LME4“优化器(NLOPTWRAP)收敛代码:0(ok);但是没有融合警告
我使用LME4软件包运行了多级模型,结果就是这样:
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: y ~ 1 + con + ev1 + ev2 + ev1:con + ev2:con + (1 | pid)
Data: dat_ind
REML criterion at convergence: 341.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.5811 -0.6757 0.0088 0.7251 1.9435
Random effects:
Groups Name Variance Std.Dev.
pid (Intercept) 0.00 0.000
Residual 15.47 3.933
Number of obs: 60, groups: pid, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.078944 0.575915 54.000000 -5.346 1.86e-06 ***
con 0.293982 0.026027 54.000000 11.295 7.71e-16 ***
ev1 -1.118278 0.836885 54.000000 -1.336 0.187
ev2 13.608356 0.716009 54.000000 19.006 < 2e-16 ***
con:ev1 -0.001739 0.037749 54.000000 -0.046 0.963
con:ev2 0.031400 0.032062 54.000000 0.979 0.332
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) con ev1 ev2 con:v1
con -0.143
ev1 0.077 0.071
ev2 -0.365 0.257 -0.364
con:ev1 0.071 0.071 -0.087 -0.155
con:ev2 0.259 -0.392 -0.156 0.022 -0.348
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see ?isSingular
什么是“优化器(NloptWrap)收敛代码:0(ok)”的意思?
而且,它不会发出融合警告。
例如,这没有发出融合警告(例如,警告消息: 模型无法与1个负特征值收敛:-2.3e+01)
I ran multilevel model using lme4 package, and results was like this:
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: y ~ 1 + con + ev1 + ev2 + ev1:con + ev2:con + (1 | pid)
Data: dat_ind
REML criterion at convergence: 341.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.5811 -0.6757 0.0088 0.7251 1.9435
Random effects:
Groups Name Variance Std.Dev.
pid (Intercept) 0.00 0.000
Residual 15.47 3.933
Number of obs: 60, groups: pid, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.078944 0.575915 54.000000 -5.346 1.86e-06 ***
con 0.293982 0.026027 54.000000 11.295 7.71e-16 ***
ev1 -1.118278 0.836885 54.000000 -1.336 0.187
ev2 13.608356 0.716009 54.000000 19.006 < 2e-16 ***
con:ev1 -0.001739 0.037749 54.000000 -0.046 0.963
con:ev2 0.031400 0.032062 54.000000 0.979 0.332
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) con ev1 ev2 con:v1
con -0.143
ev1 0.077 0.071
ev2 -0.365 0.257 -0.364
con:ev1 0.071 0.071 -0.087 -0.155
con:ev2 0.259 -0.392 -0.156 0.022 -0.348
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see ?isSingular
what is "optimizer (nloptwrap) convergence code: 0 (OK)" meaning?
Moreover, it does not throw a convergence warning.
for example, This did not throw a convergence warning (e.g., Warning message:
Model failed to converge with 1 negative eigenvalue: -2.3e+01 )
如果你对这篇内容有疑问,欢迎到本站社区发帖提问 参与讨论,获取更多帮助,或者扫码二维码加入 Web 技术交流群。

绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱,如果其他用户或者作者回复了您的评论,将不能在第一时间通知您!
发布评论
评论(1)
这意味着该模型已融合
那是因为它融合了。
但是,该行:
很重要。这意味着它已融合到一个奇异拟合度,在这种情况下,这是因为随机截距差异已估计为零:
在这种情况下,您可能根本不需要随机截距,并且可以进行模型拟合使用
lm()
It means that the model has converged
That's because it has converged.
However, the line:
is important. It means that it has converged to a singular fit which in this case is because the random intercepts variance has been estimated at zero:
In this case it is possible that you don't need random intercepts at all and you can proceed with a model fitted with
lm()