Hello Ben, Thanks a lot for your feedback! I get it now, I think. My biggest concern was that the fitting itself, getting such an error made me a bit distrustful of the fitting result. But if I understand you correctly, I can leave the contrast out while fitting the model, and then just "manually" check the contrasts. I can forward sample data and the code if you think it is helpful (thanks for making me realize about the typo in the contrasts, should be 0, 1, -1), but you have already solved the issue for me. Thanks again,
On 29 Feb 2016 19:57, Ben Bolker <bbolker at gmail.com> wrote:
PS if you have three levels of factor B, you can have at most two contrasts associated with the factor *when you fit the model*.? You can use the effects or lsmeans or contrast packages (or probably others) to compute the values and inferential statistics on the contrasts after you've fitted the model. On Mon, Feb 29, 2016 at 1:54 PM, Ben Bolker <bbolker at gmail.com> wrote:
Are your F_B_C2 and F_B_C3 contrasts really identical, or is that a typo? A reproducible example would be nice ... On Mon, Feb 29, 2016 at 5:22 AM, Daniel Preciado <danprec at hotmail.com> wrote:
Hello, I am trying to use lme() to fit and compare different models to data from an experiment in a repeated measures design. My dependent variable is response time (RT, in milliseconds); and I have 2 factors: F_A (2 levels) and F_B (3 Levels). For F_B, I have specified the following contrasts: F_B_C1 <- c(1, -1, 0)????? # Contrast prize 1 and 2 levels F_B_C2 <- c(1, 0, -1)????? # Contrast prize 1 with Neutral (no prize) F_B_C3 <- c(1, 0, -1)????? # Contrast prize 2 with Neutral (no prize) F_B_C4 <- c(1, 1, -2)????? # Contrast prize with Neutral contrasts(Data$F_B, how.many=4) <- cbind(F_B_C1, F_B_C2, F_B_C3, F_B_C4) Conditions 1 and 2 are 2 levels of the same manipulation, condition 3 is a neutral control. I am interested in the effect of each level (individually) on RT, and overall in the difference between the experimental manipulation (pooling the first 2 conditions of factor B) and the control condition (final condition of factor B). I defined the lme() models step-wise, starting with a Baseline model, and then updating that one to include each factor individually, and finally the interaction: RT_Base <- lme(RT ~ 1, random = ~1|SubjID/F_A/F_B, data=Data, method="ML")? #Baseline model RT_F_A <- update(RT_Base, .~. + F_A)??????????? #Baseline + F_A RT_F_B <- update(RT_F_A, .~. + F_B)???????????? #(Baseline+F_A) + F_B RT_Full <- update(RT_F_B, .~. + F_A:F_B)??????? #Full model (+ interaction) However, when I execute the code involving F_B, I get an "Error in MEEM (...): Singularity in Backsolve at level 0, block 1). I can still inspect the results of the model, but I would like to understand where is this error coming from, what does it mean, and how to avoid it. Furthermore, I realized that if I reduce the amount of contrasts to the default 2, the code runs without any error, so I can only assume that it has something to do with the user-specified comparison pairs. Also, the specified contrasts are not displayed (only the default first 2). I also read in some answer that the intercept needed to be suppressed in order to prevent this error (by adding RT ~ 0+Factors to the model formulae). I tried that, but it produces the same error. I would appreciate any feedback regarding this, Thanks! ???????? [[alternative HTML version deleted]]
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