024 | 0 | 0.00000 | 0.00000 | 65.86 |
33018 | 1 | 2.1661571 | 1 | 100.00000 | 100.00000 | 55.38 |
33021 | 1 | 1.4226227 | 0 | 0.00000 | 0.00000 | 56.58 |
33022 | 2 | 2.0656422 | 1 | 50.00000 | 61.49779 | 53.73 |
33023 | 4 | 5.5392704 | 2 | 50.00000 | 70.39906 | 54.60 |
33024 | 1 | 0.2450444 | 0 | 0.00000 | 0.00000 | 61.08 |
33027 | 2 | 4.1306391 | 2 | 100.00000 | 100.00000 | 57.11 |
33032 | 23 | 19.9946168 | 7 | 30.43478 | 44.05856 | 56.84 |
33035 | 3 | 1.9455964 | 1 | 33.33333 | 61.84875 | 56.46 |
33036 | 1 | 0.3485780 | 0 | 0.00000 | 0.00000 | 57.61 |
33040 | 1 | 0.1716933 | 0 | 0.00000 | 0.00000 | 53.94 |
33041 | 1 | 0.4870511 | 0 | 0.00000 | 0.00000 | 59.50 |
33044 | 1 | 0.4043708 | 0 | 0.00000 | 0.00000 | 67.59 |
33049 | 1 | 1.7225433 | 1 | 100.00000 | 100.00000 | 55.59 |
34004 | 1 | 0.2686247 | 0 | 0.00000 | 0.00000 | 65.11 |
34005 | 1 | 0.5098097 | 0 | 0.00000 | 0.00000 | 66.85 |
34006 | 1 | 0.4324760 | 0 | 0.00000 | 0.00000 | 61.54 |
34009 | 4 | 4.1985860 | 3 | 75.00000 | 97.40294 | 51.75 |
34013 | 2 | 4.0126949 | 2 | 100.00000 | 100.00000 | 54.31 |
34014 | 6 | 7.2426441 | 2 | 33.33333 | 46.70397 | 54.69 |
34015 | 3 | 1.8025244 | 0 | 0.00000 | 0.00000 | 60.02 |
34016 | 3 | 3.1102940 | 2 | 66.66667 | 96.53475 | 50.06 |
34018 | 2 | 1.6762625 | 0 | 0.00000 | 0.00000 | 57.50 |
34020 | 1 | 0.6279505 | 0 | 0.00000 | 0.00000 | 58.68 |
34023 | 4 | 3.7822762 | 1 | 25.00000 | 27.99277 | 53.93 |
34025 | 3 | 2.4151320 | 1 | 33.33333 | 50.59556 | 54.26 |
34027 | 57 | 61.0567899 | 27 | 47.36842 | 63.52821 | 55.25 |
34031 | 4 | 5.1952041 | 3 | 75.00000 | 84.03406 | 55.21 |
34032 | 8 | 9.1958734 | 3 | 37.50000 | 50.92124 | 61.27 |
34033 | 4 | 3.5851826 | 3 | 75.00000 | 91.72190 | 56.69 |
34039 | 1 | 0.8819059 | 0 | 0.00000 | 0.00000 | 51.53 |
34041 | 2 | 1.2321563 | 0 | 0.00000 | 0.00000 | 63.90 |
34042 | 1 | 0.9386562 | 0 | 0.00000 | 0.00000 | 53.36 |
34045 | 1 | 1.9160084 | 1 | 100.00000 | 100.00000 | 53.26 |
34051 | 3 | 2.3677597 | 0 | 0.00000 | 0.00000 | 60.51 |
35001 | 2 | 2.4438322 | 0 | 0.00000 | 0.00000 | 43.74 |
35002 | 3 | 3.9680106 | 2 | 66.66667 | 96.18130 | 53.12 |
35003 | 2 | 6.7407622 | 2 | 100.00000 | 100.00000 | 56.34 |
35004 | 3 | 1.0441960 | 0 | 0.00000 | 0.00000 | 57.56 |
35005 | 2 | 3.9660815 | 1 | 50.00000 | 75.56207 | 66.28 |
35008 | 2 | 2.4387078 | 0 | 0.00000 | 0.00000 | 62.31 |
35009 | 3 | 2.4613144 | 1 | 33.33333 | 37.80071 | 54.36 |
35010 | 2 | 2.8251356 | 1 | 50.00000 | 60.63752 | 57.36 |
35012 | 2 | 3.2211848 | 2 | 100.00000 | 100.00000 | 60.10 |
35013 | 1 | 0.3202932 | 0 | 0.00000 | 0.00000 | 49.86 |
35014 | 2 | 3.0243755 | 1 | 50.00000 | 57.51944 | 54.70 |
35015 | 2 | 1.7085379 | 1 | 50.00000 | 79.05738 | 58.22 |
35017 | 2 | 1.7608933 | 1 | 50.00000 | 81.10964 | 55.41 |
35018 | 2 | 4.6021820 | 1 | 50.00000 | 58.96621 | 59.69 |
35020 | 5 | 7.8791909 | 3 | 60.00000 | 77.26244 | 50.17 |
35022 | 1 | 1.1117396 | 0 | 0.00000 | 0.00000 | 59.42 |
35024 | 4 | 3.2155499 | 1 | 25.00000 | 54.09973 | 57.29 |
35026 | 2 | 2.5004887 | 0 | 0.00000 | 0.00000 | 59.99 |
35027 | 1 | 0.3502813 | 0 | 0.00000 | 0.00000 | 56.93 |
35028 | 3 | 3.5159801 | 1 | 33.33333 | 36.68030 | 52.24 |
35029 | 2 | 2.4872740 | 1 | 50.00000 | 56.76874 | 60.10 |
35030 | 3 | 3.7688436 | 0 | 0.00000 | 0.00000 | 48.35 |
35032 | 5 | 3.7594616 | 1 | 20.00000 | 38.53848 | 57.60 |
35033 | 39 | 37.5353779 | 13 | 33.33333 | 53.14974 | 53.74 |
35034 | 3 | 4.5926413 | 1 | 33.33333 | 68.92052 | 51.10 |
35035 | 2 | 3.2787860 | 1 | 50.00000 | 64.45350 | 55.76 |
35036 | 4 | 7.2799725 | 3 | 75.00000 | 97.30512 | 52.81 |
35037 | 1 | 1.2700173 | 1 | 100.00000 | 100.00000 | 51.23 |
35039 | 1 | 1.3725426 | 0 | 0.00000 | 0.00000 | 56.91 |
35040 | 6 | 4.6832159 | 2 | 33.33333 | 49.87109 | 49.42 |
35041 | 1 | 1.7992273 | 1 | 100.00000 | 100.00000 | 61.19 |
35043 | 2 | 4.1977003 | 1 | 50.00000 | 55.04046 | 53.07 |
35044 | 1 | 0.8001781 | 0 | 0.00000 | 0.00000 | 53.23 |
36003 | 1 | 2.5181193 | 1 | 100.00000 | 100.00000 | 52.36 |
36005 | 16 | 15.5047296 | 3 | 18.75000 | 44.87767 | 51.97 |
36006 | 8 | 9.6109361 | 4 | 50.00000 | 81.29270 | 53.71 |
36007 | 3 | 6.2113916 | 2 | 66.66667 | 96.23985 | 48.54 |
36008 | 1 | 1.6017371 | 1 | 100.00000 | 100.00000 | 53.10 |
36009 | 1 | 0.4302483 | 0 | 0.00000 | 0.00000 | 52.14 |
36010 | 1 | 0.8926852 | 0 | 0.00000 | 0.00000 | 57.42 |
