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Interpretation of error variance [ Reply ] By: Santiago Beguería on 2014-07-22 06:29

[forum:41436]

Hi, I'm a bit confusing about the interpretation of sig2 and g_... when summarizing (the posterior of) a model fit with BayesFactor. For example: data(puzzles) bfFull = lmBF(RT ~ shape + color + shape:color + ID, data = puzzles, whichRandom = "ID") samples = lmBF(RT ~ shape + color + shape:color + ID, data = puzzles, whichRandom = "ID", posterior = TRUE, iterations = 1000) summary(samples) I want to know the posterior distribution of the residuals from the model, but I'm not sure how to compute them from the outputs of summary.BF. I understand that sig2 is the error variance, but what are those g_shape, g_color, g_ID and g_shape:color? Feeling too newbie to BF... Cheers, Santiago