Interval kepercayaan untuk model campuran rintangan atau nol yang meningkat

Saya ingin menghitung CI dalam model campuran, binomial negatif nol, dan model rintangan. Kode saya untuk model rintangan terlihat seperti ini (x1, x2 kontinu, x3 kategorikal):

m1 <- glmmTMB(count~x1+x2+x3+(1|year/class),
          data = bd, zi = ~x2+x3+(1|year/class), family = truncated_nbinom2,
          )

Saya menggunakan confint, dan saya mendapatkan hasil ini:

ci <- confint(m1,parm="beta_")
ci
                                          2.5 %       97.5 %     Estimate
cond.(Intercept)                   1.816255e-01  0.448860094  0.285524861
cond.x1                            9.045278e-01  0.972083366  0.937697401
cond.x2                            1.505770e+01 26.817439186 20.094998772
cond.x3high                        1.190972e+00  1.492335046  1.333164894
cond.x3low                         1.028147e+00  1.215828654  1.118056377
cond.x3reg                         1.135515e+00  1.385833853  1.254445909
class:year.cond.Std.Dev.(Intercept)2.256324e+00  2.662976154  2.441845815
year.cond.Std.Dev.(Intercept)      1.051889e+00  1.523719169  1.157153015
zi.(Intercept)                     1.234418e-04  0.001309705  0.000402085
zi.x2                              2.868578e-02  0.166378014  0.069084606
zi.x3high                          8.972025e-01  1.805832900  1.272869874

Apakah saya menghitung intervalnya dengan benar? Mengapa hanya ada satu kategori di x3 untuk zi? Jika memungkinkan, saya juga ingin tahu apakah CI ini dapat diplot.

Terima kasih!

Datanya terlihat seperti ini:

     class id year count  x1      x2        x3 
956    5 3002 2002      3 15.6    47.9      high
957    5 4004 2002      3 14.3    47.9      low
958    5 6021 2002      3 14.2    47.9      high
959    4 2030 2002      3 10.5    46.3      high
960    4 2031 2002      3 15.3    46.3      high
961    4 2034 2002      3 15.2    46.3      reg

dengan x1 dan x2 kontinu, x3 variabel kategori tiga tingkat (faktor)

Ringkasan model:

summary(m1)
'giveCsparse' has been deprecated; setting 'repr = "T"' for you'giveCsparse' has been deprecated; setting 'repr = "T"' for you'giveCsparse' has been deprecated; setting 'repr = "T"' for you
 Family: truncated_nbinom2  ( log )
Formula:          count ~ x1 + x2 + x3 + (1 | year/class)
Zero inflation:          ~x2 + x3 + (1 | year/class)
Data: bd

     AIC      BIC   logLik deviance df.resid 
 37359.7  37479.7 -18663.8  37327.7    13323 

Random effects:

Conditional model:
 Groups    Name        Variance Std.Dev.
 class:year(Intercept) 0.79701  0.8928  
 year      (Intercept) 0.02131  0.1460  
Number of obs: 13339, groups:  class:year, 345; year, 15

Zero-inflation model:
 Groups    Name        Variance  Std.Dev. 
 dpto:year (Intercept) 1.024e+02 1.012e+01
 year      (Intercept) 7.842e-07 8.856e-04
Number of obs: 13339, groups:  class:year, 345; year, 15

Overdispersion parameter for truncated_nbinom2 family (): 1.02 

Conditional model:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -1.25343    0.23081  -5.431 5.62e-08 ***
x1             -0.06433    0.01837  -3.501 0.000464 ***
x2              3.00047    0.14724  20.378  < 2e-16 ***
x3high          0.28756    0.05755   4.997 5.82e-07 ***
x3low           0.11159    0.04277   2.609 0.009083 ** 
x3reg           0.22669    0.05082   4.461 8.17e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Zero-inflation model:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)     -7.8188     0.6025 -12.977  < 2e-16 ***
x2              -2.6724     0.4484  -5.959 2.53e-09 ***
x3high           0.2413     0.1784   1.352  0.17635    
x3low           -0.1325     0.1134  -1.169  0.24258    
x3reg           -0.3806     0.1436  -2.651  0.00802 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

CI dengan sapu.campuran

> broom.mixed::tidy(m1, effects="fixed", conf.int=TRUE)
# A tibble: 12 x 9
   effect component term           estimate std.error statistic  p.value conf.low conf.high
   <chr>  <chr>     <chr>             <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
 1 fixed  cond      (Intercept)     -1.25      0.231      -5.43 5.62e- 8  -1.71     -0.801 
 2 fixed  cond      x1              -0.0643    0.0184     -3.50 4.64e- 4  -0.100    -0.0283
 3 fixed  cond      x2               3.00      0.147      20.4  2.60e-92   2.71      3.29  
 4 fixed  cond      x3high           0.288     0.0575      5.00 5.82e- 7   0.175     0.400 
 5 fixed  cond      x3low            0.112     0.0428      2.61 9.08e- 3   0.0278    0.195 
 6 fixed  cond      x3reg            0.227     0.0508      4.46 8.17e- 6   0.127     0.326 
 7 fixed  zi        (Intercept)     -9.88      1.32       -7.49 7.04e-14 -12.5      -7.30  
 8 fixed  zi        x1               0.214     0.120       1.79 7.38e- 2  -0.0206    0.448 
 9 fixed  zi        x2              -2.69      0.449      -6.00 2.01e- 9  -3.57     -1.81  
10 fixed  zi        x3high           0.232     0.178       1.30 1.93e- 1  -0.117     0.582 
11 fixed  zi        x3low           -0.135     0.113      -1.19 2.36e- 1  -0.357     0.0878
12 fixed  zi        x4reg           -0.382     0.144      -2.66 7.74e- 3  -0.664    -0.101