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
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