plot.TukeyC: Plot TukeyC and TukeyC.nest Objects

Description Usage Arguments Details Author(s) References See Also Examples

View source: R/plot.TukeyC.R

Description

S3 method to plot TukeyC and TukeyC.nest objects.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
## S3 method for class 'TukeyC'
plot(x,
     result         = TRUE,
     replicates     = TRUE,
     pch            = 19,
     col            = NULL,
     xlab           = NULL,
     ylab           = NULL,
     xlim           = NULL,
     ylim           = NULL,
     id.lab         = NULL,
     id.las         = 1,
     rl             = TRUE,
     rl.lty         = 3,
     rl.col         = 'gray',
     dispersion     = c('none','mm','sd','ci','cip'),
     dispersion.lty = 1,
     title          = '', ...)

Arguments

x

A TukeyC 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.

id.lab

Factor level names at x axis.

id.las

Factor level names written either horizontally or vertically.

rl

Horizontal line connecting the circle to the y axis.

rl.lty

Line type of rl.

rl.col

Line color of rl.

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.

dispersion.lty

Line type of dispersion.

title

A title for the plot.

...

Optional plotting parameters.

Details

The plot.TukeyC function is a S3 method to plot Tukey and TukeyC.nest 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)

Jos<e9> Cl<e1>udio Faria ([email protected])
Enio Jelihovschi ([email protected])
Ivan Bezerra Allaman ([email protected])

References

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

See Also

plot

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
##
## Examples: Completely Randomized Design (CRD)
## More details: demo(package='TukeyC')
##

library(TukeyC)
data(CRD2)

## From: formula
tk1 <- with(CRD2,
            TukeyC(y ~ x,
                   data=dfm,
                   which='x'))

old.par <- par(mar=c(6, 3, 6, 2))
plot(tk1,
     id.las=2)

plot(tk1,
     dispersion='sd',
     mm.lty=3,
     id.las=2,
     rl=FALSE)

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

tk2 <- TukeyC(x=av,
              which='x')
plot(tk2,
     rl=FALSE,
     id.las=2)

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

tk3 <- TukeyC(x=av_lm,
              which='x')
plot(tk3,
     dispersion='ci',
     id.las=2,
     rl=FALSE)

plot(tk3,
     dispersion='cip',
     id.las=2,
     rl=FALSE)


par(old.par)

TukeyC documentation built on Jan. 14, 2018, 1:10 a.m.