search for: facc

Hi, I have a question about how I should report the results for a linear mixed effects model where the model includes as predictors three factors (facA, facB and facC), one of which (facA) interacts with the other two. facA and facB have two levels and facC has 3 levels. There are also several other continuous predictors (e.g. varA, varB, varC). My mixed model is specified with the following formula: model <- lmer(RT~ facA*facB*facC - facA:facB:facC - facB:f...

...es. But I must be missing something... Here's an example (running R 1.9.1 on Win2k): # generate some data df1 <- data.frame(expand.grid(obsnum=seq(1, 15, 1), faca=c("A1", "A2", "A3"), facb=c("B1","B2", "B3", "B4"), facc=c("C1","C2")), dv=rpois(15*3*4*2,10)) # now get rid of the cell B4 & C1 to simulate a missing treatment combination df2 <- df1[df1$facb !="B4" | df1$facc !="C1", ] # plain vanilla lattice plot generates an empty panel corresponding to the empty cel...

Dear All: I apologize for asking such an elementary question, but I could not find an adequate response on line. I am hoping to receive some help with the interpretation of the Intercept coefficient in the gam model below. I1 through I3 are dummy coded "Item difficulty" parameters in a data set that includes 4 items. If the Intercept is the value of Y when all other terms are 0, am I

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