36011 | 1 | 1.8884497 | 0 | 0.00000 | 0.00000 | 52.55 |
36013 | 3 | 2.9928782 | 1 | 33.33333 | 57.82767 | 59.03 |
36015 | 6 | 13.0256307 | 3 | 50.00000 | 67.97600 | 49.27 |
36016 | 1 | 2.1727971 | 0 | 0.00000 | 0.00000 | 63.89 |
36019 | 2 | 1.0861506 | 0 | 0.00000 | 0.00000 | 57.01 |
36020 | 2 | 2.9534589 | 2 | 100.00000 | 100.00000 | 53.09 |
36022 | 3 | 3.6638099 | 1 | 33.33333 | 40.87651 | 54.13 |
36023 | 38 | 32.0776845 | 5 | 13.15789 | 31.81533 | 49.21 |
36027 | 5 | 5.7167275 | 2 | 40.00000 | 66.14144 | 52.19 |
36028 | 1 | 1.7130922 | 1 | 100.00000 | 100.00000 | 57.71 |
36029 | 1 | 1.2597484 | 0 | 0.00000 | 0.00000 | 54.99 |
36030 | 2 | 0.7680412 | 0 | 0.00000 | 0.00000 | 56.17 |
36037 | 4 | 3.1799791 | 1 | 25.00000 | 62.62016 | 54.93 |
36038 | 2 | 3.9261246 | 1 | 50.00000 | 45.82706 | 58.72 |
36039 | 1 | 0.6445478 | 0 | 0.00000 | 0.00000 | 60.34 |
36040 | 5 | 5.7172115 | 2 | 40.00000 | 65.46347 | 58.68 |
36042 | 3 | 3.9830634 | 0 | 0.00000 | 0.00000 | 59.75 |
36043 | 1 | 0.5375740 | 0 | 0.00000 | 0.00000 | 52.35 |
36044 | 5 | 5.1407998 | 2 | 40.00000 | 47.22606 | 51.98 |
36045 | 1 | 0.7862399 | 0 | 0.00000 | 0.00000 | 48.95 |
36046 | 3 | 4.1402425 | 0 | 0.00000 | 0.00000 | 50.12 |
36047 | 2 | 3.6223249 | 1 | 50.00000 | 65.20077 | 60.03 |
37001 | 3 | 2.9599017 | 1 | 33.33333 | 48.26755 | 46.44 |
37002 | 5 | 2.9534046 | 0 | 0.00000 | 0.00000 | 47.00 |
37003 | 1 | 0.2986821 | 0 | 0.00000 | 0.00000 | 52.61 |
37005 | 3 | 1.6631175 | 1 | 33.33333 | 66.46754 | 48.49 |
37006 | 149 | 88.8540000 | 50 | 33.55705 | 48.81164 | 47.02 |
37008 | 4 | 3.6533907 | 1 | 25.00000 | 31.10998 | 49.20 |
37009 | 5 | 3.8860596 | 2 | 40.00000 | 64.33991 | 44.82 |
37011 | 11 | 13.7843196 | 4 | 36.36364 | 47.84211 | 47.04 |
37013 | 1 | 3.2322599 | 1 | 100.00000 | 100.00000 | 51.69 |
37017 | 1 | 0.3048596 | 0 | 0.00000 | 0.00000 | 52.36 |
37019 | 10 | 7.5084924 | 3 | 30.00000 | 60.97578 | 45.17 |
37020 | 2 | 1.8886679 | 1 | 50.00000 | 67.08132 | 47.67 |
37021 | 2 | 1.9549166 | 0 | 0.00000 | 0.00000 | 43.02 |
37024 | 2 | 2.8573439 | 2 | 100.00000 | 100.00000 | 53.13 |
37025 | 1 | 0.7420708 | 1 | 100.00000 | 100.00000 | 54.52 |
37027 | 1 | 1.5743214 | 1 | 100.00000 | 100.00000 | 54.44 |
37028 | 1 | 0.2449106 | 0 | 0.00000 | 0.00000 | 53.88 |
37030 | 5 | 8.2867025 | 2 | 40.00000 | 40.82269 | 45.44 |
37032 | 10 | 13.1692016 | 2 | 20.00000 | 46.28405 | 50.63 |
37036 | 4 | 3.2644678 | 2 | 50.00000 | 74.29321 | 51.73 |
37037 | 4 | 2.8960160 | 2 | 50.00000 | 64.58382 | 50.73 |
37039 | 4 | 6.5945163 | 2 | 50.00000 | 80.96007 | 52.17 |
37040 | 2 | 1.8712495 | 0 | 0.00000 | 0.00000 | 54.92 |
37042 | 4 | 4.4230890 | 0 | 0.00000 | 0.00000 | 45.45 |
37044 | 2 | 1.0977757 | 1 | 50.00000 | 67.59767 | 54.70 |
37045 | 1 | 0.2702894 | 0 | 0.00000 | 0.00000 | 46.89 |
37046 | 1 | 1.4341150 | 1 | 100.00000 | 100.00000 | 46.80 |
37047 | 4 | 4.3794551 | 2 | 50.00000 | 48.83287 | 48.40 |
37048 | 3 | 1.5106757 | 0 | 0.00000 | 0.00000 | 48.73 |
37050 | 2 | 1.4801245 | 0 | 0.00000 | 0.00000 | 47.18 |
37052 | 3 | 2.1393200 | 1 | 33.33333 | 34.68723 | 49.57 |
37053 | 7 | 5.5037032 | 0 | 0.00000 | 0.00000 | 47.22 |
37054 | 9 | 6.9240386 | 2 | 22.22222 | 33.15358 | 42.75 |
37055 | 6 | 6.0385274 | 3 | 50.00000 | 50.99576 | 53.60 |
37056 | 1 | 0.5492234 | 0 | 0.00000 | 0.00000 | 53.74 |
37057 | 3 | 4.1916863 | 1 | 33.33333 | 64.26907 | 46.37 |
37059 | 3 | 3.9853629 | 0 | 0.00000 | 0.00000 | 57.87 |
37060 | 4 | 3.7842446 | 1 | 25.00000 | 54.80918 | 44.48 |
37061 | 6 | 6.4107738 | 5 | 83.33333 | 94.22540 | 50.89 |
37062 | 1 | 0.4471234 | 0 | 0.00000 | 0.00000 | 56.94 |
38001 | 4 | 3.7777808 | 0 | 0.00000 | 0.00000 | 56.21 |
38003 | 1 | 1.6374276 | 1 | 100.00000 | 100.00000 | 57.99 |
38004 | 10 | 9.4418928 | 2 | 20.00000 | 33.64471 | 55.12 |
38006 | 3 | 5.8892005 | 3 | 100.00000 | 100.00000 | 70.41 |
38007 | 10 | 10.1278737 | 1 | 10.00000 | 16.16754 | 56.62 |
38008 | 38 | 41.2692654 | 19 | 50.00000 | 73.01461 | 52.89 |
38010 | 1 | 1.6775573 | 1 | 100.00000 | 100.00000 | 62.88 |
38011 | 3 | 3.5439387 | 0 | 0.00000 | 0.00000 | 65.60 |
38017 | 3 | 6.1240290 | 2 | 66.66667 | 90.56901 | 62.03 |
38018 | 3 | 5.4027939 | 3 | 100.00000 | 100.00000 | 56.18 |
38019 | 1 | 0.6965487 | 0 | 0.00000 | 0.00000 | 58.32 |
38022 | 3 | 3.5247214 | 0 | 0.00000 | 0.00000 | 54.70 |
38029 | 2 | 2.4748906 | 0 | 0.00000 | 0.00000 | 64.76 |
