[R-sig-ME] Confidence intervals on predictions for a non-linear mixed model (nlme)

Paul Buerkner paul.buerkner at gmail.com
Mon Aug 22 19:58:33 CEST 2016

Hi Piet,

as already pointed out on stackexchange, there is no direct way with nlme
to determine the distribution of the parameters so that we don't know if a
normal approximation is justified.

One solution to overcome this problem is to use Bayesian methods. With the
brms package, for instance, this looks as follows:


# define some reasonable priors
prior = c(set_prior("normal(80, 20)", nlpar = "Asym"),
          set_prior("normal(0, 10)", nlpar = "R0"),
          set_prior("normal(0, 5)", nlpar = "lrc"))

# fit the model
fm2 <- brm(height ~ Asym+(R0-Asym)*exp(-exp(lrc)*age),
           data = Loblolly, prior = prior,
           nonlinear = list(Asym ~ 1 + (1|Seed), R0 ~ 1, lrc ~ 1))

# effect of age without RE variance
# effect of age with RE variance
marginal_effects(fm2, re_formula = NULL)

Using brms may be a bit cumbersome at start as you need a C++ compiler at
run time. That is you need Rtools on Windows or Xcode on Mac (see
for more details.

Hope this will help you in getting closer to answering your research

- Paul

2016-08-22 14:23 GMT+02:00 Piet van den Berg <pvdberg1 at gmail.com>:

> Dear all,
> I'm trying to get confidence intervals on my predictions for a non-linear
> mixed model in nlme, using resampling of parameter values. I got a result,
> but would like to know if I'm going in the right direction.
> I posted my problem here:
> http://stats.stackexchange.com/questions/231074/confidence-intervals-on-
> predictions-for-a-non-linear-mixed-model-nlme
> Any help would be appreciated.
> All best,
> Piet.
>         [[alternative HTML version deleted]]
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list