[R-sig-ME] magnitude of random effect vs significance
Mike Dunbar
mdu at ceh.ac.uk
Tue Sep 4 21:04:16 CEST 2007
Following on from previous recent post, here is an example of a random effect which is tiny but highly significant. I've got no problem explaining a fixed effect which is tiny but significant (ie precisely estimated), but I'm struggling here!
regards
Mike
# read in temp3 first below
varcor.2h.crustacea.hf <- lme(log(crustdens+1) ~ HEIGHT, random=~1|MONTH/TIME/TRANSECT/POLE, data=temp3)
VarCorr(varcor.2h.crustacea.hf)
varcor.2h.crustacea.nomonth.hf <- lme(log(crustdens+1) ~ HEIGHT, random=~1|TIME/TRANSECT/POLE, data=invdens.bottommiddle)
anova(varcor.2h.crustacea.hf,varcor.2h.crustacea.nomonth.hf)
# month random effect of very low magnitude, yet it it highly significant: how can I explain this, or have I made a mistake!
temp3 <-
structure(list(MONTH = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L
), .Label = c("4", "5", "6", "7"), class = "factor"), TRANSECT = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = "factor"),
POLE = structure(c(1L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L,
6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L,
13L, 13L, 14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L, 1L, 2L,
2L, 3L, 4L, 4L, 5L, 5L, 5L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L,
10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 16L, 17L,
17L, 18L, 18L, 1L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L,
7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L,
13L, 14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L, 1L, 1L, 2L,
2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L,
10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 16L, 17L,
17L, 18L, 18L, 1L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L,
7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L,
13L, 14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L, 1L, 2L, 2L,
3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L,
10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 16L, 17L,
17L, 18L, 18L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L,
7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L,
14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L, 1L, 1L, 2L, 2L, 3L,
4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L,
11L, 11L, 13L, 13L, 14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L,
1L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L,
9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L,
15L, 16L, 17L, 17L, 18L, 18L, 1L, 1L, 2L, 2L, 3L, 4L, 4L,
5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L,
12L, 12L, 13L, 13L, 14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L,
1L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L,
9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L,
15L, 16L, 17L, 17L, 18L, 18L, 1L, 1L, 2L, 2L, 3L, 4L, 4L,
5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L,
12L, 12L, 13L, 13L, 14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L,
1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L,
9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L,
16L, 17L, 17L, 18L, 18L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L,
6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L,
12L, 13L, 13L, 14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L, 1L,
2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L,
10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 16L,
17L, 17L, 18L, 18L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 5L, 6L, 6L,
7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L,
13L, 14L, 14L, 15L, 16L, 17L, 17L, 18L, 18L), .Label = c("11",
