GLMER 警告:方差-协方差矩阵 [...] 不是正定的或包含 NA 值
GLMER warning: variance-covariance matrix [...] is not positive definite or contains NA values
我有时发现我的 glmer
包 lme4
中的 GLMM 在调用摘要时显示以下警告消息:
Warning messages:
1: In vcov.merMod(object, use.hessian = use.hessian) :
variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov estimated from RX
2: In vcov.merMod(object, correlation = correlation, sigm = sig) :
variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov estimated from RX
我在 Whosebug 上发现的类似问题指的是其他函数,而不是 glmer,并且 LME4 Wiki does not elaborate on that either. In this question, the problem was solved before that kind of error messages were tackled, and here 讨论侧重于特定模型而不是警告消息的含义。
所以问题是:我应该担心那个消息,还是它可以,因为它只是一个警告而不是错误,而且正如它所说,它是 "falling back to var-cov estimated from RX"(无论 RX 是什么) .
有趣的是,虽然摘要指出模型无法收敛,但我没有收到通常的红色收敛警告。
这是一个(最小)数据集:
testdata=structure(list(Site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("EO1", "EO2",
"EO3", "EO4", "EO5", "EO6"), class = "factor"), Treatment = structure(c(1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L), .Label = c("control",
"no ants", "no birds", "no birds no ants"), class = "factor"),
Tree = structure(c(2L, 3L, 4L, 16L, 12L, 13L, 14L, 15L, 5L,
6L, 7L, 8L, 1L, 9L, 10L, 11L, 28L, 29L, 30L, 31L, 17L, 25L,
26L, 27L, 18L, 19L, 20L, 32L, 21L, 22L, 23L, 24L, 33L, 41L,
42L, 43L, 37L, 38L, 39L, 40L, 44L, 45L, 46L, 47L, 34L, 35L,
36L, 48L, 49L, 57L, 58L, 59L, 50L, 51L, 52L, 64L, 53L, 54L,
55L, 56L, 60L, 61L, 62L, 63L, 66L, 67L, 68L, 80L, 69L, 70L,
71L, 72L, 76L, 77L, 78L, 79L, 65L, 73L, 74L, 75L, 82L, 83L,
84L, 96L, 92L, 93L, 94L, 95L, 85L, 86L, 87L, 88L, 81L, 89L,
90L, 91L), .Label = c("EO1 1", "EO1 10", "EO1 11", "EO1 12",
"EO1 13", "EO1 14", "EO1 15", "EO1 16", "EO1 2", "EO1 3",
"EO1 4", "EO1 5", "EO1 6", "EO1 7", "EO1 8", "EO1 9", "EO2 1",
"EO2 10", "EO2 11", "EO2 12", "EO2 13", "EO2 14", "EO2 15",
"EO2 16", "EO2 2", "EO2 3", "EO2 4", "EO2 5", "EO2 6", "EO2 7",
"EO2 8", "EO2 9", "EO3 1", "EO3 10", "EO3 11", "EO3 12",
"EO3 13", "EO3 14", "EO3 15", "EO3 16", "EO3 2", "EO3 3",
"EO3 4", "EO3 5", "EO3 6", "EO3 7", "EO3 8", "EO3 9", "EO4 1",
"EO4 10", "EO4 11", "EO4 12", "EO4 13", "EO4 14", "EO4 15",
"EO4 16", "EO4 2", "EO4 3", "EO4 4", "EO4 5", "EO4 6", "EO4 7",
"EO4 8", "EO4 9", "EO5 1", "EO5 10", "EO5 11", "EO5 12",
"EO5 13", "EO5 14", "EO5 15", "EO5 16", "EO5 2", "EO5 3",
"EO5 4", "EO5 5", "EO5 6", "EO5 7", "EO5 8", "EO5 9", "EO6 1",
"EO6 10", "EO6 11", "EO6 12", "EO6 13", "EO6 14", "EO6 15",
"EO6 16", "EO6 2", "EO6 3", "EO6 4", "EO6 5", "EO6 6", "EO6 7",
"EO6 8", "EO6 9"), class = "factor"), predators_trunk = c(7L,
10L, 9L, 15L, 18L, 11L, 5L, 7L, 15L, 12L, 6L, 12L, 7L, 13L,
24L, 17L, 3L, 0L, 0L, 2L, 4L, 3L, 0L, 6L, 2L, 3L, 5L, 1L,
5L, 12L, 18L, 15L, 7L, 0L, 5L, 1L, 17L, 7L, 13L, 19L, 7L,
