Hello, and thanks for your time reading this. I'm trying to test
interactions of my dataset, in which the all of the factors are within the
same column.
Type Vol
1 CMass -4.598
2 BBack -4.605
3 BMass -4.602
4 CMass -4.601
5 CBack -4.605
6 CMass -4.604
7 CMass -4.602
8 CMass -4.604
9 CBack -4.605
10 BBack -4.503
11 CMass -4.605
Im attempting to determine the interaction effects of B or C on Mass and
Back
Looking for a set up as such Volume ~ CorB + MassorBack + CorB:MassorBack
is there an easy way to arrange the data so I can have the factors in column
1 broken down as I'd like?
Here if my current setup of the situation, In which I don't consider the
interactions. please forgive the armature coding.
if (T) {
#Arrange all data in a 2 column matrix as such: [Tissue Type, Measure]
measure = matrix(NA,4812,2)
measure=data.frame(measure)
for (i in a:b) {
#loads threshold factor
measure[,1] = data[,1]
#loads ith threshold
measure[,2] = data[,i]
measure$X1=factor(measure$X1, levels
c('CMass','BMass','CBack','BBack'))
measure.aov= aov(X2 ~ X1,data = measure)
#prints results
print(TukeyHSD(measure.aov, order= TRUE, conf.level = .995))
}
}
--
View this message in context:
http://r.789695.n4.nabble.com/help-setting-up-crossed-data-tp4651674.html
Sent from the R help mailing list archive at Nabble.com.
