Hello,

I have the following model:

fm.final<-lme(log(uL)~P_atm + HoboT  + DOS + T_10cm  +
P_atm:HoboT,random=(~1|Block/Fence/Chamber), data=fences13,method="REML")

such that the response variable is log-transformed and the random effects
are nested.  I would like to use this model to predict uL based on a new
dataset that has all my explanatory variables by random effect.  (So each
row of the newdata  has P_atm, HoboT, DOS, T_10cm, Block, Fence, Chamber.)
I also want to predict based on another new dataset without random effects
(so each row has P_atm, HoboT, DOS, T_10cm).

I know that I can use the predict() function with level=3 or level=0 to
obtain a prediction for log(uL).  Then I can take exp(log(uL)) to obtain
uL.  However, this is not fully correct because it does not take into
account the lognormal bias correction (i.e. if if log(X) ~ Normal(mu,
sigma), then mean(X) = exp[mu + (sigma^2)/2]).


My question is how to apply the lognormal bias correction in each case.
If I want to predict at the population level (level=0), then my idea is
this:

uL(i)=exp(predicted value(i) + sd(resid(fm.final, level=0))

If I want to predict at the chamber level (level=3), should I include the
random plot effects when applying this correction?

I am wondering if anyone has any thoughts on this or references that
immediately come to mind.

Thanks,
Elizabeth

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