"12", "13", "14", "23", "24", "31", "32", "33", "34", "41",
"42", "43", "44", "51", "52", "53", "54"), class = "factor"),
TIME = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("1",
"2", "3", "4"), class = "factor"), HEIGHT = structure(c(1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
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1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L), .Label = c("1", "2", "3"
), class = "factor"), crustdens = c(0, 0, 0, 11.99, 14.57,
45, 0, 0, 0, 26.9, 0, 21.79, 19.82, 22.24, 9.14, 51.92, 22.58,
105.25, 78.68, 26.07, 11.83, 15.78, 112.14, 14.34, 36.69,
19.96, 37, 37.35, 48.65, 11.78, 0, 7.48, 7.44, 13.4, 0, 0,
15.3, 0, 29.12, 60.15, 31.17, 56.1, 0, 0, 0, 0, 0, 21.88,
101.09, 101.35, 176.16, 51.95, 36.66, 21.9, 61.38, 219.24,
61.5, 19.04, 47.05, 50.26, 175.78, 12.26, 22.88, 31.44, 8.97,
29.82, 56, 136.4, 169.66, 134.53, 161.76, 42.16, 171.8, 183,
96.24, 43.15, 147.15, 143.68, 157.08, 61.53, 272.44, 163.8,
498.96, 407.16, 76.16, 22, 178.71, 190.76, 393.6, 324.48,
318.24, 161.88, 222.56, 320.95, 73.04, 106.25, 206.82, 106.4,
12.92, 0, 45.2, 0, 42.88, 0, 10.45, 67.52, 0, 0, 0, 18.81,
13.06, 17.48, 8.19, 0, 100.76, 135.9, 53.22, 8.45, 11.58,
24.96, 75.04, 87.92, 248.56, 55.68, 46.3, 30.76, 137.76,
51.76, 23.24, 14.4, 70.3, 114.03, 0, 28.1, 0, 14.76, 0, 0,
16.39, 0, 16.83, 0, 0, 13.82, 16.64, 9.14, 0, 0, 40.59, 0,
72.72, 0, 0, 10.4, 79.5, 0, 105.8, 0, 0, 12.08, 0, 0, 38.95,
0, 294.3, 156.3, 78.4, 243.72, 201.1, 55.48, 104.55, 33.44,
111.96, 0, 215.4, 18.31, 86.08, 67.68, 77.98, 68, 180.7,
63.42, 223.96, 78.8, 37.8, 51.35, 496.08, 107.9, 152.88,
78.32, 239.28, 78.32, 84.76, 39.54, 48.66, 46.3, 28.74, 23.12,
10.56, 27.76, 83.04, 65.65, 334.22, 143.57, 200.97, 24.22,
34.74, 30.2, 109.78, 34.8, 89.68, 58.52, 207.35, 54.9, 148.58,
46, 116.64, 84.16, 536, 131.78, 149.76, 103.18, 91.32, 82.94,
137.34, 26.92, 56.04, 29.19, 16.47, 11.12, 0, 0, 0, 39.38,
0, 0, 0, 0, 0, 0, 0, 0, 8.73, 17.66, 0, 48.81, 50.72, 59.82,
32.67, 232.56, 0, 40.4, 0, 22.94, 6.42, 17.28, 5.99, 24.93,
18.8, 47.45, 0, 0, 0, 25.69, 30.76, 0, 0, 0, 0, 0, 0, 0,
0, 0, 22.41, 0, 0, 0, 0, 13.29, 0, 0, 17.48, 0, 0, 0, 0,
0, 0, 8.05, 16.29, 0, 84.44, 129.2, 237.64, 148.56, 484.25,
45.6, 15.84, 80.52, 29.93, 216.92, 0, 274.68, 82.77, 117.27,
32.49, 112.35, 74.62, 289.24, 132.8, 262.08, 69.86, 41.08,
54.99, 63.25, 100.9, 247.78, 123.84, 120.8, 95.5, 119.88,
95.68, 65.22, 103.18, 884.51, 441.25, 407.93, 325.44, 539.03,
307.35, 85.6, 452.76, 285.67, 839.16, 0, 5537.4, 111.2, 205.44,
149.4, 645.57, 252.45, 849.87, 477.84, 492.1, 250.38, 265.44,
142.2, 164.43, 723.58, 438.72, 181.32, 407.34, 295.46, 371.69,
132.96, 297.54, 295.68, 0, 42.48, 47.22, 0, 50.76, 19.87,
27.6, 58.95, 0, 0, 0, 0, 54.96, 25.74, 0, 15.73, 0, 40.38,
48.66, 149.13, 30.3, 41.36, 18.46, 12.62, 0, 37.78, 27.28,
73.26, 38.56, 40.17, 7.4, 0, 29.54, 0, 0, 16.6, 61.68, 47.24,
0, 0, 0, 0, 0, 381.03, 0, 68.44, 0, 47.86, 0, 0, 21.08, 12.17,
0, 0, 23.75, 0, 0, 26.7, 0, 0, 0, 0, 0, 0, 15.88, 407.76,
482.56, 205.68, 254.16, 96.6, 86.5, 644.49, 162.17, 0, 0,
536.48, 178.5, 197.67, 181.61, 275.33, 140.8, 311.63, 286.52,
158.16, 12.59, 131.13, 178.57, 139.5, 30.07, 142.8, 225.6,
226.92, 170.64, 131.52, 125.58, 257.6, 254.55, 282.8, 301.6,
44.46, 266.56, 222.6, 17.37, 834.24, 2537.56, 2818.35, 235.96,
456.88, 176.88, 205.2, 116.28, 440.37, 166.92, 253.82, 200.61,
224.7, 119.07, 91.86, 108.85, 321.75, 113.08, 113.2, 62.52,
214.92, 80.4, 354.73, 93.6, 162.36, 203.71, 0, 66.02, 0,
36.53, 49.59, 0, 76.04, 0, 0, 0, 0, 32.07, 0, 0, 0, 13.94,
118.11, 0, 46.24, 42.9, 0, 0, 0, 30.55, 0, 0, 18.77, 14.03,
0, 0, 0, 16.41)), .Names = c("MONTH", "TRANSECT", "POLE",
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