Hi,

I'm trying to analyze repeated measurements of body temperature data
collected from 7 randomly chosen subjects (e.g. turtles). I am using R,
along with the nparLD package to test for an effect of diel period (fixed
factor: day or night) and season (sub-plot fixed factor: spring, summer,
fall) on body temperature.

Based on this set-up (LD-F2), I am using the non-parametric nparLD
pacakge([url]http://www.inside-r.org/packages/cran/nparLD/docs/ld.f2[/url])
because data transformations were unsuccessful and I am randomly missing
some paired values.

Main issue/question: In R the nparLD ANOVA-type Test showed a significant
p-value for diel period, no effect of season, and no interaction between
diel period and season. But a post-hoc Wilcoxon Signed-Rank Test did NOT
find a significant difference (p = 0.054) for diel period (day vs night)
body temperature.

How is it possible to have a significant effect for day vs night, based on
the nparLD package, but NO significant difference between day and night for
the post-hoc Wilcoxon test?

Also, if I only have two levels of the fixed effect (day vs night), do I
need to run a post-hoc test or just look at the mean values after the
ANOVA-type test?

Data info:

The repeated measurements on the 7 subjects had 2 fixed effects:

1. Diel period (day or night)
2. Season (Spring, summer, and fall)(Subplot Factor)

Mean values for body temperature and for diel period are below. Diel column
(D=Day, N = Night). State column (RT=Spring, RF = Summer, PT = Fall).
Subject, N=7. NA = missing value.

All comments (good and bad) are greatly appreciated!

Thanks,
James



 -- output of sessionInfo():

[code]
> data=read.csv(file.choose(), header=TRUE)
> attach(data)
> data
  stp diel state subject
1  26.2    D    RT       1
2  26.4    N    RT       1
3  24.1    D    RT       2
4    NA    N    RT       2
5    NA    D    RT       3
6  25.2    N    RT       3
7  27.1    D    RT       4
8  26.5    N    RT       4
9  26.9    D    RT       5
10 27.1    N    RT       5
11 26.2    D    RT       6
12 26.0    N    RT       6
13 26.3    D    RT       7
14 26.7    N    RT       7
15 26.0    D    RF       1
16 26.6    N    RF       1
17 24.2    D    RF       2
18 25.6    N    RF       2
19 25.6    D    RF       3
20 26.6    N    RF       3
21 26.1    D    RF       4
22 26.9    N    RF       4
23 27.2    D    RF       5
24 27.4    N    RF       5
25 26.2    D    RF       6
26 26.7    N    RF       6
27 27.2    D    RF       7
28 27.5    N    RF       7
29 25.0    D    PT       1
30 24.8    N    PT       1
31   NA    D    PT       2
32   NA    N    PT       2
33   NA    D    PT       3
34   NA    N    PT       3
35 26.7    D    PT       4
36 26.9    N    PT       4
37 27.6    D    PT       5
38 27.5    N    PT       5
39 25.2    D    PT       6
40 24.9    N    PT       6
41 27.1    D    PT       7
42 27.0    N    PT       7


>ex.f2<-ld.f2(y=stp, time1=diel, time2=state, subject=subject,
time1.name="Diel", time2.name="State", description=FALSE)

> ex.f2$ANOVA.test
           Statistic       df    p-value
Diel       4.9028447 1.000000 0.02681249
State      0.2332795 1.374320 0.70586274
Diel:State 2.1937783 1.062943 0.13717393
[/code]

[code]
> detach(data)
> data=read.csv(file.choose(), header=TRUE)
> attach(data)
> data
    day night
1  26.2  26.4
2  26.0  26.6
3  25.0  24.8
4  24.2  25.6
5  25.6  26.6
6  27.1  26.5
7  26.1  26.9
8  26.7  26.9
9  26.9  27.1
10 27.2  27.4
11 27.6  27.5
12 26.2  26.0
13 26.2  26.7
14 25.2  24.9
15 26.3  26.7
16 27.2  27.5
17 27.1  27.0

> library(coin)

> wilcoxsign_test(day ~ night, distribution="exact")

        Exact Wilcoxon-Signed-Rank Test

data:  y by x (neg, pos)
         stratified by block
Z = -1.9234, p-value = 0.05482
alternative hypothesis: true mu is not equal to 0

[/code]

	[[alternative HTML version deleted]]

