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('none','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	Axis x? If ‘⁠TRUE⁠’ you must accept the default, otherwise, you must customize.
axisy	Axis y? If ‘⁠TRUE⁠’ you must accept the default, otherwise, you must customize.
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	Vertical line through the circle (mean value) linking the minimum to the maximum of the factor level values corresponding to that mean value. Other options are: sd (standard deviation), ci (confidence interval), cip (pooled confidence interval) and none.
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 a S3 method to plot ‘⁠Scott and Knott⁠’ objetcs. It generates a serie of points (the means) and a vertical line showing the dispersion of the values corresponding to each group mean. The ci options is calculed utilizing each treatment variance as estimating of population variance. The cip options is calculed utilizing the means square error (MSE) as estimating of population variance.

Author(s)

Jose Claudio Faria ([e-mail])
Enio Jelihovschi ([e-mail])
Ivan Bezerra Allaman ([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='SK')
##

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,
     disp='sd',
     id.las=2)

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

sk2 <- SK(x=av,
          which='x')
plot(sk2,
     disp='sd',
     yl=FALSE,
     id.las=2)

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

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

par(mfrow=c(2, 1))
plot(sk3,
     disp='ci',
     id.las=2,
     yl=FALSE)

plot(sk3,
     disp='cip',
     id.las=2,
     yl=FALSE)

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

ScottKnott documentation built on Aug. 31, 2023, 1:06 a.m.