[R-sig-ME] Summarizing the fitted model takes more RAM than

[Gorjanc Gregor]

> ## Fit the model
> fit9b <- lmer(tezaroj ~ pasma + roj2 + zj2 + spol + reja + jagLM +(1 | rejec) + (1 | hy) + (1 | hys), data=podatki)
> From your fitting, i wonder what your theoretical model is.
> y=b0+uj+b1*pasma + b2*roj2 + b3*zj2 + b4*spol + b5*reja + b6*jagLM+b7*rejec+b8*hy+b9*hys
> uj represents the random intercept effects for rejec,hy and hys?
> I cannot see how to write your model and explain it.
> It seems that you have fitted three random intercepts in your model (rejec,hy and hys), are they all two-level factor or first-level factor?
> What is your model formula?

I simply have three random effects. The model specification (without indexes) would be

y ~ N(mu, sigma^2_e)

mu = alpha + ... + rejec + hy + hys

rejec ~ N(0 sigma^2_r)

hy ~ N(0 sigma^2_hy)

hys ~ N(0 sigma^2_hys)

gg