I sometimes find that my GLMMs from glmer, package lme4, show the following warning messages, when their summary is called:
我有时发现，glmer的GLMMs包lme4在它们的摘要被调用时显示了以下警告消息:
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
Similar questions I found here on * refer to other functions, not glmer, and the 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 the discussion focuses on a particular model rather than on the meaning of the warning message.
我在*上发现的类似问题是指其他函数，而不是glmer，而LME4 Wiki也没有详细说明这一点。在这个问题中，在处理这类错误消息之前就解决了这个问题，在这里，讨论的重点是一个特定的模型，而不是警告消息的含义。
So the question is: should I worry about that message, or is it OK because it is simply a warning and not an error, and as it says, it is "falling back to var-cov estimated from RX" (whatever RX is) anyway.
所以问题是:我应该担心那条消息吗?还是因为它只是一个警告而不是一个错误?正如它所说，它“回到了RX估计的var-cov”(不管RX是什么)。
Interestingly, although the summary states that the model failed to converge, I do not get the usual convergence warnings in red.
有趣的是，尽管总结指出模型没有收敛，但我没有得到通常的红色的收敛警告。
Here comes a (minimal) dataset:
这里有一个(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))
And here is the code leading to the warnings:
下面是导致警告的代码:
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))
Interestingly to me, the summary of the glmer.nb does not yield any warnings, but the call to glmer, using the theta that was estimated by glmer.nb, does give me the warnings. The latter is the model call that is generated by using dredge (MuMIn) on the corresponding glmer.nb full model.
有趣的是，对glmer的总结。nb不会产生任何警告，但是会调用glmer，使用glmer估计的theta。nb，确实给了我警告。后者是通过在相应的glmer上使用dredge (MuMIn)生成的模型调用。nb完整模型。
1 个解决方案
#1
3
This warning suggests that your standard error estimates might be less accurate. But as with all warnings, it's hard to know for sure and the best thing is to try to cross-check if you can.
这个警告提示您的标准错误估计可能不太准确。但就像所有的警告一样，很难确定，最好的办法是尽可能地进行交叉检查。
In this case I saved your two fits, from glmer.nb and glmer, as g1 and g2. You can see that the estimates (point estimates, SEs, Z values ...) have changed a little bit, but not very much, so at the very least that should reassure you.
在这种情况下，我从格尔默那里救了你两个人。nb和glmer，如g1和g2。您可以看到，估计值(点估计值、SEs、Z值……)略有变化，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 **
I have a development version of lme4 on Github (the test_mods branch, hopefully integrated into the master branch soon: if you want to install it, you can use devtools::install_github("lme4/lme4",ref="test_mods")) which allows you to pick a more accurate (but slower) calculation for the standard errors: this gets us back to (nearly) the same standard errors as glmer.nb.
Github上我有一个开发版本的lme4(test_mods分支,希望很快集成到主分支:如果你想安装它,可以使用devtools::install_github(“lme4 / lme4”ref =“test_mods”)),它允许您选择一个更精确的(但慢)计算标准错误:这让我们回到(几乎)glmer.nb一样的标准误差。
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"
The glmmTMB package (on Github) also gives almost the same results:
glmmTMB包(在Github上)也提供了几乎相同的结果:
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 **
#1
3
This warning suggests that your standard error estimates might be less accurate. But as with all warnings, it's hard to know for sure and the best thing is to try to cross-check if you can.
这个警告提示您的标准错误估计可能不太准确。但就像所有的警告一样，很难确定，最好的办法是尽可能地进行交叉检查。
In this case I saved your two fits, from glmer.nb and glmer, as g1 and g2. You can see that the estimates (point estimates, SEs, Z values ...) have changed a little bit, but not very much, so at the very least that should reassure you.
在这种情况下，我从格尔默那里救了你两个人。nb和glmer，如g1和g2。您可以看到，估计值(点估计值、SEs、Z值……)略有变化，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 **
I have a development version of lme4 on Github (the test_mods branch, hopefully integrated into the master branch soon: if you want to install it, you can use devtools::install_github("lme4/lme4",ref="test_mods")) which allows you to pick a more accurate (but slower) calculation for the standard errors: this gets us back to (nearly) the same standard errors as glmer.nb.
Github上我有一个开发版本的lme4(test_mods分支,希望很快集成到主分支:如果你想安装它,可以使用devtools::install_github(“lme4 / lme4”ref =“test_mods”)),它允许您选择一个更精确的(但慢)计算标准错误:这让我们回到(几乎)glmer.nb一样的标准误差。
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"
The glmmTMB package (on Github) also gives almost the same results:
glmmTMB包(在Github上)也提供了几乎相同的结果:
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 **