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  1. > lCtr <- lmeControl(maxIter = 5000, niterEM = 500, msMaxIter=1000, msMaxEval=1000, msVerbose = FALSE, opt = 'optim')
  2. > lev.lm <- lme(data ~ des_days, random = ~des_days|ratID, data=data_red_trf, na.action=na.omit, method = "ML", control=lCtr)
  3. > summary(lev.lm)
  4.  
  5. Linear mixed-effects model fit by maximum likelihood
  6.  Data: data_red_trf
  7.         AIC       BIC   logLik
  8.   -3289.891 -3216.717 1659.946
  9.  
  10. Random effects:
  11.  Formula: ~des_days | ratID
  12.  Structure: General positive-definite, Log-Cholesky parametrization
  13.             StdDev      Corr                
  14. (Intercept) 0.018205256 (Intr) ds_dy1 ds_d14
  15. des_days1   0.014267902 -0.116              
  16. des_days14  0.012291117  0.289  0.916      
  17. des_days48  0.009514837 -0.866 -0.393 -0.725
  18. Residual    0.043158947                    
  19.  
  20. Fixed effects: data ~ des_days
  21.                  Value   Std.Error  DF   t-value p-value
  22. (Intercept)  0.8274313 0.007937938 962 104.23757  0.0000
  23. des_days1   -0.0026322 0.007443294 962  -0.35363  0.7237
  24. des_days14  -0.0011319 0.006635512 962  -0.17058  0.8646
  25. des_days48   0.0112579 0.005452614 962   2.06469  0.0392
  26.  Correlation:
  27.            (Intr) ds_dy1 ds_d14
  28. des_days1  -0.213              
  29. des_days14  0.068  0.694      
  30. des_days48 -0.738 -0.078 -0.208
  31.  
  32. Standardized Within-Group Residuals:
  33.          Min           Q1          Med           Q3          Max
  34. -4.692631026 -0.524900364  0.006026586  0.514647345  3.766144781
  35.  
  36. Number of Observations: 971
  37. Number of Groups: 6
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