On 5/8/07, shabnam shademan <[EMAIL PROTECTED]> wrote:
Hi all – I am unsophisticated user in need of some help. I have a study in which I have two random factors: subject and stimuli item. I also have 3 fixed effects: 1. y=similarity-based score (continuous values) 2. Age (has two levels "young" and "old") 3. x=grammatical-based score (continuous values). My dependent variable is z=item ratings from subjects. I am using the following formula to predict the effect of each factor on z: fit1 <- lmer(Z ~Age * X * Y + (1 | Subject) + (1 | Stimulus), method = "ML", data = d) furthermore, I have the option of using a different grammatical model in order to calculate values for X. I will call this X2. This means that I could also get a fit in the following way: fit2 <- lmer(Z ~Age * X2 * Y + (1 | Subject) + (1 | Stimulus), method = "ML", data = d)
Here are the questions:
1. Is there anyway to compare fit1 and fit2?
Dear Shabnam, yes. you can compare the impact of the two factors, but it is not done by doing an anova over the two above models. Also, you have to be aware that X1 and X2 may be collinear, which may make your result unreliable. see below 2. would anova (fit1, fit2) be appropriate in this case? (if possible,
would you be kind enough to give me references on the answer?)
no. anova() compares models where one model has a subset of the factors of the other model. the way to do what you need is to create a third model with X1 and X2 and all other factors contained in fit1 and fit2. then you compare this super-model (let's call it "superfit") against the two sub-models (fit1, fit2) anova(superfit, fit1) anova(superfit, fit2) if the first comparison is significant that means that X2 [sic] improves the model's data log-likelihood significantly (because X2 is the only factor contained in superfit, but not fit1). If the second comparison is significant, this means that X1 contributes significantly to the model. so, there are four possible results (where by "matter" I mean that the factor improves the model's data log-likelihood significantly): 1. X1, but not X2 matters 2. X2, but not X1 matters 3. both X1 and X2 matters 4. neither X1 and X2 improve fit2 and fit1, respectively in the last case, you have to make sure that X1 or X2 contribute significantly to fit1 and fit2, respectively (by comparing fit1 to a model without X1 and comparing fit2 to a model without X2). If so, then X1 and X2 are significant, but too collinear to be distinguished between (using your data set). even though model comparisons based on data log-likelihood (anova(model1, model2)) are considered relatively robust against problems with collinear factors, collinearity between X1 and X2 could be a problem (generally, if X1 and X2 are too collinear in your data set, then you will have a hard time distinguishing between them).
3. could I use the same formula to examine only one "Age" group (removing "Age" as a factor in the formula, of course), even if I am going to later proceed and re-examine the data for a larger young/old set?
I am not sure that I understand what you mean. Nothing prevents you from exploring e.g. only data from "young" people, but be aware that whatever conclusions you draw out of the examination of that subset of your data, may not generalize to the entire sample (and hence not to the population represented by the entire sample). I hope this helps. Let me know, if something is unclear. Florian Any help is greatly appreciated.
-shabnam _______________________________________________ R-lang mailing list [email protected] https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang
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