I'm using a leave-one-out method to evaluate how well a model with one datapoint excluded predicts that datapoint (rotating through all datapoints). The code below has successfully run on essentially the same data with a slightly different DV, so I'm stumped as to why I'm getting the error that I'm getting. Here's the relevant chunk of code:
dataPennTrim.lmer <- lmer(logDur.PENN~cNewNounDen*ContextCode+
Vowel.Contrasts+BlockCode+
(1|subject)+(0+ cNewNounDen +ContextCode|subject)+
(1|word)+(0+ContextCode|word),
data=pennTrim,
control = lmerControl(optimizer = "bobyqa"),REML=FALSE)
pennPred <- predict(dataPennTrim.lmer, newdata = dataFull2)
dataFull2 has the same columns as pennTrim, it just has more rows. Pretty standard use of the predict() function. I get this error:
Error in t(.Call(Csparse_dense_crossprod, y, x)) :
error in evaluating the argument 'x' in selecting a
method for function 't': Error: Cholmod error 'X and/or Y
have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90
Any thoughts about what might be causing this error? I can use essentially the same code with the same dataframes swapping out logDur.PENN for logDur.Manual (measurements from a different source) and the code gives no errors.
