Two factors -> nurical data dependency analyzing
Prof Brian Ripley ?????:
On Sun, 19 Feb 2006, Evgeniy Kachalin wrote:
Hello, dear R users. What is the easiest and the most visualli understandable way to analize dependency of numerical variable on two factors?
interaction.plot() is a good start.
Is the boxplot(y~f1+f2) the good way? It seems that this formula does not work.
No, nor is it documented to: the help page is there to help you. You need a single factor as the grouping, so make one via an interaction. boxplot(y ~ f1:f2) should work. E.g. library(MASS) boxplot(FL ~ sex:sp, data=crabs)
Does not work: ???????????? ?? if (any(out[nna])) stats[c(1, 5)] <- range(x[!out], na.rm = TRUE) : ?????????????????????? ????????????????, ?? ?????????? TRUE/FALSE ????????????????: Warning messages: 1: + not meaningful for factors in: Ops.factor(x[floor(d)], x[ceiling(d)]) 2: < not meaningful for factors in: Ops.factor(x, (stats[2] - coef * iqr)) 3: > not meaningful for factors in: Ops.factor(x, (stats[4] + coef * iqr)) Hm...
Another idea is to use lattice's bwplot. E.g. library(lattice) bwplot(FL ~ sex | sp, data=crabs)
That's not the point. The scales may differ significantly, also this is not conviniet for many factors.