Hi,
I'm running some data simulations using (mixed effects)* regression models
that show difficulty to converge. Therefore, I seek a way of capturing
warnings (of false convergence) inside a loop.
Inside that loop, I modify data and estimate a model. I do so many times
with slightly different modifications of the data. Next, I extract some of
the model parameters and store these in a matrix. However, as some of the
models do not converge well, some of the stored parameters are extracted
from the ill-converged models. Therefore, I seek a way of automatically
detecting whether the estimation procedure has resulted in a warning, so I
can distinguish between the well- and ill-converged models.
I have been trying to use functions as warnings(), as well as using the
object last.warning, but unfortunately to no avail.
Although I cannot provide a reproducible example, I schematically represent
the procedure I seek to use below:
for (i in 1:10)
{
<<modify data>>
<<estimate model>>
<<<evaluate whether estimation produced warning>>>
<<extract model parameters, and store whether warning occured>>
}
I hope any one can give some guidelines on how to deal with warnings inside
a loop.
With Kind regards,
Rense
*Although I use the lme4 package for that actual analysis, I sent my
question to this mailinglist (instead of the R mixed list) because I believe
this is a general issue, rather than one associated exclusively with mixed
models.
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