我正在模拟一年中的工作数量，并随机截取以考虑区域差异。使用模型进行预测/推理时如何解释模型输出？
例如，使用 log-link 函数，我将执行以下操作来计算伦敦第 2 年的预期工作数量：
exp(Intercept + Year*2 + London_intercept) = exp(0.23290 + -0.13369*2 + 0.42820729) = 1.482496
但这个值太低了，我预计一年内的工作岗位将超过 1.5 个。我是否将其解释为比平均值增加 70%？如果是这样是什么意思？有人可以澄清该怎么做吗？
总结输出：
> summary(m8)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: Negative Binomial(0.9321) ( log )
Formula: Jobs ~ 1 + Year + (1 | Region)
Data: df_jobs
AIC BIC logLik deviance df.resid
3554.5 3575.9 -1773.3 3546.5 1564
Scaled residuals:
Min 1Q Median 3Q Max
-0.7836 -0.6187 -0.5081 0.3613 7.2595
Random effects:
Groups Name Variance Std.Dev.
Region (Intercept) 0.1597 0.3996
Number of obs: 1568, groups: Region, 14
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.23290 0.13610 1.711 0.087 .
Year -0.13369 0.01572 -8.502 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
Year -0.536
区域的随机截距：
$Region
(Intercept)
East Midlands -0.02413869
East of England 0.18654921
Kent, Surrey & Sussex 0.14610941
London 0.42820729
North East -0.42892509
North West 0.48852281
Northern Ireland -0.26330415
Scotland 0.41420383
South West 0.15415043
Thames Valley -0.80072297
Wales -0.05975189
Wessex -0.53037342
West Midlands 0.15126500
Yorkshire & Humber 0.21339728