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我想在混合模型、零膨胀负二项式和障碍模型中计算 CI。我的障碍模型代码如下所示（x1，x2 连续，x3 分类）：

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

我用过confint，我得到了这些结果：

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

我是否正确计算了间隔？为什么 zi 在 x3 中只有一个类别？如果可能的话，我还想知道是否可以绘制这些 CI。

谢谢！

数据如下所示：

     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

x1 和 x2 连续，x3 三级分类变量（因子）

模型总结：

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 with broom.mixed

> 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
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1 回答 1

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tl;据我所知，这是一个错误confint.glmmTMB（可能在内部函数中glmmTMB:::getParms）。同时，broom.mixed::tidy(m1, effects="fixed")应该做你想做的事。（现在 GitHub 上的开发版本正在进行修复，是否应该在某个时候将其提交给 CRAN？很快......）

可重现的例子：

设置数据

set.seed(101)
n <- 1e3
bd <- data.frame(
    year=factor(sample(2002:2018, size=n, replace=TRUE)),
    class=factor(sample(1:20, size=n, replace=TRUE)),
    x1 = rnorm(n),
    x2 = rnorm(n),
    x3 = factor(sample(c("low","reg","high"), size=n, replace=TRUE),
                levels=c("low","reg","high")),
    count = rnbinom(n, mu = 3, size=1))

合身

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

置信区间

confint(m1, "beta_")  ## wrong/ incomplete
broom.mixed::tidy(m1, effects="fixed", conf.int=TRUE)  ## correct

您可能需要考虑需要哪种置信区间：

  • Wald CI（默认）的计算速度要快得多，并且只要（1）您的数据集很大并且（2）您没有估计任何接近边界的 log/logit 尺度上的参数，通常都可以
  • 可能性轮廓 CI 更准确但速度较慢
于 2021-04-12T22:44:30.213 回答