3L, 5L, 10L, 11L, 7L, 13L, 7L, 7L, 0L, 4L, 2L, 5L, 7L, 4L,
7L, 8L, 7L, 9L, 20L, 13L, 2L, 12L, 7L, 0L, 7L, 2L, 2L, 2L,
4L, 17L, 2L, 3L, 1L, 1L, 1L, 11L, 1L, 1L, 8L, 8L, 18L, 5L,
6L, 6L, 5L, 6L, 5L, 9L, 2L, 8L, 13L, 13L, 5L, 3L, 5L), pH_H2O = c(4.145,
4.145, 4.145, 4.145, 4.1825, 4.1825, 4.1825, 4.1825, 4.1325,
4.1325, 4.1325, 4.1325, 4.14125, 4.14125, 4.14125, 4.14125,
4.265, 4.265, 4.265, 4.265, 4.21, 4.21, 4.21, 4.21, 4.18375,
4.18375, 4.18375, 4.18375, 4.09625, 4.09625, 4.09625, 4.09625,
4.1575, 4.1575, 4.1575, 4.1575, 4.1125, 4.1125, 4.1125, 4.1125,
4.20875, 4.20875, 4.20875, 4.20875, 3.97125, 3.97125, 3.97125,
3.97125, 4.025, 4.025, 4.025, 4.025, 4.005, 4.005, 4.005,
4.005, 4.04, 4.04, 4.04, 4.04, 4.03125, 4.03125, 4.03125,
4.03125, 4.4575, 4.4575, 4.4575, 4.4575, 4.52, 4.52, 4.52,
4.52, 4.505, 4.505, 4.505, 4.505, 4.34875, 4.34875, 4.34875,
4.34875, 4.305, 4.305, 4.305, 4.305, 4.32, 4.32, 4.32, 4.32,
4.35, 4.35, 4.35, 4.35, 4.445, 4.445, 4.445, 4.445), ant_mean_abundance = c(53.85714,
53.85714, 53.85714, 53.85714, 24.28571, 24.28571, 24.28571,
24.28571, 45.5, 45.5, 45.5, 45.5, 51.14286, 51.14286, 51.14286,
51.14286, 66.28571, 66.28571, 66.28571, 66.28571, 76.5, 76.5,
76.5, 76.5, 65.71429, 65.71429, 65.71429, 65.71429, 8.642857,
8.642857, 8.642857, 8.642857, 109.3571, 109.3571, 109.3571,
109.3571, 25.14286, 25.14286, 25.14286, 25.14286, 101.3571,
101.3571, 101.3571, 101.3571, 31.78571, 31.78571, 31.78571,
31.78571, 78.64286, 78.64286, 78.64286, 78.64286, 93.28571,
93.28571, 93.28571, 93.28571, 63.14286, 63.14286, 63.14286,
63.14286, 67.14286, 67.14286, 67.14286, 67.14286, 44.0625,
44.0625, 44.0625, 44.0625, 23.875, 23.875, 23.875, 23.875,
95.8125, 95.8125, 95.8125, 95.8125, 49.125, 49.125, 49.125,
49.125, 57, 57, 57, 57, 38.125, 38.125, 38.125, 38.125, 40.6875,
40.6875, 40.6875, 40.6875, 22, 22, 22, 22), bird_activity = c(153.24,
153.24, 153.24, 153.24, 153.24, 153.24, 153.24, 153.24, 0,
0, 0, 0, 0, 0, 0, 0, 240.96, 240.96, 240.96, 240.96, 240.96,
240.96, 240.96, 240.96, 0, 0, 0, 0, 0, 0, 0, 0, 154.54, 154.54,
154.54, 154.54, 154.54, 154.54, 154.54, 154.54, 0, 0, 0,
0, 0, 0, 0, 0, 107.68, 107.68, 107.68, 107.68, 107.68, 107.68,
107.68, 107.68, 0, 0, 0, 0, 0, 0, 0, 0, 172.42, 172.42, 172.42,
172.42, 172.42, 172.42, 172.42, 172.42, 0, 0, 0, 0, 0, 0,
0, 0, 113.8, 113.8, 113.8, 113.8, 113.8, 113.8, 113.8, 113.8,
0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("Site", "Treatment",
"Tree", "predators_trunk", "pH_H2O", "ant_mean_abundance", "bird_activity"
), class = "data.frame", row.names = c(NA, -96L))
这里是导致警告的代码:
library(lme4)
summary(glmer.nb(predators_trunk ~ scale(ant_mean_abundance) + scale(bird_activity) + scale(pH_H2O) + (1 | Site/Treatment), testdata, na.action = na.fail))