38030 | 1 | 1.0165846 | 0 | 0.00000 | 0.00000 | 58.77 |
39001 | 4 | 4.2272004 | 1 | 25.00000 | 34.77693 | 49.84 |
39002 | 5 | 5.3494594 | 2 | 40.00000 | 49.29528 | 46.21 |
39003 | 1 | 1.2087746 | 0 | 0.00000 | 0.00000 | 44.88 |
39004 | 1 | 0.2571795 | 0 | 0.00000 | 0.00000 | 50.07 |
39006 | 4 | 5.6765050 | 2 | 50.00000 | 76.15009 | 47.76 |
39007 | 8 | 9.5680382 | 5 | 62.50000 | 80.25548 | 52.89 |
39008 | 4 | 4.9819188 | 1 | 25.00000 | 52.10322 | 55.46 |
39009 | 1 | 1.5179943 | 0 | 0.00000 | 0.00000 | 49.08 |
39010 | 11 | 11.7842291 | 2 | 18.18182 | 43.50619 | 47.41 |
39011 | 2 | 1.2473883 | 0 | 0.00000 | 0.00000 | 51.71 |
39012 | 7 | 9.8761965 | 2 | 28.57143 | 52.72396 | 49.32 |
39013 | 1 | 0.4400831 | 0 | 0.00000 | 0.00000 | 57.19 |
39014 | 38 | 39.4186025 | 13 | 34.21053 | 52.86766 | 51.32 |
39015 | 1 | 0.3020715 | 0 | 0.00000 | 0.00000 | 47.78 |
39016 | 1 | 0.3870504 | 0 | 0.00000 | 0.00000 | 48.88 |
40003 | 1 | 1.2175704 | 1 | 100.00000 | 100.00000 | 55.61 |
40004 | 1 | 5.7097793 | 1 | 100.00000 | 100.00000 | 65.68 |
40007 | 18 | 15.7795276 | 5 | 27.77778 | 61.03956 | 52.74 |
40008 | 5 | 6.2420500 | 3 | 60.00000 | 82.39081 | 55.62 |
40012 | 18 | 14.2573459 | 4 | 22.22222 | 36.54991 | 53.01 |
40013 | 7 | 5.9910246 | 0 | 0.00000 | 0.00000 | 54.76 |
40014 | 1 | 0.5993990 | 0 | 0.00000 | 0.00000 | 52.00 |
40015 | 5 | 7.2094758 | 2 | 40.00000 | 60.31246 | 56.68 |
40016 | 4 | 5.7845642 | 2 | 50.00000 | 64.18279 | 61.32 |
40018 | 2 | 2.0416483 | 2 | 100.00000 | 100.00000 | 56.90 |
40019 | 2 | 1.7292873 | 0 | 0.00000 | 0.00000 | 59.86 |
40020 | 1 | 1.3466984 | 0 | 0.00000 | 0.00000 | 57.48 |
40022 | 3 | 2.8861011 | 0 | 0.00000 | 0.00000 | 55.76 |
40041 | 4 | 4.7418319 | 2 | 50.00000 | 73.29643 | 60.62 |
40044 | 1 | 1.5971418 | 1 | 100.00000 | 100.00000 | 52.34 |
40045 | 2 | 2.1230627 | 0 | 0.00000 | 0.00000 | 59.30 |
99001 | 1 | 1.0856282 | 1 | 100.00000 | 100.00000 | 60.15 |
99002 | 2 | 1.7402327 | 1 | 50.00000 | 80.10899 | 62.80 |
99003 | 2 | 2.3729563 | 1 | 50.00000 | 69.00370 | 58.17 |
99004 | 1 | 0.7973038 | 0 | 0.00000 | 0.00000 | 70.51 |
99005 | 2 | 2.2136106 | 1 | 50.00000 | 93.33595 | 64.11 |
99013 | 8 | 5.8165124 | 2 | 25.00000 | 46.19567 | 56.20 |
99014 | 31 | 29.0847170 | 11 | 35.48387 | 46.95421 | 57.38 |
99017 | 3 | 3.8574864 | 1 | 33.33333 | 60.68620 | 60.44 |
99018 | 4 | 5.7213755 | 3 | 75.00000 | 94.15012 | 54.26 |
99020 | 4 | 3.5020331 | 2 | 50.00000 | 77.75643 | 61.77 |
99022 | 1 | 1.1319200 | 1 | 100.00000 | 100.00000 | 52.26 |
99023 | 2 | 2.1476191 | 0 | 0.00000 | 0.00000 | 57.08 |
99026 | 1 | 1.8472268 | 1 | 100.00000 | 100.00000 | 64.27 |
99029 | 1 | 1.6124732 | 1 | 100.00000 | 100.00000 | 70.68 |
33001 | 56.65 | |||||
33002 | 56.79 | |||||
33003 | 56.12 | |||||
33004 | 61.74 | |||||
33005 | 56.53 | |||||
33007 | 60.61 | |||||
33008 | 59.62 | |||||
33010 | 60.57 | |||||
33014 | 59.84 | |||||
33015 | 66.49 | |||||
33017 | 50.69 | |||||
33019 | 70.41 | |||||
33020 | 68.65 | |||||
33025 | 64.12 | |||||
33026 | 61.37 | |||||
33028 | 80.95 | |||||
33030 | 83.36 | |||||
33033 | 61.97 | |||||
33034 | 54.65 | |||||
33037 | 57.74 | |||||
33038 | 57.62 | |||||
33039 | 61.00 | |||||
33042 | 66.42 | |||||
33043 | 50.99 | |||||
33045 | 56.55 | |||||
33046 | 62.13 | |||||
33047 | 71.79 | |||||
33048 | 63.84 | |||||
34001 | 69.08 | |||||
34002 | 76.32 | |||||
34003 | 69.68 | |||||
34007 | 60.46 | |||||
34008 | 64.47 | |||||
34010 | 66.03 | |||||
34011 | 54.92 | |||||
34012 | 59.70 | |||||
34017 | 61.57 | |||||
34019 | 55.59 | |||||
34022 | 59.94 | |||||
34024 | 55.20 | |||||
34026 | 50.29 | |||||
34028 | 72.09 | |||||
34050 | 67.44 | |||||
34030 | 69.02 | |||||
34049 | 61.33 | |||||
34035 | 59.05 | |||||
34036 | 57.00 | |||||
34038 | 67.35 | |||||
34040 | 59.96 | |||||
34044 | 75.82 | |||||
34046 | 70.34 | |||||
35006 | 68.10 | |||||
35011 | 50.89 | |||||
35016 | 51.42 | |||||
35021 | 53.99 | |||||
35023 | 62.59 | |||||
35038 | 55.75 | |||||
35046 | 49.25 | |||||
35042 | 54.67 | |||||
35045 | 55.61 | |||||
36001 | 54.27 | |||||
36002 | 56.21 | |||||
36004 | 50.46 | |||||
36012 | 56.39 | |||||
36014 | 51.10 | |||||
36017 | 62.62 | |||||
36018 | 53.35 | |||||
36021 | 50.56 | |||||
36024 | 45.06 | |||||
36025 | 58.30 | |||||
36026 | 53.72 | |||||
36031 | 67.69 | |||||
36032 | 55.30 | |||||
36033 | 53.21 | |||||
