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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 pac
akge([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]
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