I don't really understand when it come to mixed model,
how do you know when to use linear or nonlinear model?
For example, when using R function
lmer to build linear mixed model,
my model may look like this:
lmer( Y ~ X1 + X2 + X1*X2 + (1|Z) )
where $Y$ is the response (from a repeated measured data), $X_1$ and $X_2$ are fixed effects and $Z$ is the random effect.
Does this means when you pick these effects up to see their relation separately, like
Y~X2, both has to be linear so than you can use linear mixed model?
Y~X1 is nonlinear and
Y~X2 is linear? Should I use nonlinear mixed model when this is the case?