Hi all,

I've just started with glmm and have been working on my data for soil
respiration. The data is a continuous soil respiration measured over time
on different treatments (Control, Litter, Root, SOM, and Myc). I've made a
reproducible sample subset of data.

sample.glmm2<-structure(list(Rep = structure(c(1L, 1L, 2L, 3L, 2L, 3L, 2L,
1L, 1L, 1L, 2L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("T1",
"T2", "T3"), class = "factor"), Port = c(4L, 4L, 3L, 2L, 1L,
4L, 4L, 1L, 3L, 4L, 2L, 2L, 4L, 4L, 3L, 4L, 3L, 1L, 3L, 3L),
    Time = c(40103.23538, 40196.53689, 40015.52118, 40191.30837,
    40131.5086, 40191.21449, 40130.82421, 40146.25, 40071.55473,
    40101.5565, 40132.45973, 40136.52361, 40065.7656, 40056.5364,
    40142.20439, 40032.0448, 40075.73726, 40011.48273, 40143.37164,
    40122.35059), Lin_Flux = c(4.69, 2.4, 7.87, 11.05, 6.94,
    7.2, 3.33, 7.92, 5.28, 4.83, 1.95, 8.29, 9.79, 6.74, 1.37,
    4.95, 2.37, 7.24, 1.73, 2.33), temp = c(24.661, 22.555, 25.993,
    24.59, 24.21, 23.784, 25.991, 22.719, 25.75, 24.992, 25.057,
    25.977, 26.015, 26.558, 23.837, 25.742, 22.652, 25.096, 24.186,
    25.561), Moist = c(53.12073034, 73.17318785, 10.85104703,
    32.32684789, 68.78023321, 47.22009228, 70.96981555, 252.2126268,
    60.74723213, 49.6945396, 61.99900286, 60.96778851, 45.67500636,
    48.84389487, 80.87678452, 43.51131005, 65.55899378, 3.546478647,
    77.23567152, 64.29384304), Lit = c(0L, 0L, 0L, 0L, 1L, 0L,
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L),
    Root = c(1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L,
    1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L), SOM = c(1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
    1L), Myc = c(1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
    0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("Rep", "Port",
"Time", "Lin_Flux", "temp", "Moist", "Lit", "Root", "SOM", "Myc"
), row.names = c(7031L, 2139L, 28270L, 30410L, 18838L, 30448L,
19109L, 2343L, 10930L, 7714L, 18455L, 32126L, 36769L, 24979L,
3783L, 14847L, 9266L, 17254L, 3313L, 5315L), class = "data.frame")

As for the Litter, SOM, Myc, and Root - 1 denotes respiration contribution
to Lin_Flux, while 0 denotes that the components does not contribute to
Lin_Flux.

I've tried to fit a model for Rep (repilcate) and Time as random effects
using lmer. For example:

glmm.model1 <- lmer(Lin_Flux ~ temp + Moist + Port + Lit + Root +
(Time|Rep), data=sample.glmm, family="gaussian")

I obtained output. However, when i try to include SOM and Myc, as follow:

glmm.model1 <- lmer(Lin_Flux ~ temp + Moist + Port + Lit + Root + SOM + Myc
+ (Time|Rep), data=sample.glmm, family="gaussian")

I get error message:
Error in mer_finalize(ans) : Downdated X'X is not positive definite, 6.

Is this due to singularity?
If so, is there a way to go around this? I've come across a few of these
problems - and perhaps the way to do this is to remove the singularities?
Does this mean, to remove the predictor?

Sorry for such long questions. Hope to hear from all.
Thanks so much.

Khoon

	[[alternative HTML version deleted]]