36034 | 55.26 | |||||
36035 | 56.64 | |||||
36036 | 48.12 | |||||
36041 | 52.03 | |||||
37007 | 56.54 | |||||
37010 | 55.04 | |||||
37012 | 53.92 | |||||
37014 | 54.39 | |||||
37015 | 52.54 | |||||
37016 | 50.50 | |||||
37022 | 50.44 | |||||
37026 | 48.15 | |||||
37031 | 54.38 | |||||
37033 | 50.75 | |||||
37034 | 52.83 | |||||
37035 | 55.27 | |||||
37038 | 51.53 | |||||
37041 | 54.41 | |||||
37051 | 51.87 | |||||
38005 | 63.42 | |||||
38027 | 62.72 | |||||
38025 | 52.39 | |||||
38012 | 50.78 | |||||
38014 | 61.00 | |||||
38028 | 56.14 | |||||
38023 | 50.66 | |||||
39005 | 47.28 | |||||
39017 | 49.47 | |||||
39018 | 47.07 | |||||
40001 | 53.83 | |||||
40005 | 56.52 | |||||
40009 | 51.69 | |||||
40011 | 64.95 | |||||
40028 | 51.78 | |||||
40031 | 53.65 | |||||
40032 | 55.10 | |||||
40033 | 49.00 | |||||
40036 | 48.94 | |||||
40037 | 50.96 | |||||
40043 | 38.81 | |||||
40046 | 57.24 | |||||
40049 | 59.76 | |||||
40050 | 53.32 | |||||
99021 | 47.55 | |||||
99006 | 61.08 | |||||
99008 | 76.32 | |||||
99009 | 68.55 | |||||
99011 | 64.33 | |||||
99024 | 58.33 | |||||
99028 | 60.21 | |||||
99015 | 71.78 | |||||
99016 | 68.36 | |||||
99025 | 65.26 | |||||
99027 | 66.23 | |||||
99031 | 71.09 | |||||
99030 | 59.63 | |||||
n=n.osservazioni nel campione | ||||||
SLL.2021 | n | n.weighted | n.ast | prcast.camp | prcast.w | prcAst.reale |
|---|---|---|---|---|---|---|
335 | 2 | 4.130639 | 2 | 100.00000 | 100.00000 | 58.47809 |
340 | 2 | 3.966081 | 1 | 50.00000 | 75.56207 | 66.28000 |
801 | 9 | 9.059648 | 3 | 33.33333 | 57.81541 | 59.41266 |
802 | 41 | 36.623302 | 12 | 29.26829 | 46.15949 | 58.23846 |
803 | 3 | 1.210910 | 0 | 0.00000 | 0.00000 | 66.63549 |
804 | 17 | 18.241042 | 5 | 29.41176 | 44.21488 | 59.38743 |
805 | 92 | 96.996859 | 43 | 46.73913 | 63.65557 | 56.25932 |
806 | 2 | 2.119521 | 1 | 50.00000 | 84.88841 | 52.76766 |
807 | 19 | 18.731581 | 5 | 26.31579 | 40.15572 | 56.61795 |
808 | 78 | 84.378621 | 26 | 33.33333 | 52.28105 | 53.99765 |
809 | 31 | 36.100471 | 11 | 35.48387 | 59.89394 | 52.00030 |
810 | 12 | 12.737395 | 3 | 25.00000 | 41.51691 | 55.03548 |
811 | 62 | 65.616537 | 17 | 27.41935 | 53.11296 | 50.68926 |
812 | 7 | 7.177128 | 0 | 0.00000 | 0.00000 | 55.88914 |
813 | 1 | 2.172797 | 0 | 0.00000 | 0.00000 | 62.50531 |
814 | 23 | 37.067942 | 11 | 47.82609 | 70.22038 | 55.94789 |
815 | 281 | 213.144996 | 91 | 32.38434 | 47.38890 | 48.07431 |
816 | 8 | 12.861392 | 3 | 37.50000 | 55.73553 | 55.60826 |
817 | 14 | 16.070230 | 4 | 28.57143 | 50.43023 | 50.41952 |
818 | 9 | 15.557168 | 5 | 55.55556 | 73.50742 | 65.05635 |
819 | 64 | 71.605444 | 25 | 39.06250 | 56.54291 | 54.88455 |
820 | 20 | 20.906086 | 4 | 20.00000 | 45.19991 | 48.24653 |
821 | 25 | 28.849015 | 6 | 24.00000 | 41.28385 | 50.28599 |
822 | 47 | 49.373691 | 18 | 38.29787 | 57.76065 | 51.40780 |
823 | 1 | 1.597142 | 1 | 100.00000 | 100.00000 | 53.29539 |
824 | 26 | 26.377350 | 9 | 34.61538 | 60.74005 | 53.58295 |
825 | 16 | 19.977137 | 8 | 50.00000 | 67.16074 | 58.73317 |
826 | 28 | 23.195228 | 5 | 17.85714 | 27.71526 | 54.02989 |
827 | 1 | 0.599399 | 0 | 0.00000 | 0.00000 | 46.67192 |
828 | 5 | 5.597719 | 2 | 40.00000 | 66.72440 | 62.97732 |
829 | 11 | 8.827427 | 3 | 27.27273 | 53.84436 | 61.20629 |
830 | 43 | 48.003334 | 19 | 44.18605 | 64.00788 | 57.97947 |
831 | 4 | 5.126766 | 2 | 50.00000 | 58.10967 | 59.94657 |
332 | 71.79000 | |||||
1,102 | 68.55000 | |||||
n=n.osservazioni nel campione | ||||||
2. analisi “ecologica” per Sistema Locale del Lavoro (SLL):
aggregazione di comuni dalle caratteristiche diverse, come giustificare questo livello di analisi? In questo caso i dati sono aggregati per SLL, il modello è uguale a quello precedente
mod_beta_sll <- betareg(
prcAst ~
Indice.di.Vecchiaia +
Indice.di.Dipendenza.Totale +
VA.add.2020 +
Add.resid.2020 +
Add.micro.2020 +
Add.tecno.2020 +
Reddito.IRPEF.resid+
perc.stranieri,
data = tbSLL_s,
link = "logit"
)##
## Call:
## betareg(formula = prcAst ~ Indice.di.Vecchiaia + Indice.di.Dipendenza.Totale +
## VA.add.2020 + Add.resid.2020 + Add.micro.2020 + Add.tecno.2020 +
## Reddito.IRPEF.resid + perc.stranieri, data = tbSLL_s, link = "logit")
##
## Quantile residuals:
## Min 1Q Median 3Q Max
## -1.6459 -0.5898 -0.1281 0.3759 2.9553
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.28283 0.02609 10.840 < 2e-16 ***
