Dear R-helpers, I'm analyzing a data with hierarchical linear model. I have one level 1 predictor and one level 2 predictor, which looks like below:
fm1 <- lmer(y ~ 1 + x1 + x2 + x1:x2 + (1 + x1 | id.full)) where: y is the outcome variable. x1 is the level 1 predictor variable. x2 is the level 2 predictor variable. id.full is the conditioned variable. It runs beautifully when only x1 is centered (I subtracted the grand mean from each value). However, when I also centered x2 variable with the same procedure, it gives me the following error message: Warning message: In mer_finalize(ans) : singular convergence (7) I'd appreciate if someone could explain me what it means. One of the differences between "non-centered values" and "centered values" is that the "centered values" include negative values. Could it be the reason? If so, what shall I do? Thank you! Sachi ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
