I fitted a LMM with random intercept and random slope by means of lmer():
model <- lmer(y ~ x + (1+x|subject),df)
However, lmer() returned an error:
Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.037749 (tol = 0.002, component 1) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio - Rescale variables?
Therefore, I standardized the predictor by centering and dividing by the SD:
x <- (x-mean(x)/sd(x))
This made the model work. But now the intercept and slope for predictions do not represent the original scale anymore. But this was actually my reason for fitting the model: providing a formula to predict future observations.
I retrieved the original slope by dividing the slope by the sd(predictor)
But I cannot retrieve the intercept anymore. As far as I understood, the intercept is now the value of y at the mean slope value, expressed in SDs:
I would like to have the intercept in the classical meaning, i.e. the value of y when x = 0. Is it possible to retrieve this from the model?