plothulls: plothulls for data peeling
In aplpack: Another Plot Package: 'Bagplots', 'Iconplots', 'Summaryplots', Slider Functions and Others
Description
Usage
Arguments
Details
Value
Note
Author(s)
References
See Also
Examples
Description
plothulls plots convex hulls of a bivariate data set.
Usage
1
2
Arguments
x
two column matrix of the coordinates of points of x-values of a data set
y
if x is one dimensional then y contains the y-values of the data set
fraction
... of points that lies inside the hull to be plotted
n.hull
number of directions sequential hulls to be plotted
main
title for the graphics
add
if TRUE no new plot is initialized
col.hull
color(s) of the hull(s)
lty.hull
line type(s) of the hull(s)
lwd.hull
line width(s) of the hull(s)
density
density argument of polygon() that draws the hulls
...
further arguments used in the call of plot() or points()
Details
The function plothulls computes hulls of a bivariate data set using the
function chull. After finding a hull the hull maybe plotted.
Then the data points of the hull will be removed and
the hull of the remaining points is computed.
The style of plotting a hull depends on the setting of
col.hull, lty.hull, lwd.hull and density.
density=NA has the effect that the regions of the hulls are filled by a color.
Using fraction you can plot a single hull.
n.hull defines the number of hull that should be drawn one after the other.
Value
The hull(s) are stored as a list of matrices with two columns,
the innermost first and so on.
Note
Version of plothulls: 10/2013
Author(s)
Peter Wolf
References
Green, P.J. (1981): Peeling bivariate data.
In: Interpreting Multivariate Data, V. Barnett (ed.), pp 3-19, Wiley.
Porzio, Giovanni C., Ragozini, Giancarlo (2000):
Peeling multvariate data sets: a new approach. Quanderni di Statistica, Vol. 2.
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
# 10 hulls computed from the faithful data and plotted
plothulls(faithful, n.hull=10, lty.hull=1)
# plotting additionally a hull with 90 percent of points within the hull
plot(faithful)
plothulls(faithful, fraction=.90, add=TRUE, col.hull="red", lwd.hull=3)
# hull with 10 percent of points within the hull
plothulls(faithful, fraction=.10, col.hull="red", lwd.hull=3)
# first 3 hulls of the cars data set
n <- 3
plothulls(cars, n.hull=n, col.hull=1:n, lty.hull=1:n)
# 5 hulls represented by colored regions
n <- 5
cols <- heat.colors(9)[3:(3+n-1)]
plothulls(cars, n.hull=n, col.hull=cols, lty.hull=1:n, density=NA, col=0)
points(cars, pch=17, cex=1)
# 6 hulls: regions colored and boundaries shown
n <- 6
cols <- rainbow(n)
