library(ggplot2)
library(rrcov)
library(reshape2)
library(lme4)
## Create
dat <- read.table(header = TRUE, text = "
group x y
o -2 1
o 0 4
o 1 1
o 2 2
o 3 -5
o 3 2
o 3 4
o 4 2
o 5 3
o 6 2
c -5 -2
c -4 0
c -4 0
c -1 -2
c -1 0
c -1 -2
c -1 0
c 0 -1
c 0 0
c 0 2
")
## Graph
ggplot(data = dat,
mapping = aes(x = x, y = y, color = group)) +
layer(geom = "point", size = 5) +
theme_bw() +
theme(legend.key = element_blank())
## Using manova
resManova <- manova(cbind(x,y) ~ group, data = dat)
summary(resManova)
## Df Pillai approx F num Df den Df Pr(>F)
## group 1 0.562 10.9 2 17 0.00089 ***
## Residuals 18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## One-way MANOVA (Bartlett Chi2) (same as manova())
Wilks.test(group ~ x + y, data = dat, method = "c")
##
## One-way MANOVA (Bartlett Chi2)
##
## data: x
## Wilks' Lambda = 0.4378, Chi2-Value = 14.04, DF = 2.00, p-value = 0.0008933
## sample estimates:
## x y
## c -1.7 -0.5
## o 2.5 1.6
## Robust One-way MANOVA (Bartlett Chi2)
Wilks.test(group ~ x + y, data = dat, method = "mcd")
##
## Robust One-way MANOVA (Bartlett Chi2)
##
## data: x
## Wilks' Lambda = 0.324, Chi2-Value = 4.754, DF = 1.143, p-value = 0.03609
## sample estimates:
## x y
## c -1.700 -0.500
## o 2.444 2.333
## One-way MANOVA (Bartlett Chi2) rank based?
Wilks.test(group ~ x + y, data = dat, method = "rank")
##
## One-way MANOVA (Bartlett Chi2)
##
## data: x
## Wilks' Lambda = 0.3976, Chi2-Value = 15.68, DF = 2.00, p-value = 0.0003936
## sample estimates:
## x y
## c 6.35 6.9
## o 14.65 14.1