I&#39;m using `lmer()` in package `lme4` to estimate mixed effects models.  This works well, but now I want to run the estimation process for a fixed number of iterations, then resume the process by specifying start values, as calculated by the last estimation process.

According to the help for `?lmer` this is possible, by setting the arguments:

* `start` - these are the new start values, and according to the help one can extract the value in slot `ST` from a fitted model and use these, i.e. use `x@ST`
* `maxiter` - supplied as a named argument to `control`

So, for example, suppose I want to fit a `lme` using the `iris` data, one can try this:

    library(lme4)
    
    # Fit model with limited number of iterations
    
    frm &lt;- &quot;Sepal.Length ~ Sepal.Width | Species&quot;
    
    x &lt;- lmer(frm, data=iris, 
              verbose=TRUE, control=list(maxIter=1), model=FALSE)
    
    # Capture starting values for next set of iterations
    start &lt;- list(ST=x@ST)
    
    # Update model
    twoStep &lt;-  lmer(frm, data=iris, 
              verbose=TRUE, control=list(maxIter=100), model=TRUE, 
              start=start)


This works.  Take a look at the output, where the **first column** is the REML, i.e. the random effect maximum likelihood.  Notice especially that the REML in model 2 starts where model 1 terminates:

    &gt; x &lt;- lmer(frm, data=iris, 
    +           verbose=TRUE, control=list(maxIter=1), model=FALSE)
      0:     264.60572: 0.230940 0.0747853  0.00000
      1:     204.22878: 0.518239  1.01025 0.205835
      1:     204.22878: 0.518239  1.01025 0.205835
    
    &gt; # Capture starting values for next set of iterations
    &gt; start &lt;- list(ST=x@ST)
    
    &gt; # Update model
    &gt; twoStep &lt;-  lmer(frm, data=iris, 
    +           verbose=TRUE, control=list(maxIter=100), model=TRUE, 
    +           start=start)
      0:     204.22878: 0.518239  1.01025 0.205835
      1:     201.51667: 0.610272  2.00277 0.286049
      2:     201.46706: 0.849203  1.94906 0.358809
      3:     201.44614: 0.932371  1.88581 0.482423
      4:     201.39421:  1.00909  1.71078 0.871824
      5:     201.36543:  1.00643  1.60453  1.01663
      6:     201.31066:  1.00208  1.35520  1.27524
      7:     201.28458:  1.08227  1.22335  1.35147
      8:     201.24330:  1.50333 0.679759  1.31698
      9:     201.11881:  1.95760 0.329767 0.936047


However, when I have a different value of `maxIters` this no longer works:

    x &lt;- lmer(frm, data=iris, 
              verbose=TRUE, control=list(maxIter=3), model=FALSE)
    start &lt;- list(ST=x@ST)
    twoStep &lt;-  lmer(frm, data=iris, 
                     verbose=TRUE, control=list(maxIter=100), model=TRUE, 
                     start=start)

Notice that **the REML value restarts at 264**, i.e. the beginning:

    &gt; x &lt;- lmer(frm, data=iris, 
    +           verbose=TRUE, control=list(maxIter=3), model=FALSE)
      0:     264.60572: 0.230940 0.0747853  0.00000
      1:     204.22878: 0.518238  1.01025 0.205835
      2:     201.94075:  0.00000  1.51757 -1.18259
      3:     201.71473:  0.00000  1.69036 -1.89803
      3:     201.71473:  0.00000  1.69036 -1.89803
    
    &gt; # Capture starting values for next set of iterations
    &gt; start &lt;- list(ST=x@ST)
    
    &gt; # Update model
    &gt; twoStep &lt;-  lmer(frm, data=iris, 
    +           verbose=TRUE, control=list(maxIter=100), model=TRUE, 
    +           start=start)
      0:     264.60572: 0.230940 0.0747853  0.00000
      1:     204.22878: 0.518238  1.01025 0.205835
      2:     201.94075:  0.00000  1.51757 -1.18259
      3:     201.71473:  0.00000  1.69036 -1.89803
      4:     201.64641:  0.00000  1.82159 -2.44144
      5:     201.63698:  0.00000  1.88282 -2.69497
      6:     201.63649:  0.00000  1.89924 -2.76298
      7:     201.63649: 4.22291e-08  1.90086 -2.76969
      8:     201.63649: 4.22291e-08  1.90086 -2.76969


---

**Question: How can I reliably restart `lmer()` with start values obtained from a previously fitted model?**


---

Session information:

    packageVersion(&quot;lme4&quot;)
    [1] ‘0.999999.2’



All Mixed Models Solutions on Gang of Coders

Total of 1 Mixed Models Solutions

Restart mixed effect model estimation with previously estimated values