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我有一个奇怪的问题。我运行了以下模型，其中包括作为预测变量之一的“Valence.c”。这是编码为“0”或“1”的预测变量，代表“正”和“负”。预测变量居中，因此实际上是“-0.5”和“0.5”。

> loss.1 <- glmer.nb(Loss_across.Chain ~ Posn.c*Valence.c + (Valence.c|mood.c/Chain), data = FinalData_forpoisson, control = glmerControl(optimizer = "bobyqa", check.conv.grad = .makeCC("warning", 0.05)))

我得到以下输出：

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: Negative Binomial(4.9852)  ( log )
Formula: Loss_across.Chain ~ Posn.c * Valence.c + (Valence.c | mood.c/Chain)
   Data: FinalData_forpoisson
Control: ..3

     AIC      BIC   logLik deviance df.resid 
  1894.7   1945.3   -936.4   1872.7      725 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.3882 -0.7225 -0.5190  0.4375  7.1873 

Random effects:
 Groups       Name        Variance  Std.Dev.  Corr
 Chain:mood.c (Intercept) 8.782e-15 9.371e-08     
              Valence.c   9.608e-15 9.802e-08 0.48
 mood.c       (Intercept) 0.000e+00 0.000e+00     
              Valence.c   1.654e-14 1.286e-07  NaN
Number of obs: 736, groups:  Chain:mood.c, 92; mood.c, 2

Fixed effects:
                 Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -0.19255    0.04794  -4.016 5.92e-05 ***
Posn.c           -0.61011    0.04122 -14.800  < 2e-16 ***
Valence.c        -0.27372    0.09589  -2.855  0.00431 ** 
Posn.c:Valence.c  0.38043    0.08245   4.614 3.95e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) Posn.c Vlnc.c
Posn.c       0.491              
Valence.c    0.029 -0.090       
Psn.c:Vlnc. -0.090  0.062  0.491

由于 Valence.c 的固定效应是负数，我想我会尝试重新编码变量，使正数现在是“0.5”，负数现在是“-0.5”。我认为解释事故率的增加比解释事故率的下降更容易。所以我运行了这个相同的模型，除了它调用的数据文件具有相反的编码：

> loss.2 <- glmer.nb(Loss_across.Chain ~ Posn.c*Valence.c + (Valence.c|mood.c/Chain), data = LossAnalysis_ValenceCodingReversed, control = glmerControl(optimizer = "bobyqa", check.conv.grad = .makeCC("warning", 0.05)))

我收到此警告消息：

Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  unable to evaluate scaled gradient
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge: degenerate  Hessian with 1 negative eigenvalues

为什么更改参考组意味着模型现在无法收敛？我对正面和负面的观察次数相同。任何帮助都会很棒！

谢谢

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