I am modeling grade as a function of membership in various cohorts. There are four "cohorts". (NONE, ISE07,ISE08,ISE09) and two times of cohorts coded as ISE = TRUE (ISE0#) or FALSE (NONE). There is clear co-linearity but that is to be expected. running the following code CutOff <-0 fit.base <- lme(fixed= zGrade ~ Rep + COHORT/ISE + P7APrior + Female + White + HSGPA + MATH + AP_TOTAL + Years + EOP + Course, random= ~1|SID, data = share[share$GRADE >= CutOff,]) I get the following error Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 but if I take out the /ISE I get no error, simmilarly if I take out the COHORT/. I want to test for the effects of the different cohorts within the ISE subset and across ISE & NONE. Robert
I am modeling grade as a function of membership in various cohorts. There are four "cohorts". (NONE, ISE07,ISE08,ISE09) and two times of cohorts coded as ISE = TRUE (ISE0#) or FALSE (NONE). There is clear co-linearity but that is to be expected. running the following code CutOff <-0 fit.base <- lme(fixed= zGrade ~ Rep + COHORT/ISE + P7APrior + Female + White + HSGPA + MATH + AP_TOTAL + Years + EOP + Course, random= ~1|SID, data = share[share$GRADE >= CutOff,]) I get the following error Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 but if I take out the /ISE I get no error, simmilarly if I take out the COHORT/. I want to test for the effects of the different cohorts within the ISE subset and across ISE & NONE I can send the data (the whole is too large) if you wish. Robert [[alternative HTML version deleted]]