summary(glmer(predators_trunk ~ scale(ant_mean_abundance) + scale(bird_activity) + scale(pH_H2O) + (1 | Site/Treatment), testdata, family = negative.binomial(theta = 4.06643400243645), na.action = na.fail))
对我来说有趣的是,glmer.nb
的摘要没有产生任何警告,但是使用 glmer.nb
估计的 theta 调用 glmer
确实给了我警告。后者是在相应的glmer.nb
完整模型上使用dredge
(MuMIn)生成的模型调用。
此警告表明您的标准误差估计 可能 不太准确。但与所有警告一样,很难确定,最好的办法是尝试交叉检查是否可以。
在这种情况下,我将 glmer.nb
和 glmer
中的两个拟合保存为 g1
和 g2
。您可以看到估计值(点估计值、SE、Z 值...)发生了一些变化,但变化不大,所以至少应该让您放心。
printCoefmat(coef(summary(g1)),digits=2)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.844 0.111 16.7 <2e-16 ***
scale(ant_mean_abundance) -0.347 0.077 -4.5 7e-06 ***
scale(bird_activity) -0.122 0.076 -1.6 0.107
scale(pH_H2O) -0.275 0.104 -2.6 0.008 **
> printCoefmat(coef(summary(g2)),digits=2)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.846 0.108 17.1 <2e-16 ***
scale(ant_mean_abundance) -0.347 0.077 -4.5 6e-06 ***
scale(bird_activity) -0.122 0.075 -1.6 0.102
scale(pH_H2O) -0.275 0.102 -2.7 0.007 **
我在 Github(test_mods
分支上有一个 lme4
的开发版本,希望很快集成到 master 分支:如果你想安装它,你可以使用 devtools::install_github("lme4/lme4",ref="test_mods")
) 允许您为标准误差选择更准确(但更慢)的计算:这使我们回到(几乎)与 glmer.nb
.
相同的标准误差
g3 <- update(g2, control=glmerControl(deriv.method="Richardson"))
printCoefmat(coef(summary(g3)),digits=2)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.846 0.111 16.7 <2e-16 ***
scale(ant_mean_abundance) -0.347 0.077 -4.5 6e-06 ***
scale(bird_activity) -0.122 0.076 -1.6 0.106
scale(pH_H2O) -0.275 0.104 -2.6 0.008 **
all.equal(coef(summary(g1))[,"Std. Error"],
coef(summary(g3))[,"Std. Error"])
[1] "Mean relative difference: 0.001597978"
glmmTMB
包(在 Github 上)也给出几乎相同的结果:
library(glmmTMB)
g5 <- glmmTMB(predators_trunk ~ scale(ant_mean_abundance) +
scale(bird_activity) + scale(pH_H2O) +
(1 | Site/Treatment), testdata,
family=nbinom2)
printCoefmat(coef(summary(g5))[["cond"]],digits=2)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.852 0.110 16.8 <2e-16 ***
scale(ant_mean_abundance) -0.348 0.077 -4.5 7e-06 ***
scale(bird_activity) -0.123 0.076 -1.6 0.106
scale(pH_H2O) -0.276 0.105 -2.6 0.008 **
我有时发现我的 glmer
包 lme4
中的 GLMM 在调用摘要时显示以下警告消息:
Warning messages:
1: In vcov.merMod(object, use.hessian = use.hessian) :
variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov estimated from RX
2: In vcov.merMod(object, correlation = correlation, sigm = sig) :
variance-covariance matrix computed from finite-difference Hessian is
not positive definite or contains NA values: falling back to var-cov estimated from RX
我在 Whosebug 上发现的类似问题指的是其他函数,而不是 glmer,并且 LME4 Wiki does not elaborate on that either. In this question, the problem was solved before that kind of error messages were tackled, and here 讨论侧重于特定模型而不是警告消息的含义。
所以问题是:我应该担心那个消息,还是它可以,因为它只是一个警告而不是错误,而且正如它所说,它是 "falling back to var-cov estimated from RX"(无论 RX 是什么) .
