<roberta_varriale <at> ds.unifi.it> writes: > Im able to estimate a null model. > For example, using SAS data, I can estimate the model: > lme(y ~ 1, data = Mississippi, random = ~ 1|influent, method="ML") > > As suggested in the literature I want to test the significance of the > second level variance. So, I would like also to estimate a linear model > without random effects. Is it possible with your library? > In the description of the function lme I found that the random part is > optional, but I don't know how to remove it.
In general, linear models without random effects are fit with lm() , which is part of base R. If you need other fancy things (heteroscedasticity, spatial autocorrelation etc.) you can use gls in the nlme package. Ben Bolker ______________________________________________ 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.
