I would try one of these illustrations for starts.
interaction2wt (two-way tables) is designed to be used with aov() for testing.
interaction2wt shows all main effects and all two-way interactions for
many factors.
test <-
structure(list(item = structure(c(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), .Label = c("A",
"B"), class = "factor"), day = c(0L, 100L, 200L, 300L, 400L,
500L, 0L, 100L, 200L, 300L, 400L, 500L, 0L, 100L, 200L, 300L,
400L, 500L, 0L, 100L, 200L, 300L, 400L, 500L, 0L, 100L, 200L,
300L, 400L, 500L, 0L, 100L, 200L, 300L, 400L, 500L), set = c(1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L), value = c(1.08163365169503, 2.61998412608805, 3.07820466606394,
4.44993419381934, 5.29163171545805, 6.29155990999293, -0.123163011367676,
2.07767236834003, 2.32537052874901, 3.09372794501084, 6.65273721166635,
5.92304962329131, 1.50504697705548, 2.66253728086866, 2.63420157418685,
2.78195098580416, 6.47578642973288, 5.89587443775143, 0.848864231485078,
1.27549677119713, 2.19573089053609, 2.45659926134292, 5.15424403414103,
5.4813151140983, 1.25731482647214, 2.09662105167973, 1.75954023316977,
4.81624002288939, 4.65029189325307, 6.39946904227214, 0.944996929887344,
1.74667265331284, 2.42956264345558, 5.17852980415141, 3.5453435965834,
6.9011238437191)), .Names = c("item", "day", "set", "value"), row.names = c(NA,
-36L), class = "data.frame")
library(HH)
test$set <- factor(test$set)
test$day <- factor(test$day)
test$item <- factor(test$item)
interaction2wt(value ~ item * day * set, data=test)
test$item.day <- interaction(test$item, test$day)
position(test$item.day) <- outer(c(-10,10), as.numeric(levels(test$day)), `+`)
xyplot(value ~ as.position(item.day) | set, groups=item,
data=test, horizontal=FALSE, pch=c(17,16),
xlab="day",
scales=list(
x=list(
alternating=1,
at=levels(test$day), ## placement of tick labels and marks
tck=1)),
key=list(
text=list(c("A","B"), col=c("blue","red")),
points=list(pch=c(17, 16), col=c("blue","red")),
space="top", columns=2, border=TRUE),
layout=c(3,1))
## see also the examples in
demo(package="HH", bwplot.examples)
On Fri, Feb 6, 2015 at 6:09 AM, PIKAL Petr <petr.pi...@precheza.cz> wrote:
> Dear all
>
> I would like to ask for your opinion about possible graphical representation
> of such data.
>
>> dput(test)
> structure(list(item = structure(c(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), .Label = c("A",
> "B"), class = "factor"), day = c(0L, 100L, 200L, 300L, 400L,
> 500L, 0L, 100L, 200L, 300L, 400L, 500L, 0L, 100L, 200L, 300L,
> 400L, 500L, 0L, 100L, 200L, 300L, 400L, 500L, 0L, 100L, 200L,
> 300L, 400L, 500L, 0L, 100L, 200L, 300L, 400L, 500L), set = c(1L,
> 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
> 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
> 3L, 3L, 3L), value = c(1.08163365169503, 2.61998412608805, 3.07820466606394,
> 4.44993419381934, 5.29163171545805, 6.29155990999293, -0.123163011367676,
> 2.07767236834003, 2.32537052874901, 3.09372794501084, 6.65273721166635,
> 5.92304962329131, 1.50504697705548, 2.66253728086866, 2.63420157418685,
> 2.78195098580416, 6.47578642973288, 5.89587443775143, 0.848864231485078,
> 1.27549677119713, 2.19573089053609, 2.45659926134292, 5.15424403414103,
> 5.4813151140983, 1.25731482647214, 2.09662105167973, 1.75954023316977,
> 4.81624002288939, 4.65029189325307, 6.39946904227214, 0.944996929887344,
> 1.74667265331284, 2.42956264345558, 5.17852980415141, 3.5453435965834,
> 6.9011238437191)), .Names = c("item", "day", "set", "value"), row.names =
> c(NA,
> -36L), class = "data.frame")
>>
>
> One option I came with is
>
> library(ggplot2)
> p<-ggplot(test, aes(x=day, y=value, colour=item))
> p+geom_point()+stat_smooth(method="lm", formula= y~poly(x,2))
>
> but -
> I have more items (around 5-10), and I want to show if the difference between
> items is or is not significant. The actual development of value with day is
> usually not linear nor growing steadily and actually I cannot usually
> evaluate some analytical equation for my data to compare equation parameters.
>
> I thought about boxplots, but there is not many repetitions and actually 5+
> boxplots can be quite messy.
>
> I can plot only mean for each set and item but in that case I lose
> information if the difference is or is not significant.
>
> I appreciate any suggestion.
>
> Best regards
> Petr
>
>
>
>
>
>
>
>
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