有趣的是,虽然摘要指出模型无法收敛,但我没有收到通常的红色收敛警告。
这是一个(最小)数据集:
testdata=structure(list(Site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("EO1", "EO2",
"EO3", "EO4", "EO5", "EO6"), class = "factor"), Treatment = structure(c(1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L), .Label = c("control",
"no ants", "no birds", "no birds no ants"), class = "factor"),
Tree = structure(c(2L, 3L, 4L, 16L, 12L, 13L, 14L, 15L, 5L,
6L, 7L, 8L, 1L, 9L, 10L, 11L, 28L, 29L, 30L, 31L, 17L, 25L,
26L, 27L, 18L, 19L, 20L, 32L, 21L, 22L, 23L, 24L, 33L, 41L,
42L, 43L, 37L, 38L, 39L, 40L, 44L, 45L, 46L, 47L, 34L, 35L,
36L, 48L, 49L, 57L, 58L, 59L, 50L, 51L, 52L, 64L, 53L, 54L,
55L, 56L, 60L, 61L, 62L, 63L, 66L, 67L, 68L, 80L, 69L, 70L,
71L, 72L, 76L, 77L, 78L, 79L, 65L, 73L, 74L, 75L, 82L, 83L,
84L, 96L, 92L, 93L, 94L, 95L, 85L, 86L, 87L, 88L, 81L, 89L,
90L, 91L), .Label = c("EO1 1", "EO1 10", "EO1 11", "EO1 12",
"EO1 13", "EO1 14", "EO1 15", "EO1 16", "EO1 2", "EO1 3",
"EO1 4", "EO1 5", "EO1 6", "EO1 7", "EO1 8", "EO1 9", "EO2 1",
"EO2 10", "EO2 11", "EO2 12", "EO2 13", "EO2 14", "EO2 15",
"EO2 16", "EO2 2", "EO2 3", "EO2 4", "EO2 5", "EO2 6", "EO2 7",
"EO2 8", "EO2 9", "EO3 1", "EO3 10", "EO3 11", "EO3 12",
"EO3 13", "EO3 14", "EO3 15", "EO3 16", "EO3 2", "EO3 3",
"EO3 4", "EO3 5", "EO3 6", "EO3 7", "EO3 8", "EO3 9", "EO4 1",
"EO4 10", "EO4 11", "EO4 12", "EO4 13", "EO4 14", "EO4 15",
"EO4 16", "EO4 2", "EO4 3", "EO4 4", "EO4 5", "EO4 6", "EO4 7",
"EO4 8", "EO4 9", "EO5 1", "EO5 10", "EO5 11", "EO5 12",
"EO5 13", "EO5 14", "EO5 15", "EO5 16", "EO5 2", "EO5 3",
"EO5 4", "EO5 5", "EO5 6", "EO5 7", "EO5 8", "EO5 9", "EO6 1",
"EO6 10", "EO6 11", "EO6 12", "EO6 13", "EO6 14", "EO6 15",
"EO6 16", "EO6 2", "EO6 3", "EO6 4", "EO6 5", "EO6 6", "EO6 7",
"EO6 8", "EO6 9"), class = "factor"), predators_trunk = c(7L,
10L, 9L, 15L, 18L, 11L, 5L, 7L, 15L, 12L, 6L, 12L, 7L, 13L,
24L, 17L, 3L, 0L, 0L, 2L, 4L, 3L, 0L, 6L, 2L, 3L, 5L, 1L,
5L, 12L, 18L, 15L, 7L, 0L, 5L, 1L, 17L, 7L, 13L, 19L, 7L,
3L, 5L, 10L, 11L, 7L, 13L, 7L, 7L, 0L, 4L, 2L, 5L, 7L, 4L,
7L, 8L, 7L, 9L, 20L, 13L, 2L, 12L, 7L, 0L, 7L, 2L, 2L, 2L,
4L, 17L, 2L, 3L, 1L, 1L, 1L, 11L, 1L, 1L, 8L, 8L, 18L, 5L,
6L, 6L, 5L, 6L, 5L, 9L, 2L, 8L, 13L, 13L, 5L, 3L, 5L), pH_H2O = c(4.145,
4.145, 4.145, 4.145, 4.1825, 4.1825, 4.1825, 4.1825, 4.1325,
