The question is too vague. We need a sample dataset for an adequate answer. In particular, the term "variance component" usually refers to random-effect models which are different from regular anova models. If you're actually talking about variance-component (random-effect) models, you have many possibilities in R. You might want to try the (recommanded) nlme package, function lme and its numerous utility functions. You will find many examples in the companion book

Pinheiro, J. C. & Bates, D. M. Mixed-effect models in S and S-Plus. Springer, 2000, 528 p.

Hi Kisito,

You have to perform an analysis of variance (ANOVA). The method employed would differ depending on the experimental design and the variables (factors) included. I attach you some information regarding this. You will find there some interesting explanations and code in R.

Best,

http://www.personality-project.org/r/r.anova.html

http://www.statmethods.net/stats/anova.html

Thank you dear Martin

The question is too vague. We need a sample dataset for an adequate answer. In particular, the term "variance component" usually refers to random-effect models which are different from regular anova models. If you're actually talking about variance-component (random-effect) models, you have many possibilities in R. You might want to try the (recommanded) nlme package, function lme and its numerous utility functions. You will find many examples in the companion book

Pinheiro, J. C. & Bates, D. M. Mixed-effect models in S and S-Plus. Springer, 2000, 528 p.

If you are interested in getting the variance components (VC values), you could estimate an ANOVA table in VCA package (function anovaVCA). This function will create an pbject with an ANOVA table, with the variance actual values, but withou t performig an analysis of variance to evaluate group means, etc. In order to test the latter, you should use other functios, such as anova (lm()) of the base R-

I hope it is useful.

As Renaud says, the question is too vague, it depends whether you are referring to a random effects models or not, and whether your factors are crossed or nested. In package lmer you can you use the function lmer (with the simplest case) as follows:

model