plothulls(cars, n.hull=n, col.hull=cols, lty.hull=1:n, density=NA, col=0)
plothulls(cars, n.hull=n, add=TRUE, col.hull=1, lwd.hull=2, lty=1, col=0)
Example output
Loading required package: tcltk
Warning message:
no DISPLAY variable so Tk is not available
[[1]]
x.hull y.hull
[1,] 2.200 45
[2,] 1.983 43
[3,] 1.783 46
[4,] 1.750 47
[5,] 1.600 52
[6,] 1.667 64
[7,] 3.500 87
[8,] 4.083 93
[9,] 5.100 96
[10,] 5.067 76
[[2]]
x.hull y.hull
[1,] 2.350 47
[2,] 2.150 46
[3,] 1.917 45
[4,] 1.867 45
[5,] 1.750 47
[6,] 1.700 59
[7,] 1.750 62
[8,] 2.383 71
[9,] 3.317 83
[10,] 4.133 91
[11,] 4.800 94
[12,] 5.000 88
[13,] 5.033 77
[14,] 4.700 73
[[3]]
x.hull y.hull
[1,] 3.833 64
[2,] 2.417 50
[3,] 2.167 48
[4,] 1.833 46
[5,] 1.817 46
[6,] 1.750 48
[7,] 1.733 54
[8,] 1.750 58
[9,] 1.833 63
[10,] 2.133 67
[11,] 3.600 85
[12,] 3.967 89
[13,] 4.400 92
[14,] 4.617 93
[15,] 4.900 89
[16,] 4.933 88
[17,] 4.933 86
[18,] 4.900 82
[19,] 4.800 75
[[4]]
x.hull y.hull
[1,] 2.883 55
[2,] 2.317 50
[3,] 1.833 46
[4,] 1.750 54
[5,] 1.817 60
[6,] 2.067 65
[7,] 3.600 83
[8,] 4.333 90
[9,] 4.783 90
[10,] 4.933 86
[11,] 4.817 77
[12,] 4.800 76
[13,] 4.750 75
[[5]]
x.hull y.hull
[1,] 2.617 53
[2,] 1.867 47
[3,] 1.783 52
[4,] 1.817 59
[5,] 1.983 62
[6,] 3.600 83
[7,] 4.150 88
[8,] 4.417 90
[9,] 4.450 90
[10,] 4.716 90
[11,] 4.850 86
[12,] 4.883 83
[13,] 4.817 78
[14,] 4.733 75
[[6]]
x.hull y.hull
[1,] 2.217 50
[2,] 2.100 49
[3,] 1.867 48
[4,] 1.800 51
[5,] 1.800 54
[6,] 1.833 59
[7,] 4.000 86
[8,] 4.333 89
[9,] 4.650 90
[10,] 4.800 84
[11,] 4.833 80
[12,] 4.767 78
[13,] 4.533 73
[[7]]
x.hull y.hull
[1,] 2.800 56
[2,] 2.250 51
[3,] 2.017 49
[4,] 1.867 49
[5,] 1.800 53
[6,] 1.833 57
[7,] 1.850 58
[8,] 3.767 83
[9,] 3.850 84
[10,] 4.367 88
[11,] 4.700 88
[12,] 4.800 82
[13,] 4.833 80
[14,] 4.500 73
[[8]]
x.hull y.hull
[1,] 2.400 53
[2,] 1.933 49
[3,] 1.917 49
[4,] 1.867 50
[5,] 1.800 53
[6,] 1.883 58
[7,] 2.017 60
[8,] 2.400 65
[9,] 3.917 84
[10,] 4.000 85
[11,] 4.150 86
[12,] 4.600 88
[13,] 4.800 81
[14,] 4.700 78
[15,] 4.500 73
[[9]]
x.hull y.hull
[1,] 2.417 54
[2,] 2.167 52
[3,] 2.033 51
[4,] 1.867 50
[5,] 1.833 54
[6,] 1.983 59
[7,] 2.333 64
[8,] 3.683 81
[9,] 4.183 86
[10,] 4.417 87
[11,] 4.700 84
[12,] 4.700 80
[13,] 4.667 78
[14,] 4.533 74
[15,] 4.067 69
[[10]]
x.hull y.hull
[1,] 4.300 72
[2,] 4.100 70
[3,] 1.950 51
[4,] 1.867 51
[5,] 1.833 54
[6,] 2.000 59
[7,] 3.450 78
[8,] 3.833 82
[9,] 4.083 84
[10,] 4.350 85
[11,] 4.600 85
[12,] 4.667 84
[13,] 4.700 83
[14,] 4.583 76
[15,] 4.467 74
x.hull y.hull
[1,] 3.833 64
[2,] 2.417 50
[3,] 2.167 48
[4,] 1.833 46
[5,] 1.817 46
[6,] 1.750 48
[7,] 1.733 54
[8,] 1.750 58
[9,] 1.833 63
[10,] 2.133 67
