title: Plot generalized linear mixed models (GLMMs): mixture of categorical and numeric variables
body: <p>I'd like to plot the relationship between the number of <code>ladenant</code> response variable in function of <code>Bioma</code> (categorical) and <code>temp</code> (numeric) using binomial negative
  generalized linear mixed models (GLMMs) without success. I try to do:</p>
  <pre><code>#Packages
  library(lme4)
  library(ggplot2)
  library(ggeffects)
  
  #Open my dataset
  myds&lt;-read.csv(&quot;https://raw.githubusercontent.com/Leprechault/trash/main/my_glmm_dataset.csv&quot;)
  myds &lt;- myds[,-c(3)] # remove bad character variable
  
  # Negative binomial GLMM
  m.laden.1  &lt;- glmer.nb(ladenant ~ Bioma +  poly(temp,2) + scale(UR) + (1 | formigueiro), data = DataBase)
  
  # Plot the results
  mydf &lt;- ggpredict(m.laden.1, terms = c(&quot;temp&quot;,&quot;Bioma&quot;))
  ggplot(mydf, aes(x, predicted), group = Bioma) +
    geom_point(DataBase, aes(temp, ladenant), alpha = 0.5) + # Observed ladenant response variable
    geom_line() +
    geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .1)
  </code></pre>
  <p>I have a not so good plot because I don't have one line for each <code>Bioma</code> and bad lines for <code>temp</code> variable:</p>
  <p><a href="https://i.stack.imgur.com/cnXiH.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/cnXiH.png" alt="my-plot" /></a></p>
  <p>But the object <code>mydf</code> specification contains the <code>Bioma</code> variable:</p>
  <pre><code>mydf 
  
  # # Predicted counts of ladenant
  
  # # Bioma = Atlantic Forest
  
  # temp | Predicted |         95% CI
  # ---------------------------------
  #   10 |      1.88 | [ 0.81,  4.35]
  #   15 |     12.95 | [ 9.11, 18.40]
  #   20 |     32.61 | [26.42, 40.25]
  #   25 |     30.00 | [23.51, 38.28]
  #   30 |     10.08 | [ 4.79, 21.24]
  #   35 |      1.24 | [ 0.24,  6.43]
  
  # # Bioma = Transition
  
  # temp | Predicted |          95% CI
  # ----------------------------------
  #   10 |      6.84 | [ 3.04,  15.42]
  #   15 |     47.17 | [34.05,  65.34]
  #   20 |    118.79 | [92.27, 152.94]
  #   25 |    109.29 | [76.84, 155.43]
  #   30 |     36.73 | [16.17,  83.44]
  #   35 |      4.51 | [ 0.82,  24.71]
  
  # # Bioma = Pampa
  
  # temp | Predicted |         95% CI
  # ---------------------------------
  #   10 |      1.42 | [ 0.70,  2.90]
  #   15 |      9.80 | [ 7.47, 12.86]
  #   20 |     24.69 | [18.74, 32.52]
  #   25 |     22.71 | [16.46, 31.35]
  #   30 |      7.63 | [ 3.65, 15.96]
  #   35 |      0.94 | [ 0.19,  4.67]
  
  # Adjusted for:
  # *          UR = 82.78
  # * formigueiro = 0 (population-level)
  </code></pre>
  <p>Plaese, any help to improve this plot?</p>