demo/CRD.R

In ScottKnott: The ScottKnott Clustering Algorithm

##
## Examples: Completely Randomized Design (CRD)
##

## The parameters can be: formula, aov, lm or lmer.

## Example 1: an small experiment
library(ScottKnott)
data(CRD1)

## From: formula - balanced
sk1 <- with(CRD1,
            SK(y ~ x,
               dfm))
sk1
summary(sk1)

plot(sk1)

plot(sk1,
     di='mm',
     d.lty=3)

plot(sk1,
     di='sd')

plot(sk1,
     di='ci',
     d.col='red')

## From: formula - unbalanced
u_sk1 <- with(CRD1,
              SK(y ~ x,
                 dfm[-1,]))
u_sk1
summary(u_sk1)

## From: aov - balanced
av1 <- with(CRD1,
            aov(y ~ x,
                data=dfm))
summary(av1)

sk2 <- SK(av1)
sk2
summary(sk2)

## From: lm - unbalanced
u_lm1 <- with(CRD1,
              lm(y ~ x,
                 data=dfm[-1,]))
summary(u_lm1)

u_sk2 <- SK(u_lm1)
u_sk2
summary(u_sk2)

## Example 2: a lot of groups
data(CRD2)

## From: data.frame (dfm) - balanced
sk3 <- with(CRD2,
            SK(y ~ x,
               dfm))
plot(sk3,
     id.las=2,
     yl=FALSE,
     di='sd',
     d.lty=3,
     d.col='red')

## From: data.frame (dfm) - unbalanced
u_sk3 <- with(CRD2,
              SK(y ~ x,
                 dfm[-1,]))
plot(u_sk3,
     id.las=2,
     yl=FALSE,
     di='sd',
     d.lty=3,
     d.col='red')

## From: aov - balanced
av2 <- with(CRD2,
            aov(y ~ x,
                data=dfm))
summary(av2)

sk4 <- SK(av2)
plot(sk4,
     id.las=2,
     yl=FALSE,
     di='sd',
     d.lty=4,
     d.col='darkgreen')

## From: lm - unbalanced
u_lm2 <- with(CRD2,
              lm(y ~ x,
                 data=dfm[-1,]))
summary(u_lm2)

u_sk8 <- SK(u_lm2)

plot(u_sk8,
     id.las=2,
     yl=FALSE)