Hello,
I sent an email out earlier, and I don't blame you for ignoring this
because of that.

I have a repeated measures situation and would like some help to determine
if this is 1) appropriate, and 2) how to properly interpret the results.
Basically, I have repeated measures of "Y" recorded in consecutive years
(94:98) taken at approximately the same time (July) at the same 6 sites; Y
is a percentage that I would like to regress along the continuous variable
called "env.gradient".

I am trying to follow Venables and Ripley (2002), Chapter 10; but this is
confusing and I really need to figure this out soon.   I'm sure this is
easy as pie to many of you, and please believe me that your guidance in
this situation will be the catalyst to help me understand this stuff (and I
plan on reading plenty ASAP).

I basically just want to know if Y is linearly related to env.gradient,
while accounting for repeated measures.

# Here are the data
#################################################################
dat <- structure(list(site = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L), .Label = c("C1", "C2", "C3", "Q1",
"Q2", "Q3"), class = "factor"), year = c(94L, 95L, 96L, 97L,
98L, 94L, 95L, 96L, 97L, 98L, 94L, 95L, 96L, 97L, 98L, 94L, 95L,
96L, 97L, 98L, 94L, 95L, 96L, 97L, 98L, 94L, 95L, 96L, 97L, 98L
), env.gradient = c(18, 18, 18, 18, 18, 3.5, 3.5, 3.5, 3.5, 3.5,
14, 14, 14, 14, 14, 13, 13, 13, 13, 13, 8, 8, 8, 8, 8, 6.5, 6.5,
6.5, 6.5, 6.5), Y = c(0.171158638926407, 0.169713288641782,
0.152153933837569,
0.197575217418616, 0.203910975101867, 0.0750279002284801,
0.170743241890423,
0.124641793138257, 0.114178607721316, 0.094511173181886, 0.246397132453605,
0.204852712848377, 0.127234745915353, 0.250078422865994, 0.167826032256711,
0.179431760128959, 0.197893902312921, 0.129699846863689, 0.149706090438842,
0.138852795878536, 0.125088313507783, 0.178352158019673, 0.116281351771267,
0.147501950163454, 0.118865506981108, 0.0924414290963082,
0.144984499149044,
0.132532609540349, 0.137715018373314, 0.134704824360581)), .Names =
c("site",
"year", "env.gradient", "Y"), class = "data.frame", row.names = c(NA,
-30L))

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