[11,] 3.600 85
[12,] 3.967 89
[13,] 4.400 92
[14,] 4.617 93
[15,] 4.900 89
[16,] 4.933 88
[17,] 4.933 86
[18,] 4.900 82
[19,] 4.800 75
x.hull y.hull
[1,] 4.233 76
[2,] 4.133 75
[3,] 3.567 71
[4,] 3.750 75
[5,] 4.233 81
[6,] 4.350 82
[7,] 4.417 79
[[1]]
x.hull y.hull
[1,] 20 32
[2,] 15 20
[3,] 7 4
[4,] 4 2
[5,] 4 10
[6,] 14 80
[7,] 24 120
[8,] 25 85
[9,] 23 54
[[2]]
x.hull y.hull
[1,] 19 36
[2,] 12 14
[3,] 9 10
[4,] 7 22
[5,] 18 84
[6,] 24 93
[7,] 24 70
[[3]]
x.hull y.hull
[1,] 17 32
[2,] 15 26
[3,] 11 17
[4,] 8 16
[5,] 10 34
[6,] 14 60
[7,] 18 76
[8,] 24 92
[9,] 22 66
[10,] 20 48
[[1]]
x.hull y.hull
[1,] 20 32
[2,] 15 20
[3,] 7 4
[4,] 4 2
[5,] 4 10
[6,] 14 80
[7,] 24 120
[8,] 25 85
[9,] 23 54
[[2]]
x.hull y.hull
[1,] 19 36
[2,] 12 14
[3,] 9 10
[4,] 7 22
[5,] 18 84
[6,] 24 93
[7,] 24 70
[[3]]
x.hull y.hull
[1,] 17 32
[2,] 15 26
[3,] 11 17
[4,] 8 16
[5,] 10 34
[6,] 14 60
[7,] 18 76
[8,] 24 92
[9,] 22 66
[10,] 20 48
[[4]]
x.hull y.hull
[1,] 16 32
[2,] 12 20
[3,] 10 18
[4,] 10 26
[5,] 13 46
[6,] 15 54
[7,] 19 68
[8,] 20 64
[9,] 20 52
[10,] 19 46
[[5]]
x.hull y.hull
[1,] 14 26
[2,] 12 24
[3,] 11 28
[4,] 18 56
[5,] 20 56
[6,] 18 42
[[1]]
x.hull y.hull
[1,] 20 32
[2,] 15 20
[3,] 7 4
[4,] 4 2
[5,] 4 10
[6,] 14 80
[7,] 24 120
[8,] 25 85
[9,] 23 54
[[2]]
x.hull y.hull
[1,] 19 36
[2,] 12 14
[3,] 9 10
[4,] 7 22
[5,] 18 84
[6,] 24 93
[7,] 24 70
[[3]]
x.hull y.hull
[1,] 17 32
[2,] 15 26
[3,] 11 17
[4,] 8 16
[5,] 10 34
[6,] 14 60
[7,] 18 76
[8,] 24 92
[9,] 22 66
[10,] 20 48
[[4]]
x.hull y.hull
[1,] 16 32
[2,] 12 20
[3,] 10 18
[4,] 10 26
[5,] 13 46
[6,] 15 54
[7,] 19 68
[8,] 20 64
[9,] 20 52
[10,] 19 46
[[5]]
x.hull y.hull
[1,] 14 26
[2,] 12 24
[3,] 11 28
[4,] 18 56
[5,] 20 56
[6,] 18 42
[[6]]
x.hull y.hull
[1,] 13 26
[2,] 12 28
[3,] 13 34
[4,] 17 50
[5,] 17 40
[[1]]
x.hull y.hull
[1,] 20 32
[2,] 15 20
[3,] 7 4
[4,] 4 2
[5,] 4 10
[6,] 14 80
[7,] 24 120
[8,] 25 85
[9,] 23 54
[[2]]
x.hull y.hull
[1,] 19 36
[2,] 12 14
[3,] 9 10
[4,] 7 22
[5,] 18 84
[6,] 24 93
[7,] 24 70
[[3]]
x.hull y.hull
[1,] 17 32
[2,] 15 26
[3,] 11 17
[4,] 8 16
[5,] 10 34
[6,] 14 60
[7,] 18 76
[8,] 24 92
[9,] 22 66
[10,] 20 48
[[4]]
x.hull y.hull
[1,] 16 32
[2,] 12 20
[3,] 10 18
[4,] 10 26
[5,] 13 46
[6,] 15 54
[7,] 19 68
[8,] 20 64
[9,] 20 52
[10,] 19 46
[[5]]
x.hull y.hull
[1,] 14 26
[2,] 12 24
[3,] 11 28
[4,] 18 56
[5,] 20 56
[6,] 18 42
[[6]]
x.hull y.hull
[1,] 13 26
[2,] 12 28
[3,] 13 34
[4,] 17 50
[5,] 17 40
aplpack documentation built on Sept. 30, 2021, 5:08 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Note Author(s) References See Also Examples
Description
plothulls plots convex hulls of a bivariate data set.