4.1325, 4.1325, 4.1325, 4.14125, 4.14125, 4.14125, 4.14125,
4.265, 4.265, 4.265, 4.265, 4.21, 4.21, 4.21, 4.21, 4.18375,
4.18375, 4.18375, 4.18375, 4.09625, 4.09625, 4.09625, 4.09625,
4.1575, 4.1575, 4.1575, 4.1575, 4.1125, 4.1125, 4.1125, 4.1125,
4.20875, 4.20875, 4.20875, 4.20875, 3.97125, 3.97125, 3.97125,
3.97125, 4.025, 4.025, 4.025, 4.025, 4.005, 4.005, 4.005,
4.005, 4.04, 4.04, 4.04, 4.04, 4.03125, 4.03125, 4.03125,
4.03125, 4.4575, 4.4575, 4.4575, 4.4575, 4.52, 4.52, 4.52,
4.52, 4.505, 4.505, 4.505, 4.505, 4.34875, 4.34875, 4.34875,
4.34875, 4.305, 4.305, 4.305, 4.305, 4.32, 4.32, 4.32, 4.32,
4.35, 4.35, 4.35, 4.35, 4.445, 4.445, 4.445, 4.445), ant_mean_abundance = c(53.85714,
53.85714, 53.85714, 53.85714, 24.28571, 24.28571, 24.28571,
24.28571, 45.5, 45.5, 45.5, 45.5, 51.14286, 51.14286, 51.14286,
51.14286, 66.28571, 66.28571, 66.28571, 66.28571, 76.5, 76.5,
76.5, 76.5, 65.71429, 65.71429, 65.71429, 65.71429, 8.642857,
8.642857, 8.642857, 8.642857, 109.3571, 109.3571, 109.3571,
109.3571, 25.14286, 25.14286, 25.14286, 25.14286, 101.3571,
101.3571, 101.3571, 101.3571, 31.78571, 31.78571, 31.78571,
31.78571, 78.64286, 78.64286, 78.64286, 78.64286, 93.28571,
93.28571, 93.28571, 93.28571, 63.14286, 63.14286, 63.14286,
63.14286, 67.14286, 67.14286, 67.14286, 67.14286, 44.0625,
44.0625, 44.0625, 44.0625, 23.875, 23.875, 23.875, 23.875,
95.8125, 95.8125, 95.8125, 95.8125, 49.125, 49.125, 49.125,
49.125, 57, 57, 57, 57, 38.125, 38.125, 38.125, 38.125, 40.6875,
40.6875, 40.6875, 40.6875, 22, 22, 22, 22), bird_activity = c(153.24,
153.24, 153.24, 153.24, 153.24, 153.24, 153.24, 153.24, 0,
0, 0, 0, 0, 0, 0, 0, 240.96, 240.96, 240.96, 240.96, 240.96,
240.96, 240.96, 240.96, 0, 0, 0, 0, 0, 0, 0, 0, 154.54, 154.54,
154.54, 154.54, 154.54, 154.54, 154.54, 154.54, 0, 0, 0,
0, 0, 0, 0, 0, 107.68, 107.68, 107.68, 107.68, 107.68, 107.68,
107.68, 107.68, 0, 0, 0, 0, 0, 0, 0, 0, 172.42, 172.42, 172.42,
172.42, 172.42, 172.42, 172.42, 172.42, 0, 0, 0, 0, 0, 0,
0, 0, 113.8, 113.8, 113.8, 113.8, 113.8, 113.8, 113.8, 113.8,
0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("Site", "Treatment",
"Tree", "predators_trunk", "pH_H2O", "ant_mean_abundance", "bird_activity"
), class = "data.frame", row.names = c(NA, -96L))