## Indice.di.Vecchiaia 0.50207 0.13607 3.690 0.000225 ***
## Indice.di.Dipendenza.Totale -0.33943 0.11524 -2.945 0.003226 **
## VA.add.2020 0.08380 0.07539 1.111 0.266366
## Add.resid.2020 0.03772 0.04495 0.839 0.401364
## Add.micro.2020 0.08389 0.06026 1.392 0.163886
## Add.tecno.2020 -0.07896 0.04570 -1.728 0.083994 .
## Reddito.IRPEF.resid -0.05572 0.07688 -0.725 0.468591
## perc.stranieri 0.04558 0.05042 0.904 0.366014
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 171.91 40.98 4.195 2.73e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 65.28 on 10 Df
## Pseudo R-squared: 0.6079
## Number of iterations: 24 (BFGS) + 2 (Fisher scoring)3. Analisi multilivello bayesiana
Giustificazione uso modello multilivello bayesiano
Analisi del comportamento del singolo elettore all’interno dei territori (SLL)
Il modello multilivello riconosce la struttura gerarchica individui “annidati” in territori (SLL)
Partial Pooling (Shrinkage): in un modello a effetti fissi, i territori (SLL) con poche osservazioni (n<5) produrrebbero stime inaffidabili; per mezzo del “partial pooling” le stime dei territori più piccoli vengono “spinte” verso la media generale, riducendo l’impatto del rumore statistico senza perdere l’informazione territoriale.
3.1. Modello Baseline (Individuale)
Le variabili in analisi:
Q19: 0=ha votato, 1=non ha votato
condprf: condizione professionale: 1=imprenditore/lib.prof; 2=altro
auton.; 3=dirigente/quadro; 4=impiegato; 5=insegnante; 6=operaio;
7=disoccupati; 8=studente; 9=casalinga; 10=pensionato
Q26: Il Suo reddito familiare Le consente di vivere: 1=agiatamente;
2=con tranquillità; 3=avverto difficoltà; 4=arrivo a fine mese con molte
difficoltà; 5=mi sento povero e non arrivo mai a fine mese; 6=preferisco
non rispondere
Q29: come considera il suo luogo di residenza: 1=Urbano; 2=Suburbano;
3=Rurale
Q30: come considera il suo luogo di residenza: 1=Centrale; 2=Intermedio;
3=Periferico
SEXETA: 1=M 18-30; 2=M 30-49; 3=M 50-64; 4=M 65+; 5=F 18-30; 6=F 31-49;
7=F 50-64; 8=F 65+
weight: Peso di raking
mod_ast_bayes_W_1 <- brm(
formula = Q19 | weights(weight) ~ condprf + Q26 + Q29+ Q30 +
SEXETA,
data = df.ast.SLL,
family = bernoulli(link = "logit"),
chains = 4, iter = 2000, cores = 4,
silent = 2
)## Family: bernoulli
## Links: mu = logit
## Formula: Q19 | weights(weight) ~ condprf + Q26 + Q29 + Q30 + SEXETA
## Data: df.ast.SLL (Number of observations: 1004)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI
## Intercept -1.40 0.64 -2.65 -0.14
## condprfaltroauton. 1.81 0.51 0.83 2.83
## condprfdirigenteDquadro 0.14 0.60 -1.02 1.30
## condprfimpiegato 0.77 0.42 -0.03 1.60
## condprfinsegnante 0.15 0.66 -1.18 1.47
## condprfoperaio 1.47 0.43 0.63 2.33
## condprfdisoccupati 1.97 0.50 1.02 2.99
## condprfstudente 0.82 0.56 -0.26 1.90
## condprfcasalinga 1.64 0.48 0.70 2.57
## condprfpensionato 0.96 0.45 0.10 1.84
## Q26contranquillità 0.14 0.46 -0.73 1.05
## Q26avvertodifficoltà 0.70 0.46 -0.18 1.61
## Q26arrivoafinemeseconmoltedifficoltà 1.03 0.49 0.09 2.00
## Q26misentopoveroenonarrivomaiafinemese 1.26 0.56 0.20 2.36
## Q26preferiscononrispondere 2.34 0.75 0.94 3.88
## Q29Suburbano 0.02 0.19 -0.36 0.40
## Q29Rurale -0.23 0.22 -0.66 0.18
## Q30Intermedio 0.04 0.20 -0.34 0.43
## Q30Periferico 0.33 0.23 -0.13 0.79
## SEXETAM30M49 0.24 0.34 -0.44 0.89
## SEXETAM50M64 -0.60 0.34 -1.26 0.06
## SEXETAM65P -0.62 0.38 -1.39 0.12
## SEXETAF18M30 0.11 0.38 -0.63 0.84
## SEXETAF31M49 0.26 0.34 -0.41 0.94
## SEXETAF50M64 -0.23 0.34 -0.90 0.44
## SEXETAF65P -0.32 0.37 -1.05 0.39
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.01 1746 2336
## condprfaltroauton. 1.00 1736 1802
## condprfdirigenteDquadro 1.00 2463 2306
## condprfimpiegato 1.01 1487 1733
## condprfinsegnante 1.00 2644 2841
## condprfoperaio 1.01 1564 2093
## condprfdisoccupati 1.00 1846 1996
## condprfstudente 1.00 2081 2763
## condprfcasalinga 1.00 1781 2132
## condprfpensionato 1.01 1616 1947
## Q26contranquillità 1.00 2037 2359
## Q26avvertodifficoltà 1.00 2001 2430
## Q26arrivoafinemeseconmoltedifficoltà 1.00 1987 2152
## Q26misentopoveroenonarrivomaiafinemese 1.00 2304 2945
## Q26preferiscononrispondere 1.00 2761 3144
## Q29Suburbano 1.00 4042 3449
## Q29Rurale 1.00 3475 2659
## Q30Intermedio 1.00 3645 2921
## Q30Periferico 1.00 2955 2589
## SEXETAM30M49 1.00 2473 2985
## SEXETAM50M64 1.00 2424 2899
## SEXETAM65P 1.00 2331 3004
## SEXETAF18M30 1.00 3101 3110
## SEXETAF31M49 1.00 2468 2983
## SEXETAF50M64 1.00 2250 2574
## SEXETAF65P 1.00 2383 2868
##
## 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).Variabile | Coefficiente | IC 95% | Est.Error | OR | OR IC 95% |
|---|---|---|---|---|---|