Usage
1 2 |
Arguments
x |
two column matrix of the coordinates of points of x-values of a data set |
y |
if x is one dimensional then y contains the y-values of the data set |
fraction |
... of points that lies inside the hull to be plotted |
n.hull |
number of directions sequential hulls to be plotted |
main |
title for the graphics |
add |
if TRUE no new plot is initialized |
col.hull |
color(s) of the hull(s) |
lty.hull |
line type(s) of the hull(s) |
lwd.hull |
line width(s) of the hull(s) |
density |
density argument of polygon() that draws the hulls |
... |
further arguments used in the call of plot() or points() |
Details
The function plothulls computes hulls of a bivariate data set using the
function chull. After finding a hull the hull maybe plotted.
Then the data points of the hull will be removed and
the hull of the remaining points is computed.
The style of plotting a hull depends on the setting of
col.hull, lty.hull, lwd.hull and density.
density=NA has the effect that the regions of the hulls are filled by a color.
Using fraction you can plot a single hull.
n.hull defines the number of hull that should be drawn one after the other.
Value
The hull(s) are stored as a list of matrices with two columns, the innermost first and so on.
Note
Version of plothulls: 10/2013
Author(s)
Peter Wolf
References
Green, P.J. (1981): Peeling bivariate data. In: Interpreting Multivariate Data, V. Barnett (ed.), pp 3-19, Wiley. Porzio, Giovanni C., Ragozini, Giancarlo (2000): Peeling multvariate data sets: a new approach. Quanderni di Statistica, Vol. 2.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # 10 hulls computed from the faithful data and plotted
plothulls(faithful, n.hull=10, lty.hull=1)
# plotting additionally a hull with 90 percent of points within the hull
plot(faithful)
plothulls(faithful, fraction=.90, add=TRUE, col.hull="red", lwd.hull=3)
# hull with 10 percent of points within the hull
plothulls(faithful, fraction=.10, col.hull="red", lwd.hull=3)
# first 3 hulls of the cars data set
n <- 3
plothulls(cars, n.hull=n, col.hull=1:n, lty.hull=1:n)
# 5 hulls represented by colored regions
n <- 5
cols <- heat.colors(9)[3:(3+n-1)]
plothulls(cars, n.hull=n, col.hull=cols, lty.hull=1:n, density=NA, col=0)
points(cars, pch=17, cex=1)
# 6 hulls: regions colored and boundaries shown
n <- 6
cols <- rainbow(n)
plothulls(cars, n.hull=n, col.hull=cols, lty.hull=1:n, density=NA, col=0)
plothulls(cars, n.hull=n, add=TRUE, col.hull=1, lwd.hull=2, lty=1, col=0)
