plot.SK: Plot SK objects

In ScottKnott: The ScottKnott Clustering Algorithm

Plot SK objects

Description

S3 method to plot SK objects.

Usage

## S3 method for class 'SK'
plot(x,
     result         = TRUE,
     replicates     = TRUE,
     pch            = 19,
     col            = NULL,
     xlab           = NULL,
     ylab           = NULL,
     xlim           = NULL,
     ylim           = NULL,
     axisx          = TRUE,
     axisy          = TRUE,
     id.lab         = NULL,
     id.las         = 1,
     yl             = TRUE,
     yl.lty         = 3,
     yl.col         = 'gray',
     dispersion     = c('mm','sd','ci','cip'),
     d.lty          = 1,
     d.col          = 'black',
     title          = '', ...)

Arguments

x	A SK object.
result	The result of the test (letters) should be visible.
replicates	The number of replicates should be visible.
pch	A vector of plotting symbols or characters.
col	A vector of colors for the means representation.
xlab	A label for the ‘⁠x⁠’ axis.
ylab	A label for the ‘⁠y⁠’ axis.
xlim	The ‘⁠x⁠’ limits of the plot.
ylim	The ‘⁠y⁠’ limits of the plot.
axisx	If TRUE, the x axis is drawn using defaults; set to FALSE to suppress it.
axisy	If TRUE, the y axis is drawn using defaults; set to FALSE to suppress it.
id.lab	Factor level names at ‘⁠x⁠’ axis.
id.las	Factor level names written either horizontally or vertically.
yl	Horizontal (reference) line connecting the circle to the ‘⁠y⁠’ axis.
yl.lty	Line type of ‘⁠yl⁠’.
yl.col	Line color of ‘⁠yl⁠’.
dispersion	Type of dispersion bar drawn through each mean point. Options: ‘⁠mm⁠’ (min-max range), ‘⁠sd⁠’ (standard deviation), ‘⁠ci⁠’ (individual confidence interval), ‘⁠cip⁠’ (pooled confidence interval). Default is ‘⁠mm⁠’.
d.lty	Line type of dispersion.
d.col	A vector of colors for the line type of dispersion.
title	A title for the plot.
...	Optional plotting parameters.

Details

The plot.SK function is an S3 method for plotting SK objects. It generates a series of points representing the treatment means, optionally with vertical dispersion bars. The ‘⁠ci⁠’ option is calculated using each treatment's own variance as an estimate of the population variance. The ‘⁠cip⁠’ option is calculated using the mean square error (MSE) as an estimate of the population variance.

Author(s)

Faria, J. C. ([e-mail])
Jelihovschi, E. G. ([e-mail])
Allaman, I. B. ([e-mail])

References

Murrell, P. (2005) R Graphics. Chapman and Hall/CRC Press.

See Also

plot

Examples

##
## Examples: Completely Randomized Design (CRD)
## More details: demo(package='ScottKnott')
##

library(ScottKnott)
data(CRD2)

## From: formula
sk1 <- with(CRD2,
            SK(y ~ x,
               data=dfm,
               which='x'))

old.par <- par(mar=c(6, 3, 6, 2))
plot(sk1,
     id.las=2)

plot(sk1,
     yl=FALSE,
     dispersion='sd',
     id.las=2)

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

sk2 <- SK(x=av,
          which='x')

col=c(rep(2, 6),
      rep(3, 36),
      rep(4, 1),
      rep(5, 2))

plot(sk2,
     dispersion='sd',
     yl=FALSE,
     id.las=2,
     col=col,
     d.col=col)

## From: lm
av_lm <- with(CRD2,
           lm(y ~ x,
              data=dfm))

sk3 <- SK(x=av_lm,
          which='x')

par(mfrow=c(2, 1))
plot(sk2,
     dispersion='ci',
     yl=FALSE,
     id.las=2,
     col=col,
     d.col=col)

plot(sk2,
     dispersion='cip',
     yl=FALSE,
     id.las=2,
     col=col,
     d.col=col)

par(mfrow=c(1, 1))
par(old.par)

ScottKnott documentation built on May 24, 2026, 5:06 p.m.