title: Can I use emmeans with LME model?
body: <p>I am using LME model defined like: </p>
  
  <pre><code>mod4.lme &lt;- lme(pRNFL ~  Init.Age + Status + I(Time^2), random= ~1|Patient/EyeID,data = long1, na.action = na.omit)
  </code></pre>
  
  <p>The output is: </p>
  
  <pre><code>&gt; summary(mod4.lme)
  Linear mixed-effects model fit by REML
   Data: long1 
         AIC      BIC    logLik
    2055.295 2089.432 -1018.647
  
  Random effects:
   Formula: ~1 | Patient
          (Intercept)
  StdDev:    7.949465
  
   Formula: ~1 | EyeID %in% Patient
          (Intercept) Residual
  StdDev:    12.10405 2.279917
  
  Fixed effects: pRNFL ~ Init.Age + Status + I(Time^2) 
                  Value Std.Error  DF   t-value p-value
  (Intercept)  97.27827  6.156093 212 15.801950  0.0000
  Init.Age      0.02114  0.131122  57  0.161261  0.8725
  StatusA     -27.32643  3.762155 212 -7.263504  0.0000
  StatusF     -23.31652  3.984353 212 -5.852023  0.0000
  StatusN      -0.28814  3.744980  57 -0.076940  0.9389
  I(Time^2)    -0.06498  0.030223 212 -2.149921  0.0327
   Correlation: 
            (Intr) Int.Ag StatsA StatsF StatsN
  Init.Age  -0.921                            
  StatusA   -0.317  0.076                     
  StatusF   -0.314  0.088  0.834              
  StatusN   -0.049 -0.216  0.390  0.365       
  I(Time^2) -0.006 -0.004  0.001 -0.038 -0.007
  
  Standardized Within-Group Residuals:
         Min         Q1        Med         Q3        Max 
  -2.3565641 -0.4765840  0.0100608  0.4670792  2.7775392 
  
  Number of Observations: 334
  Number of Groups: 
             Patient EyeID %in% Patient 
                  60                119 
  </code></pre>
  
  <p>I wanted to get comparisons between my 'Status' factors (named A, N, F and H). So I did a emmeans model using this code: </p>
  
  <pre><code>emmeans(mod4.lme, pairwise ~ Status, adjust="bonferroni")
  </code></pre>
  
  <p>The output for this, is: </p>
  
  <pre><code>&gt; emmeans(mod4.lme, pairwise ~ Status, adjust="bonferroni")
  $emmeans
   Status   emmean       SE df lower.CL  upper.CL
   H      98.13515 2.402248 57 93.32473 102.94557
   A      70.80872 2.930072 57 64.94135  76.67609
   F      74.81863 3.215350 57 68.38000  81.25726
   N      97.84701 2.829706 57 92.18062 103.51340
  
  Degrees-of-freedom method: containment 
  Confidence level used: 0.95 
  
  $contrasts
   contrast    estimate       SE  df t.ratio p.value
   H - A     27.3264289 3.762155 212   7.264  &lt;.0001
   H - F     23.3165220 3.984353 212   5.852  &lt;.0001
   H - N      0.2881375 3.744980  57   0.077  1.0000
   A - F     -4.0099069 2.242793 212  -1.788  0.4513
   A - N    -27.0382913 4.145370  57  -6.523  &lt;.0001
   F - N    -23.0283844 4.359019  57  -5.283  &lt;.0001
  </code></pre>