Mac和PC

Question

我一直在我的PC上分析我的多级数据。我现在正在使用Mac并运行相同的模型。一些输出是相同的，但有些是完全不同的。我似乎无法找出原因。这是模型：

> loss.2 <- 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)))

在PC上我得到了这个输出：

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

在Mac上我得到了这个输出：

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) 1.242e-13 3.524e-07     
              Valence.c   4.724e-13 6.873e-07 0.98
 mood.c       (Intercept) 7.998e-16 2.828e-08     
              Valence.c   3.217e-14 1.793e-07 1.00
Number of obs: 736, groups:  Chain:mood.c, 92; mood.c, 2

Fixed effects:
                   Estimate Std. Error z value Pr(>|z|)
(Intercept)       2.947e-05  4.794e-02   0.001    1.000
Posn.c            7.441e-05  4.122e-02   0.002    0.999
Valence.c        -4.011e-05  9.589e-02   0.000    1.000
Posn.c:Valence.c -6.672e-05  8.245e-02  -0.001    0.999

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

有谁知道为什么两个平台的输出可能会有所不同，以及我如何让它们对齐？