[R] how to exclude level 1 residuals from multilevel model with lme4
elena.wolf86 at gmail.com
Wed Jan 29 10:29:59 CET 2014
I have the following problem: I have a multilevel model with two level 1 nominal predictors. My level one predictors are experimental condition nested in persons. I used the R-package lme4
my data file looks like that:
(DV = dependent variable, IV = independent variable)
I want to do the following random slope model:
rs.model <- lmer(DV~ 1 + IV1 * IV2 + (1 + IV1 * IV2 | id), data=dat)
I want to integrate the interaction term as a varying slope because I assume that the interaction will look different for different individuals. (I know I can do a repeated measure ANOVA, but this won't help me as I want to explain this different interaction patterns with a between subjects Level 2 Variable (IV3) which is metric). The final model should be:
rs.model.final <- lmer(DV~ 1 + IV1 * IV2 * IV3 (1 + IV1 * IV2 | id), data=dat)
The problem is, that actually my model is not allowed to have Level 1 residuals because every person has just one datapoint for the interaction of the experimental conditions (IV1*IV2). But if I use the usual lmer-function the package does include level 1 residuals although it shouldn't ... also en error message appears:
In checkZrank(reTrms$Zt, n = n, control, nonSmall = 1e+06) :
number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
Consequently I am not allowed to interpret my output although lmer calculates one.
My question is: Is there a possibility to exclude the level 1 residuals from the model? Or does someone know another package so I can calculate my model?
Hope someone can help me...
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