[R] R-Help: nparLD Package Non-parametric Repeated Measures
Gerrit Eichner
Gerrit.Eichner at math.uni-giessen.de
Thu May 16 10:39:27 CEST 2013
Hello, James,
see my comments inline.
> ... 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?
Those tests -- in general -- test different hypotheses and use different
test statistics, the first one, in addition, using a distributional
approximation to obtain a p-value. You may want to look at the references
cited in the respective online help pages for technical details.
> 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?
Hm, actually no (of course depending on your scientific question) since
the ANOVA-table -- if you believe its p-value -- already tells you that
there is a (marginally) significant main "Diel-period"-effect, i.e., a
difference between day and night.
However, I recommend that you consult with a local statistician since this
is actually not an R-problem and since I have the impression that there
exist some uncertainties in your statistical expertise.
Hth -- Gerrit
> 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]]
>
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