Linear mixed models not working

Hello, I am interested in fitting a random intercept linear mixed model to my data. My response variable is Spike_prob, my predictor is gen and grouping variable is animal. Here is the formula I use:

lme = fitlme(data,'Spike_prob~1+gen+(1|animal)')

Linear mixed-effects model fit by ML

Model information:
    Number of observations             209
    Fixed effects coefficients           2
    Random effects coefficients         13
    Covariance parameters                2

Formula:
    Spike_prob ~ 1 + gen + (1 | animal)

Model fit statistics:
    AIC        BIC        LogLikelihood    Deviance
    -225.27    -211.91    116.64           -233.27 

Fixed effects coefficients (95% CIs):
    Name                 Estimate     SE          tStat      DF 
    '(Intercept)'          0.22942    0.014438      15.89    207
    'gen'                -0.061785    0.019297    -3.2019    207

      pValue        Lower        Upper    
      1.0669e-37      0.20096      0.25788
       0.0015807    -0.099828    -0.023742

Random effects covariance parameters (95% CIs):
Group: animal (13 Levels)
    Name1                Name2                Type         Estimate    Lower
    '(Intercept)'        '(Intercept)'        'std'        0           NaN  

      Upper
      NaN  

Group: Error
    Name             Estimate    Lower      Upper  
    'Res Std'        0.13848     0.12582    0.15242

Why do I get the exact same p and DF values when I do not include the random intercept term?

    lme = fitlme(data,'Spike_prob~1+gen')
  lme = 

Linear mixed-effects model fit by ML

Model information:
    Number of observations             209
    Fixed effects coefficients           2
    Random effects coefficients          0
    Covariance parameters                1

Formula:
    Spike_prob ~ 1 + gen

Model fit statistics:
    AIC        BIC        LogLikelihood    Deviance
    -227.27    -217.25    116.64           -233.27 

Fixed effects coefficients (95% CIs):
    Name                 Estimate     SE          tStat      DF 
    '(Intercept)'          0.22942    0.014438      15.89    207
    'gen'                -0.061785    0.019297    -3.2019    207

      pValue        Lower        Upper    
      1.0669e-37      0.20096      0.25788
       0.0015807    -0.099828    -0.023742

Random effects covariance parameters (95% CIs):
Group: Error
    Name             Estimate    Lower      Upper  
    'Res Std'        0.13848     0.12582    0.15242

Answer 1

[Peng Li]

In your first equation with random effect, you see that the random effect estimate is 0. If you check your data, you will find out that your variable gen is always the same in a given animal. They are sort of duplicating each other.