title: Predict function in package spaMM resulting in error when third random effect added to linear mixed model
body: <p>I'm trying to run a linear mixed model using the spaMM package and fitme() function in R, however when I add a third random effect the predict() function cannot be completed.</p>
  
  <p>I would like the meanfit1 model to have one fixed effect:</p>
  
  <pre><code>elev
  </code></pre>
  
  <p>and three random effects:</p>
  
  <pre><code>Matern(1|long+lat)
  1|river_ID
  1|source_ID
  </code></pre>
  
  <p>however the predict function does not seem to permit three random effects. </p>
  
  <pre><code>Data &lt;- data.frame(source_ID = c("A","B","C","D","E"),
                 lat = c(51.63,51.69,51.72,51.69,51.72),
                 long = c(1.56,1.51,1.37,1.43,1.60),
                 river_ID = c("b","a","b","b","t"),
                 elev = c(63,54,32,45,48),
                 mean = c(28,28,31,31,27),
                 n = rep(5,5),
                 var = c(0.1,0.4,0.5,1.5,0.8))
  
  dispfit1 &lt;- fitme(
    formula = var ~ 1 + Matern(1|long + lat) + (1|river_ID) + 
    (1|source_ID),
    family = stats::Gamma(log), data = Data, fixed = list(phi = 2),
    prior.weights = n - 1, control.dist = list(dist.method = 
    "Earth"),
    method = "REML")
  
  Data$disp &lt;- spaMM::predict.HLfit(dispfit1, newdata = Data, variances = 
                                list(respVar = T))
  
  meanfit1 &lt;- fitme(
    formula = mean ~ 1 + elev + Matern(1|long + lat) + (1|river_ID) + 
    (1|source_ID),
    data = Data, resid.model = list(formula = ~0 + offset(disp), family = 
    stats::Gamma(identity)),
    prior.weights = n, control.dist = list(dist.method = "Earth"),
    method = "REML")
  </code></pre>
  
  <p>The code works well if I remove either (1|source_ID) or (1|river_ID), however gives the following error message when all three random effects are included: 'Error in '+'(c(0, 0, 0, 0, 0, 0, 0.00508460138716231, 0, 0, 0, 0, 0, 0,  : 
    operator needs one or two arguments'</p>
  
  <p>Is this a coding problem or a modelling problem?</p>
  
  <p>Many thanks for your help</p>