|
Example output
Loading required package: tcltk
Warning message:
no DISPLAY variable so Tk is not available
[[1]]
x.hull y.hull
[1,] 2.200 45
[2,] 1.983 43
[3,] 1.783 46
[4,] 1.750 47
[5,] 1.600 52
[6,] 1.667 64
[7,] 3.500 87
[8,] 4.083 93
[9,] 5.100 96
[10,] 5.067 76
[[2]]
x.hull y.hull
[1,] 2.350 47
[2,] 2.150 46
[3,] 1.917 45
[4,] 1.867 45
[5,] 1.750 47
[6,] 1.700 59
[7,] 1.750 62
[8,] 2.383 71
[9,] 3.317 83
[10,] 4.133 91
[11,] 4.800 94
[12,] 5.000 88
[13,] 5.033 77
[14,] 4.700 73
[[3]]
x.hull y.hull
[1,] 3.833 64
[2,] 2.417 50
[3,] 2.167 48
[4,] 1.833 46
[5,] 1.817 46
[6,] 1.750 48
[7,] 1.733 54
[8,] 1.750 58
[9,] 1.833 63
[10,] 2.133 67
[11,] 3.600 85
[12,] 3.967 89
[13,] 4.400 92
[14,] 4.617 93
[15,] 4.900 89
[16,] 4.933 88
[17,] 4.933 86
[18,] 4.900 82
[19,] 4.800 75
[[4]]
x.hull y.hull
[1,] 2.883 55
[2,] 2.317 50
[3,] 1.833 46
[4,] 1.750 54
[5,] 1.817 60
[6,] 2.067 65
[7,] 3.600 83
[8,] 4.333 90
[9,] 4.783 90
[10,] 4.933 86
[11,] 4.817 77
[12,] 4.800 76
[13,] 4.750 75
[[5]]
x.hull y.hull
[1,] 2.617 53
[2,] 1.867 47
[3,] 1.783 52
[4,] 1.817 59
[5,] 1.983 62
[6,] 3.600 83
[7,] 4.150 88
[8,] 4.417 90
[9,] 4.450 90
[10,] 4.716 90
[11,] 4.850 86
[12,] 4.883 83
[13,] 4.817 78
[14,] 4.733 75
[[6]]
x.hull y.hull
[1,] 2.217 50
[2,] 2.100 49
[3,] 1.867 48
[4,] 1.800 51
[5,] 1.800 54
[6,] 1.833 59
[7,] 4.000 86
[8,] 4.333 89
[9,] 4.650 90
[10,] 4.800 84
[11,] 4.833 80
[12,] 4.767 78
[13,] 4.533 73
[[7]]
x.hull y.hull
[1,] 2.800 56
[2,] 2.250 51
[3,] 2.017 49
[4,] 1.867 49
[5,] 1.800 53
[6,] 1.833 57
[7,] 1.850 58
[8,] 3.767 83
[9,] 3.850 84
[10,] 4.367 88
[11,] 4.700 88
[12,] 4.800 82
[13,] 4.833 80
[14,] 4.500 73
[[8]]
x.hull y.hull
[1,] 2.400 53
[2,] 1.933 49
[3,] 1.917 49
[4,] 1.867 50
[5,] 1.800 53
[6,] 1.883 58
[7,] 2.017 60
[8,] 2.400 65
[9,] 3.917 84
[10,] 4.000 85
[11,] 4.150 86
[12,] 4.600 88
[13,] 4.800 81
[14,] 4.700 78
[15,] 4.500 73
[[9]]
x.hull y.hull
[1,] 2.417 54
[2,] 2.167 52
[3,] 2.033 51
[4,] 1.867 50
[5,] 1.833 54
[6,] 1.983 59
[7,] 2.333 64
[8,] 3.683 81
[9,] 4.183 86
[10,] 4.417 87
[11,] 4.700 84
[12,] 4.700 80
[13,] 4.667 78
[14,] 4.533 74
[15,] 4.067 69
[[10]]
x.hull y.hull
[1,] 4.300 72
[2,] 4.100 70
[3,] 1.950 51
[4,] 1.867 51
[5,] 1.833 54
[6,] 2.000 59
[7,] 3.450 78
[8,] 3.833 82
[9,] 4.083 84
[10,] 4.350 85
[11,] 4.600 85
[12,] 4.667 84
[13,] 4.700 83
[14,] 4.583 76
[15,] 4.467 74
x.hull y.hull
[1,] 3.833 64
[2,] 2.417 50
[3,] 2.167 48
[4,] 1.833 46
[5,] 1.817 46
