我正在使用glmer()lme4 包中的函数来使用泊松分布运行 GLMM。在我看到的所有示例中，输出的随机效应部分都有一个从数据估计的残差部分（在下面的示例中，两侧被 2 个星号包围）。然后可以使用此信息来解释随机效应解释的变化量。这是一个例子：
> summary(M1)
Linear mixed model fit by REML
Formula: Richness ~ NAP * fExp + (1 | fBeach)
Data: RIKZ
AIC BIC logLik deviance REMLdev
236.5 247.3 -112.2 230.3 224.5
Random effects:
Groups Name Variance Std.Dev.
fBeach (Intercept) 3.3072 1.8186
**Residual 8.6605 2.9429**
Number of obs: 45, groups: fBeach, 9
Fixed effects:
Estimate Std. Error t value
(Intercept) 8.8611 1.0208 8.681
NAP -3.4637 0.6279 -5.517
fExp11 -5.2556 1.5451 -3.401
NAP:fExp11 2.0005 0.9461 2.114
Correlation of Fixed Effects:
(Intr) NAP fExp11
NAP -0.181
fExp11 -0.661 0.120
NAP:fExp11 0.120 -0.664 -0.221
但是，当我使用自己的数据时，我得到的输出不包含此信息，我不知道为什么。我想知道我的随机效应解释了多少变化，但不知道如何访问回答问题所需的信息。有什么线索吗？这是数据/统计问题还是知道如何访问信息问题？如果我问错地方了，我深表歉意。我得到的输出类似于以下输出：
Generalized linear mixed model fit by the Laplace approximation
Formula: y ~ z.score(x1) + z.score(x2) + z.score(x3) + z.score(x4) + z.score(x5) + z.score(x6) + (1 | RE)
Data: p
AIC BIC logLik deviance
419.5 454.7 -201.8 403.5
Random effects:
Groups Name Variance Std.Dev.
RE (Intercept) 0.021605 0.14699
Number of obs: 600, groups: RE, 40
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.70591 0.02911 58.60 < 2e-16 ***
z.score(x1) 0.19087 0.03595 5.31 1.10e-07 ***
z.score(x2) -0.14302 0.04083 -3.50 0.000460 ***
z.score(x3) -0.16562 0.04020 -4.12 3.79e-05 ***
z.score(x4) 0.13229 0.03425 3.86 0.000112 ***
z.score(x5) -0.10588 0.03985 -2.66 0.007885 **
z.score(x6) 0.17600 0.05798 3.04 0.002401 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) z.(x1) z.(x2 z.s(x3) z.(x4 z.(x5
z.scr(x1) -0.051
z.s(x2) 0.038 0.259
z.scr(x3) 0.045 0.156 0.113
z.(x4 -0.040 0.144 -0.052 0.044
z.(x5 0.026 -0.368 -0.339 -0.072 -0.073
z.scor(x6) -0.031 -0.020 0.002 -0.143 -0.004 0.004
这是一些示例数据，以适应glmer(y ~ x1 + (1|RE), data=d, family=poisson).
d <- data.frame(
y = c(3, 5, 2, 6, 3, 7, 2, 3, 0, 4, 0,10, 1, 4, 0, 4, 2, 3, 0, 6,
3, 4, 2, 3, 2, 3, 3, 4, 0, 5, 6, 5, 4, 4, 0, 3, 1, 6, 0, 3, 2,
2, 1, 6, 2, 7, 0, 2, 0, 4, 0, 6, 4, 5, 1, 5, 1, 4, 1, 2, 3, 6,
6, 7, 0, 5, 0, 9, 1, 4, 5, 6, 1, 7, 1, 4, 1, 4, 0, 4, 1, 6, 1,
4, 0, 7, 1, 4, 0, 6, 0, 7, 2, 6, 0, 6, 1, 5, 0, 4, 1, 7, 2, 4,
1, 5, 1, 7, 2, 5, 0, 4, 3, 5, 1, 4, 0, 3, 0, 6, 0, 8, 3, 9, 0,
2, 3, 8, 0, 1, 0, 3, 0, 5, 0, 4, 4, 5, 0, 5, 1, 5, 3, 5, 1, 4,
3, 4, 4, 4, 4, 4, 4, 7, 1, 8, 1, 4, 0, 2, 2, 5, 1, 4, 1, 5, 1,
4, 2, 4, 2, 4, 0, 6, 1, 6, 0, 6, 1, 2, 1, 3, 1, 8, 1, 6, 1, 6,
0, 6, 1, 6, 2, 6, 2, 4, 0, 1, 1, 1, 1, 6, 5, 5, 1, 5, 2, 4, 2,
6, 1, 7, 1, 8, 2, 8, 1, 8, 2, 4, 1, 7, 3, 6, 4, 7, 3, 7, 1, 6,
3, 5, 1,10, 1, 7, 2, 5, 1, 5, 0, 6, 1, 8, 4, 7, 1, 6, 1, 9,
0, 9, 1, 3, 2, 5, 2, 9, 3, 5, 0, 2, 2, 3, 0, 5, 0, 5, 0, 4, 3,
6, 1,10, 2, 8, 0, 6, 0, 4, 2, 6, 2, 4, 2, 6, 1, 4, 0, 5, 2,
6, 1, 5, 2, 5, 1, 5, 1, 5),
x1 = rep(c(0.1008, 0.0511, 0.1792, 1.0345), c(80, 80, 80, 60)),
RE = rep(c(37, 88, 139, 190, 241, 292, 343, 394, 91, 142, 193, 244, 295,
346, 397, 40, 94, 145, 43, 196, 247, 298, 349, 400, 301, 352,
403, 250, 148, 199, 46, 97, 355, 406, 253, 304, 49, 100, 151,
202, 37, 88, 139, 190, 241, 292, 343, 394, 91, 142, 193, 244,
295, 346, 397, 40, 43, 94, 145, 247, 298, 349, 196, 400, 199,
250, 301, 352, 406, 46, 97, 148, 403, 49, 100, 151, 202, 253,
304, 355, 37, 88, 139, 190, 241, 292, 343, 394, 193, 244, 346,
397, 295, 40, 91, 142, 43, 94, 145, 196, 46, 97, 148, 151, 247,
400, 298, 349, 352, 199, 250, 301, 403, 253, 304, 355, 202, 406,
49, 100, 37, 88, 139, 190, 241, 292, 343, 394, 346, 397, 193,
244, 295, 40, 91, 142, 43, 94, 145, 196, 247, 298, 349, 400,
97, 148, 46, 199, 250, 301), each=2)
)