I am running a large mixed model, 65k entries on 11 fixed effects and one
random. One of the fixed effects is "Course" a factor that takes on 14
different values
>levels(Master.complete$Course)
 [1] "B101"  "B2A"   "B2B"   "B2C"   "C118A" "C118B" "C118C" "C2A"   "C2B"
[10] "C2C"   "N101"  "P7A"   "P7B"   "P7C"

and another "Yfrm7A" that is continuous
 >summary(Master.complete$Yfrm7A)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
-5.25000 -0.75000  0.00000 -0.07688  0.50000  6.00000

but all the values are 0(zero) when course==P7A
>summary(Master.complete$Yfrm7A[Master.complete$Course=="P7A"])
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
      0       0       0       0       0       0

Thus when I run the following mixed model I get no errors
fit.full <- lme(fixed= zGrade ~ Rep + ISE
+Yfrm7A+Ufrm7A+Female+White+HSGPA+MATH+AP_TOTAL+Years+Course +
                  Course*Rep + Course*Female +Course*White,
                random= ~1|SID, data = Master.complete,
na.action=na.exclude)

but if I add in a Course*Yfrm7A term
fit.full <- lme(fixed= zGrade ~ Rep + ISE
+Yfrm7A+Ufrm7A+Female+White+HSGPA+MATH+AP_TOTAL+Years+Course +
+                   Course*Rep + Course*Female +Course*White+Course*Yfrm7A,
+                 random= ~1|SID, data = Master.complete,
na.action=na.exclude)

I get

Error in MEEM(object, conLin, control$niterEM) :
  Singularity in backsolve at level 0, block 1

I suspect I could solve this problem with ordering the levels of "Course"
so that P7A was the first level and thus the one that others were compared
to but I am unclear on how to do so.

Robert

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