condprfaltroauton. | 1.810*** | [0.830, 2.825] | 0.509 | 6.11 | [2.29, 16.87] |
condprfdirigenteDquadro | 0.137 | [-1.016, 1.303] | 0.597 | 1.15 | [0.36, 3.68] |
condprfimpiegato | 0.767 | [-0.026, 1.597] | 0.419 | 2.15 | [0.97, 4.94] |
condprfinsegnante | 0.154 | [-1.185, 1.474] | 0.663 | 1.17 | [0.31, 4.37] |
condprfoperaio | 1.470*** | [0.633, 2.333] | 0.433 | 4.35 | [1.88, 10.30] |
condprfdisoccupati | 1.974*** | [1.019, 2.988] | 0.500 | 7.20 | [2.77, 19.84] |
condprfstudente | 0.822 | [-0.265, 1.899] | 0.559 | 2.27 | [0.77, 6.68] |
condprfcasalinga | 1.636*** | [0.704, 2.575] | 0.477 | 5.13 | [2.02, 13.13] |
condprfpensionato | 0.956*** | [0.099, 1.841] | 0.446 | 2.60 | [1.10, 6.30] |
Q26contranquillità | 0.142 | [-0.725, 1.051] | 0.459 | 1.15 | [0.48, 2.86] |
Q26avvertodifficoltà | 0.705 | [-0.177, 1.608] | 0.464 | 2.02 | [0.84, 5.00] |
Q26arrivoafinemeseconmoltedifficoltà | 1.027*** | [0.085, 1.996] | 0.492 | 2.79 | [1.09, 7.36] |
Q26misentopoveroenonarrivomaiafinemese | 1.259*** | [0.195, 2.364] | 0.561 | 3.52 | [1.22, 10.64] |
Q26preferiscononrispondere | 2.338*** | [0.942, 3.880] | 0.751 | 10.36 | [2.57, 48.41] |
Q29Suburbano | 0.021 | [-0.361, 0.402] | 0.190 | 1.02 | [0.70, 1.50] |
Q29Rurale | -0.229 | [-0.657, 0.185] | 0.218 | 0.80 | [0.52, 1.20] |
Q30Intermedio | 0.038 | [-0.344, 0.426] | 0.196 | 1.04 | [0.71, 1.53] |
Q30Periferico | 0.332 | [-0.127, 0.789] | 0.234 | 1.39 | [0.88, 2.20] |
SEXETAM30M49 | 0.239 | [-0.439, 0.887] | 0.341 | 1.27 | [0.64, 2.43] |
SEXETAM50M64 | -0.604 | [-1.256, 0.058] | 0.336 | 0.55 | [0.28, 1.06] |
SEXETAM65P | -0.625 | [-1.387, 0.121] | 0.383 | 0.54 | [0.25, 1.13] |
SEXETAF18M30 | 0.109 | [-0.626, 0.839] | 0.377 | 1.11 | [0.53, 2.31] |
SEXETAF31M49 | 0.262 | [-0.413, 0.938] | 0.340 | 1.30 | [0.66, 2.55] |
SEXETAF50M64 | -0.228 | [-0.902, 0.444] | 0.341 | 0.80 | [0.41, 1.56] |
SEXETAF65P | -0.321 | [-1.053, 0.390] | 0.374 | 0.73 | [0.35, 1.48] |
3.2. Modello Multilivello “Puro” (Individuale + SLL)
mod_ast_bayes_W_2 <- brm(
formula = Q19 | weights(weight) ~ condprf + Q26 + Q29+ Q30 +
SEXETA +
(1 | SLL.2021),
data = df.ast.SLL,
family = bernoulli(link = "logit"),
chains = 4, iter = 2000, cores = 4,
silent = 2
)## Family: bernoulli
## Links: mu = logit
## Formula: Q19 | weights(weight) ~ condprf + Q26 + Q29 + Q30 + SEXETA + (1 | SLL.2021)
## Data: df.ast.SLL (Number of observations: 1004)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~SLL.2021 (Number of levels: 33)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.33 0.17 0.05 0.70 1.00 708 1456
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI
## Intercept -1.38 0.65 -2.69 -0.12
## condprfaltroauton. 1.78 0.51 0.79 2.81
## condprfdirigenteDquadro 0.11 0.60 -1.06 1.26
## condprfimpiegato 0.72 0.43 -0.12 1.56
## condprfinsegnante 0.08 0.67 -1.24 1.36
## condprfoperaio 1.42 0.44 0.56 2.32
## condprfdisoccupati 1.93 0.51 0.93 2.92
## condprfstudente 0.71 0.58 -0.43 1.81
## condprfcasalinga 1.59 0.49 0.63 2.54
## condprfpensionato 0.87 0.46 -0.02 1.78
## Q26contranquillità 0.18 0.46 -0.70 1.12
## Q26avvertodifficoltà 0.76 0.47 -0.14 1.72
## Q26arrivoafinemeseconmoltedifficoltà 1.08 0.49 0.15 2.10
## Q26misentopoveroenonarrivomaiafinemese 1.28 0.56 0.21 2.42
## Q26preferiscononrispondere 2.34 0.76 0.93 3.87
## Q29Suburbano -0.02 0.19 -0.41 0.34
## Q29Rurale -0.29 0.23 -0.74 0.18
## Q30Intermedio 0.08 0.20 -0.32 0.45
## Q30Periferico 0.40 0.24 -0.08 0.88
## SEXETAM30M49 0.21 0.34 -0.44 0.85
## SEXETAM50M64 -0.64 0.34 -1.31 0.03
## SEXETAM65P -0.68 0.40 -1.44 0.10
## SEXETAF18M30 0.11 0.37 -0.63 0.82
## SEXETAF31M49 0.23 0.34 -0.42 0.90
## SEXETAF50M64 -0.28 0.35 -0.97 0.38
## SEXETAF65P -0.33 0.38 -1.07 0.44
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.01 1374 1355
## condprfaltroauton. 1.00 1292 1696
## condprfdirigenteDquadro 1.00 1550 2123
## condprfimpiegato 1.00 1055 1506
## condprfinsegnante 1.00 1901 2236
## condprfoperaio 1.00 1108 1535
## condprfdisoccupati 1.00 1427 2076
## condprfstudente 1.00 1487 1779
## condprfcasalinga 1.00 1220 1780
## condprfpensionato 1.00 1130 1733
## Q26contranquillità 1.00 1469 2093
## Q26avvertodifficoltà 1.00 1416 2171
## Q26arrivoafinemeseconmoltedifficoltà 1.00 1559 2165
## Q26misentopoveroenonarrivomaiafinemese 1.00 1921 2335
## Q26preferiscononrispondere 1.00 2183 2631
## Q29Suburbano 1.00 2987 2924
## Q29Rurale 1.00 2768 2842
## Q30Intermedio 1.00 2575 2681
## Q30Periferico 1.00 2287 2502
## SEXETAM30M49 1.00 1539 2397
## SEXETAM50M64 1.00 1406 2045
## SEXETAM65P 1.00 1443 2435
## SEXETAF18M30 1.00 2015 2605
## SEXETAF31M49 1.00 1549 2235
## SEXETAF50M64 1.00 1455 2601
## SEXETAF65P 1.00 1430 2238
##
## 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).Variabile | Coefficiente | IC 95% | Est.Error | OR | OR IC 95% |
|---|---|---|---|---|---|
