data(meaudret)
pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 3)
rand1 <- randtest(discrimin(pca1, meaudret$design$season, scan = FALSE), 99)
rand1
#Monte-Carlo test
#Observation: 0.3035
#Call: as.randtest(sim = sim, obs = obs)
#Based on 999 replicates
#Simulated p-value: 0.001
plot(rand1, main = "Monte-Carlo test")
summary.manova(manova(as.matrix(meaudret$env)~meaudret$design$season), "Pillai")
# Df Pillai approx F num Df den Df Pr(>F)
# meaudret$design$season 3 2.73 11.30 27 30 1.6e-09 ***
# Residuals 16
# ---
# Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
# 2.731/9 = 0.3034
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