这里是导致警告的代码:
library(lme4)
summary(glmer.nb(predators_trunk ~ scale(ant_mean_abundance) + scale(bird_activity) + scale(pH_H2O) + (1 | Site/Treatment), testdata, na.action = na.fail))
summary(glmer(predators_trunk ~ scale(ant_mean_abundance) + scale(bird_activity) + scale(pH_H2O) + (1 | Site/Treatment), testdata, family = negative.binomial(theta = 4.06643400243645), na.action = na.fail))
对我来说有趣的是,glmer.nb
的摘要没有产生任何警告,但是使用 glmer.nb
估计的 theta 调用 glmer
确实给了我警告。后者是在相应的glmer.nb
完整模型上使用dredge
(MuMIn)生成的模型调用。
此警告表明您的标准误差估计 可能 不太准确。但与所有警告一样,很难确定,最好的办法是尝试交叉检查是否可以。
在这种情况下,我将 glmer.nb
和 glmer
中的两个拟合保存为 g1
和 g2
。您可以看到估计值(点估计值、SE、Z 值...)发生了一些变化,但变化不大,所以至少应该让您放心。
printCoefmat(coef(summary(g1)),digits=2)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.844 0.111 16.7 <2e-16 ***
scale(ant_mean_abundance) -0.347 0.077 -4.5 7e-06 ***
scale(bird_activity) -0.122 0.076 -1.6 0.107
scale(pH_H2O) -0.275 0.104 -2.6 0.008 **
> printCoefmat(coef(summary(g2)),digits=2)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.846 0.108 17.1 <2e-16 ***
scale(ant_mean_abundance) -0.347 0.077 -4.5 6e-06 ***
scale(bird_activity) -0.122 0.075 -1.6 0.102
scale(pH_H2O) -0.275 0.102 -2.7 0.007 **
我在 Github(test_mods
分支上有一个 lme4
的开发版本,希望很快集成到 master 分支:如果你想安装它,你可以使用 devtools::install_github("lme4/lme4",ref="test_mods")
) 允许您为标准误差选择更准确(但更慢)的计算:这使我们回到(几乎)与 glmer.nb
.
g3 <- update(g2, control=glmerControl(deriv.method="Richardson"))
printCoefmat(coef(summary(g3)),digits=2)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.846 0.111 16.7 <2e-16 ***
scale(ant_mean_abundance) -0.347 0.077 -4.5 6e-06 ***
scale(bird_activity) -0.122 0.076 -1.6 0.106
scale(pH_H2O) -0.275 0.104 -2.6 0.008 **
all.equal(coef(summary(g1))[,"Std. Error"],
coef(summary(g3))[,"Std. Error"])
[1] "Mean relative difference: 0.001597978"
glmmTMB
包(在 Github 上)也给出几乎相同的结果:
library(glmmTMB)
g5 <- glmmTMB(predators_trunk ~ scale(ant_mean_abundance) +
scale(bird_activity) + scale(pH_H2O) +
(1 | Site/Treatment), testdata,
family=nbinom2)
printCoefmat(coef(summary(g5))[["cond"]],digits=2)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.852 0.110 16.8 <2e-16 ***
scale(ant_mean_abundance) -0.348 0.077 -4.5 7e-06 ***
scale(bird_activity) -0.123 0.076 -1.6 0.106
scale(pH_H2O) -0.276 0.105 -2.6 0.008 **