condprfaltroauton. | 1.781*** | [0.786, 2.809] | 0.512 | 5.94 | [2.19, 16.59] |
condprfdirigenteDquadro | 0.109 | [-1.056, 1.264] | 0.601 | 1.12 | [0.35, 3.54] |
condprfimpiegato | 0.723 | [-0.122, 1.558] | 0.429 | 2.06 | [0.88, 4.75] |
condprfinsegnante | 0.081 | [-1.242, 1.359] | 0.669 | 1.08 | [0.29, 3.89] |
condprfoperaio | 1.415*** | [0.555, 2.319] | 0.443 | 4.12 | [1.74, 10.17] |
condprfdisoccupati | 1.931*** | [0.932, 2.922] | 0.512 | 6.90 | [2.54, 18.58] |
condprfstudente | 0.712 | [-0.426, 1.810] | 0.581 | 2.04 | [0.65, 6.11] |
condprfcasalinga | 1.593*** | [0.629, 2.541] | 0.492 | 4.92 | [1.88, 12.69] |
condprfpensionato | 0.874 | [-0.020, 1.782] | 0.459 | 2.40 | [0.98, 5.94] |
Q26contranquillità | 0.181 | [-0.702, 1.118] | 0.463 | 1.20 | [0.50, 3.06] |
Q26avvertodifficoltà | 0.756 | [-0.138, 1.718] | 0.466 | 2.13 | [0.87, 5.57] |
Q26arrivoafinemeseconmoltedifficoltà | 1.081*** | [0.152, 2.098] | 0.494 | 2.95 | [1.16, 8.15] |
Q26misentopoveroenonarrivomaiafinemese | 1.283*** | [0.205, 2.423] | 0.565 | 3.61 | [1.23, 11.28] |
Q26preferiscononrispondere | 2.336*** | [0.928, 3.868] | 0.763 | 10.34 | [2.53, 47.87] |
Q29Suburbano | -0.023 | [-0.409, 0.345] | 0.190 | 0.98 | [0.66, 1.41] |
Q29Rurale | -0.286 | [-0.741, 0.183] | 0.230 | 0.75 | [0.48, 1.20] |
Q30Intermedio | 0.077 | [-0.318, 0.454] | 0.197 | 1.08 | [0.73, 1.57] |
Q30Periferico | 0.402 | [-0.079, 0.877] | 0.241 | 1.49 | [0.92, 2.40] |
SEXETAM30M49 | 0.206 | [-0.440, 0.854] | 0.340 | 1.23 | [0.64, 2.35] |
SEXETAM50M64 | -0.639 | [-1.308, 0.032] | 0.343 | 0.53 | [0.27, 1.03] |
SEXETAM65P | -0.678 | [-1.437, 0.096] | 0.397 | 0.51 | [0.24, 1.10] |
SEXETAF18M30 | 0.108 | [-0.629, 0.821] | 0.374 | 1.11 | [0.53, 2.27] |
SEXETAF31M49 | 0.234 | [-0.422, 0.904] | 0.341 | 1.26 | [0.66, 2.47] |
SEXETAF50M64 | -0.284 | [-0.968, 0.384] | 0.346 | 0.75 | [0.38, 1.47] |
SEXETAF65P | -0.328 | [-1.073, 0.436] | 0.384 | 0.72 | [0.34, 1.55] |
## Stat value
## 1 Variance: SLL.2021 0.1089
## 2 ICC 0.0321
## 3 R2 Marginal 0.1496
## 4 R2 Conditional 0.1605
## 5 N_SLL.2021 33
## 6 Observations 1004## Livello SD N_gruppi
## 1 SLL.2021 0.3301 [0.05, 0.70] 33## # A tibble: 1 × 5
## ICC mean median q025 q975
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ICC latent 0.0384 0.0294 0.0008 0.1313.3. Modello Completo (Individuale + SLL + variabili di contesto)
Le variabili di contesto sono variabili con caratteristiche socio-demografiche ed economiche aggregare a livello di SLL
mod_ast_bayes_W_3 <- brm(
formula = Q19 | weights(weight) ~ condprf + Q26 + Q29+ Q30 +
SEXETA +
scale(Indice.di.Vecchiaia)+
scale(Indice.di.Dipendenza.Totale) +
scale(Reddito.IRPEF.resid) +
scale(Add.resid.2020) +
scale(VA.add.2020)+
scale(Add.tecno.2020)+
(1 | SLL.2021),
data = df.ast.SLL,
family = bernoulli(link = "logit"),
chains = 4, iter = 2000, cores = 4,
silent = 2
)## Family: bernoulli
## Links: mu = logit
## Formula: Q19 | weights(weight) ~ condprf + Q26 + Q29 + Q30 + SEXETA + scale(Indice.di.Vecchiaia) + scale(Indice.di.Dipendenza.Totale) + scale(Reddito.IRPEF.resid) + scale(Add.resid.2020) + scale(VA.add.2020) + scale(Add.tecno.2020) + (1 | SLL.2021)
## Data: df.ast.SLL (Number of observations: 1004)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Multilevel Hyperparameters:
## ~SLL.2021 (Number of levels: 33)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.32 0.18 0.02 0.73 1.00 888 1083
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI
## Intercept -1.42 0.64 -2.67 -0.22
## condprfaltroauton. 1.73 0.50 0.75 2.73
## condprfdirigenteDquadro 0.03 0.60 -1.16 1.19
## condprfimpiegato 0.71 0.41 -0.07 1.52
## condprfinsegnante 0.04 0.67 -1.30 1.33
## condprfoperaio 1.42 0.43 0.62 2.26
## condprfdisoccupati 1.85 0.50 0.87 2.82
## condprfstudente 0.76 0.55 -0.31 1.83
## condprfcasalinga 1.59 0.48 0.70 2.57
## condprfpensionato 0.82 0.45 -0.02 1.70
## Q26contranquillità 0.25 0.49 -0.69 1.22
## Q26avvertodifficoltà 0.81 0.49 -0.13 1.79
## Q26arrivoafinemeseconmoltedifficoltà 1.17 0.51 0.18 2.22
## Q26misentopoveroenonarrivomaiafinemese 1.38 0.58 0.24 2.57
## Q26preferiscononrispondere 2.43 0.77 0.99 4.00
## Q29Suburbano -0.05 0.19 -0.42 0.33
## Q29Rurale -0.30 0.23 -0.76 0.16
## Q30Intermedio 0.07 0.20 -0.33 0.47
## Q30Periferico 0.41 0.24 -0.06 0.88
## SEXETAM30M49 0.23 0.35 -0.47 0.90
## SEXETAM50M64 -0.60 0.34 -1.26 0.07
## SEXETAM65P -0.67 0.39 -1.46 0.11
## SEXETAF18M30 0.12 0.38 -0.63 0.87
## SEXETAF31M49 0.27 0.34 -0.42 0.95
## SEXETAF50M64 -0.27 0.34 -0.97 0.39
## SEXETAF65P -0.26 0.39 -1.03 0.50
## scaleIndice.di.Vecchiaia 0.43 0.23 -0.01 0.91
## scaleIndice.di.Dipendenza.Totale -0.66 0.23 -1.14 -0.22
## scaleReddito.IRPEF.resid 0.06 0.33 -0.60 0.71
## scaleAdd.resid.2020 -0.23 0.25 -0.73 0.26
## scaleVA.add.2020 0.10 0.18 -0.25 0.46
## scaleAdd.tecno.2020 -0.19 0.25 -0.68 0.31
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.00 1684 2414
