I'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 <- "Sepal.Length ~ Sepal.Width | Species" x <- lmer(frm, data=iris, verbose=TRUE, control=list(maxIter=1), model=FALSE) # Capture starting values for next set of iterations start <- list(ST=x@ST) # Update model twoStep <- 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:
> x <- 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 > # Capture starting values for next set of iterations > start <- list(ST=x@ST) > # Update model > twoStep <- 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 <- lmer(frm, data=iris, verbose=TRUE, control=list(maxIter=3), model=FALSE) start <- list(ST=x@ST) twoStep <- 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:
> x <- 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 > # Capture starting values for next set of iterations > start <- list(ST=x@ST) > # Update model > twoStep <- 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("lme4") [1] ‘0.999999.2’
This was a confirmed bug in lme4 and as per the comments
I've logged an issue at github.com/lme4/lme4/issues/55 – Andrie Jul 2 '13 at 15:42
This should be fixed now for lmer (although not for glmer, which is slightly trickier). – Ben Bolker Jul 14
That was back when the version was < 0.99999911-6; lme4 on CRAN has had versions > 1.0-4 since 21-Sep-2013.
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