title: Better fits for a linear model
body: <p>I am fitting some lines and I feel like I am telling R exactly how to fit them, but I feel like there is something (some factor or effect) I am unaware of that is preventing a good fit.</p>
  
  <p>My experimental unit is "plot" as in field plot, which I am sorry is confusing.</p>
  
  <p>The data can be found: <a href="https://www.dropbox.com/s/a0tplyvs8lxu1d0/rootmeansv2.csv" rel="nofollow noreferrer">https://www.dropbox.com/s/a0tplyvs8lxu1d0/rootmeansv2.csv</a> .
  with</p>
  
  <pre><code>df$plot.f&lt;-as.factor(df$plot)
  dfG&lt;-groupedData(mass ~ year|plot.f, data=df)
  dfG30&lt;-dfG[dfG$depth == 30,]
  </code></pre>
  
  <p>Simply, I have mass over time and I fit it to each experimental unit with the model:</p>
  
  <pre><code>fit &lt;- lme(mass ~ year , random = ~ 1 | plot, data = df)
  </code></pre>
  
  <p>and with <code>plot (augPred(fit))</code> I get these fits for each experimental unit ("plot"): </p>
  
  <p><a href="https://imgur.com/qKlJ1Ii" rel="nofollow noreferrer"><img src="https://i.imgur.com/qKlJ1Ii.png" title="Hosted by imgur.com" /></a></p>
  
  <p>What do I need to do to let the slope vary more between experimental units?  I am not interested in this from a statistical perspective, but from a predictive perspective -- so anything in the model can be manipulated to get those lines to move.</p>