title: How to get solution for mixed model using nlme package
body: <p>My data look like this</p>
  
  <pre><code>Study   NDF ADF CP  Eeff
  1   35.8    24.4    18.6    34.83181476
  1   35.8    24.4    18.6    33.76824264
  1   35.8    24.4    18.6    32.67390287
  1   35.8    24.4    18.6    33.05520666
  2   39.7    23.4    16.1    33.19730252
  2   39.4    22.9    16.3    34.04709188
  3   28.9    20.6    18.7    33.22501606
  3   27.1    18.9    17.9    33.80766289
  </code></pre>
  
  <p>Of course, I have 80 lines like this.
  I used <code>lme</code> function to run a mixed model (Study as random effect), as following:</p>
  
  <pre><code>fm1&lt;-lme(Eeff~NDF+ADF+CP,random=~1|Study, data=na.omit(phuong))
  </code></pre>
  
  <p>I got this result:</p>
  
  <pre><code>Fixed effects: Ratio ~ ADF + CP + FCM + DMI + DIM 
                   Value  Std.Error  DF   t-value p-value
  (Intercept)  3.1199808 0.16237303 158 19.214896  0.0000
  ADF         -0.0265626 0.00406990 158 -6.526603  0.0000
  CP          -0.0534021 0.00539108 158 -9.905636  0.0000
  FCM         -0.0149314 0.00353524 158 -4.223598  0.0000
  DMI          0.0072318 0.00498779 158  1.449894  0.1491
  DIM         -0.0008994 0.00019408 158 -4.634076  0.0000
   Correlation: 
      (Intr) ADF    CP     FCM    DMI   
  ADF -0.628                            
  CP  -0.515  0.089                     
  FCM -0.299  0.269 -0.203              
  DMI -0.229 -0.145  0.083 -0.624       
  DIM -0.113  0.127 -0.061  0.010 -0.047
  </code></pre>
  
  <p>These results show the case where intercept is random but slope is fixed. How can I see my 80 intercept, for example, like below when I used study as fixed effect:</p>
  
  <pre><code>Coefficients:
                              Estimate      Std. Error t value Pr(&gt;|t|)    
  (Intercept)        -0.0021083  0.0102536  -0.206 0.837351    
  ADF                      0.0005248  0.0002962   1.772 0.078313 .  
  CP                        0.0021131  0.0003277   6.448 1.26e-09 ***
  factor(Study)2   0.0057274  0.0038709   1.480 0.140933    
  factor(Study)3   0.0117722  0.0035262   3.338 0.001046 ** 
  factor(Study)4   0.0091049  0.0043227   2.106 0.036730 *  
  factor(Study)6   0.0149733  0.0045345   3.302 0.001182 ** 
  factor(Study)7   0.0065518  0.0036837   1.779 0.077196 .  
  factor(Study)8   0.0066134  0.0035371   1.870 0.063337 .  
  factor(Study)9   0.0086758  0.0036641   2.368 0.019083 *  
  factor(Study)10  0.0105657  0.0041296   2.559 0.011434 *  
  factor(Study)11  0.0083694  0.0040194   2.082 0.038900 *  
  factor(Study)16  0.0171258  0.0028962   5.913 1.95e-08 ***
  factor(Study)18  0.0019277  0.0042300   0.456 0.649209    
  factor(Study)20  0.0172469  0.0040412   4.268 3.36e-05 ***
  factor(Study)23  0.0132676  0.0031658   4.191 4.57e-05 ***
  factor(Study)24  0.0063313  0.0031519   2.009 0.046236 *  
  factor(Study)25  0.0050929  0.0039135   1.301 0.194989    
  </code></pre>
  
  <p>Thank you very much,
  Phuong</p>