## condprfaltroauton. 1.00 1499 2524
## condprfdirigenteDquadro 1.00 1891 2718
## condprfimpiegato 1.00 1264 2161
## condprfinsegnante 1.00 2345 2652
## condprfoperaio 1.00 1271 2110
## condprfdisoccupati 1.00 1487 2588
## condprfstudente 1.00 1758 2191
## condprfcasalinga 1.00 1419 2518
## condprfpensionato 1.00 1290 2226
## Q26contranquillità 1.00 1781 2495
## Q26avvertodifficoltà 1.00 1831 2453
## Q26arrivoafinemeseconmoltedifficoltà 1.00 1867 2599
## Q26misentopoveroenonarrivomaiafinemese 1.00 2131 2896
## Q26preferiscononrispondere 1.00 2712 2687
## Q29Suburbano 1.00 3839 3128
## Q29Rurale 1.00 3465 3295
## Q30Intermedio 1.00 3885 3017
## Q30Periferico 1.00 2806 2861
## SEXETAM30M49 1.00 1937 2705
## SEXETAM50M64 1.00 1829 2364
## SEXETAM65P 1.00 1840 2654
## SEXETAF18M30 1.00 2430 3028
## SEXETAF31M49 1.00 1966 2727
## SEXETAF50M64 1.00 1743 2282
## SEXETAF65P 1.00 1818 2596
## scaleIndice.di.Vecchiaia 1.00 2674 2040
## scaleIndice.di.Dipendenza.Totale 1.00 2953 2264
## scaleReddito.IRPEF.resid 1.00 2151 2265
## scaleAdd.resid.2020 1.00 2776 2725
## scaleVA.add.2020 1.00 2256 2233
## scaleAdd.tecno.2020 1.00 2394 2114
##
## 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).Variabile | Coefficiente | IC 95% | Est.Error | OR | OR IC 95% |
|---|---|---|---|---|---|
condprfaltroauton. | 1.730*** | [0.748, 2.726] | 0.505 | 5.64 | [2.11, 15.27] |
condprfdirigenteDquadro | 0.031 | [-1.162, 1.185] | 0.599 | 1.03 | [0.31, 3.27] |
condprfimpiegato | 0.712 | [-0.071, 1.515] | 0.412 | 2.04 | [0.93, 4.55] |
condprfinsegnante | 0.045 | [-1.295, 1.335] | 0.673 | 1.05 | [0.27, 3.80] |
condprfoperaio | 1.420*** | [0.624, 2.257] | 0.426 | 4.14 | [1.87, 9.55] |
condprfdisoccupati | 1.847*** | [0.874, 2.818] | 0.504 | 6.34 | [2.40, 16.75] |
condprfstudente | 0.763 | [-0.314, 1.835] | 0.552 | 2.14 | [0.73, 6.26] |
condprfcasalinga | 1.595*** | [0.703, 2.569] | 0.482 | 4.93 | [2.02, 13.06] |
condprfpensionato | 0.823 | [-0.020, 1.704] | 0.446 | 2.28 | [0.98, 5.49] |
Q26contranquillità | 0.246 | [-0.692, 1.220] | 0.486 | 1.28 | [0.50, 3.39] |
Q26avvertodifficoltà | 0.813 | [-0.130, 1.795] | 0.489 | 2.25 | [0.88, 6.02] |
Q26arrivoafinemeseconmoltedifficoltà | 1.173*** | [0.180, 2.218] | 0.515 | 3.23 | [1.20, 9.19] |
Q26misentopoveroenonarrivomaiafinemese | 1.376*** | [0.237, 2.568] | 0.585 | 3.96 | [1.27, 13.04] |
Q26preferiscononrispondere | 2.426*** | [0.989, 4.000] | 0.772 | 11.31 | [2.69, 54.57] |
Q29Suburbano | -0.047 | [-0.417, 0.328] | 0.191 | 0.95 | [0.66, 1.39] |
Q29Rurale | -0.296 | [-0.765, 0.155] | 0.234 | 0.74 | [0.47, 1.17] |
Q30Intermedio | 0.071 | [-0.328, 0.468] | 0.199 | 1.07 | [0.72, 1.60] |
Q30Periferico | 0.410 | [-0.059, 0.880] | 0.241 | 1.51 | [0.94, 2.41] |
SEXETAM30M49 | 0.232 | [-0.466, 0.901] | 0.345 | 1.26 | [0.63, 2.46] |
SEXETAM50M64 | -0.603 | [-1.264, 0.067] | 0.339 | 0.55 | [0.28, 1.07] |
SEXETAM65P | -0.666 | [-1.460, 0.106] | 0.394 | 0.51 | [0.23, 1.11] |
SEXETAF18M30 | 0.123 | [-0.632, 0.873] | 0.378 | 1.13 | [0.53, 2.39] |
SEXETAF31M49 | 0.272 | [-0.416, 0.946] | 0.345 | 1.31 | [0.66, 2.57] |
SEXETAF50M64 | -0.267 | [-0.975, 0.386] | 0.345 | 0.77 | [0.38, 1.47] |
SEXETAF65P | -0.261 | [-1.035, 0.505] | 0.386 | 0.77 | [0.36, 1.66] |
scaleIndice.di.Vecchiaia | 0.426 | [-0.014, 0.909] | 0.230 | 1.53 | [0.99, 2.48] |
scaleIndice.di.Dipendenza.Totale | -0.659*** | [-1.141, -0.224] | 0.234 | 0.52 | [0.32, 0.80] |
scaleReddito.IRPEF.resid | 0.060 | [-0.598, 0.708] | 0.327 | 1.06 | [0.55, 2.03] |
scaleAdd.resid.2020 | -0.229 | [-0.733, 0.256] | 0.253 | 0.80 | [0.48, 1.29] |
scaleVA.add.2020 | 0.099 | [-0.250, 0.460] | 0.179 | 1.10 | [0.78, 1.58] |
scaleAdd.tecno.2020 | -0.186 | [-0.676, 0.312] | 0.247 | 0.83 | [0.51, 1.37] |
## Stat value
## 1 Variance: SLL.2021 0.0995
## 2 ICC 0.0293
## 3 R2 Marginal 0.1643
## 4 R2 Conditional 0.1694
## 5 N_SLL.2021 33
## 6 Observations 1004## Livello SD N_gruppi
## 1 SLL.2021 0.3154 [0.02, 0.73] 33## # A tibble: 1 × 5
## ICC mean median q025 q975
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ICC latent 0.0369 0.0255 0.0002 0.138