[6,] 1.750 48
[7,] 1.733 54
[8,] 1.750 58
[9,] 1.833 63
[10,] 2.133 67
[11,] 3.600 85
[12,] 3.967 89
[13,] 4.400 92
[14,] 4.617 93
[15,] 4.900 89
[16,] 4.933 88
[17,] 4.933 86
[18,] 4.900 82
[19,] 4.800 75
x.hull y.hull
[1,] 4.233 76
[2,] 4.133 75
[3,] 3.567 71
[4,] 3.750 75
[5,] 4.233 81
[6,] 4.350 82
[7,] 4.417 79
[[1]]
x.hull y.hull
[1,] 20 32
[2,] 15 20
[3,] 7 4
[4,] 4 2
[5,] 4 10
[6,] 14 80
[7,] 24 120
[8,] 25 85
[9,] 23 54
[[2]]
x.hull y.hull
[1,] 19 36
[2,] 12 14
[3,] 9 10
[4,] 7 22
[5,] 18 84
[6,] 24 93
[7,] 24 70
[[3]]
x.hull y.hull
[1,] 17 32
[2,] 15 26
[3,] 11 17
[4,] 8 16
[5,] 10 34
[6,] 14 60
[7,] 18 76
[8,] 24 92
[9,] 22 66
[10,] 20 48
[[1]]
x.hull y.hull
[1,] 20 32
[2,] 15 20
[3,] 7 4
[4,] 4 2
[5,] 4 10
[6,] 14 80
[7,] 24 120
[8,] 25 85
[9,] 23 54
[[2]]
x.hull y.hull
[1,] 19 36
[2,] 12 14
[3,] 9 10
[4,] 7 22
[5,] 18 84
[6,] 24 93
[7,] 24 70
[[3]]
x.hull y.hull
[1,] 17 32
[2,] 15 26
[3,] 11 17
[4,] 8 16
[5,] 10 34
[6,] 14 60
[7,] 18 76
[8,] 24 92
[9,] 22 66
[10,] 20 48
[[4]]
x.hull y.hull
[1,] 16 32
[2,] 12 20
[3,] 10 18
[4,] 10 26
[5,] 13 46
[6,] 15 54
[7,] 19 68
[8,] 20 64
[9,] 20 52
[10,] 19 46
[[5]]
x.hull y.hull
[1,] 14 26
[2,] 12 24
[3,] 11 28
[4,] 18 56
[5,] 20 56
[6,] 18 42
[[1]]
x.hull y.hull
[1,] 20 32
[2,] 15 20
[3,] 7 4
[4,] 4 2
[5,] 4 10
[6,] 14 80
[7,] 24 120
[8,] 25 85
[9,] 23 54
[[2]]
x.hull y.hull
[1,] 19 36
[2,] 12 14
[3,] 9 10
[4,] 7 22
[5,] 18 84
[6,] 24 93
[7,] 24 70
[[3]]
x.hull y.hull
[1,] 17 32
[2,] 15 26
[3,] 11 17
[4,] 8 16
[5,] 10 34
[6,] 14 60
[7,] 18 76
[8,] 24 92
[9,] 22 66
[10,] 20 48
[[4]]
x.hull y.hull
[1,] 16 32
[2,] 12 20
[3,] 10 18
[4,] 10 26
[5,] 13 46
[6,] 15 54
[7,] 19 68
[8,] 20 64
[9,] 20 52
[10,] 19 46
[[5]]
x.hull y.hull
[1,] 14 26
[2,] 12 24
[3,] 11 28
[4,] 18 56
[5,] 20 56
[6,] 18 42
[[6]]
x.hull y.hull
[1,] 13 26
[2,] 12 28
[3,] 13 34
[4,] 17 50
[5,] 17 40
[[1]]
x.hull y.hull
[1,] 20 32
[2,] 15 20
[3,] 7 4
[4,] 4 2
[5,] 4 10
[6,] 14 80
[7,] 24 120
[8,] 25 85
[9,] 23 54
[[2]]
x.hull y.hull
[1,] 19 36
[2,] 12 14
[3,] 9 10
[4,] 7 22
[5,] 18 84
[6,] 24 93
[7,] 24 70
[[3]]
x.hull y.hull
[1,] 17 32
[2,] 15 26
[3,] 11 17
[4,] 8 16
[5,] 10 34
[6,] 14 60
[7,] 18 76
[8,] 24 92
[9,] 22 66
[10,] 20 48
[[4]]
x.hull y.hull
[1,] 16 32
[2,] 12 20
[3,] 10 18
[4,] 10 26
[5,] 13 46
[6,] 15 54
[7,] 19 68
[8,] 20 64
[9,] 20 52
[10,] 19 46
[[5]]
x.hull y.hull
[1,] 14 26
[2,] 12 24
[3,] 11 28
[4,] 18 56
[5,] 20 56
[6,] 18 42
[[6]]
x.hull y.hull
[1,] 13 26
[2,] 12 28
[3,] 13 34
[4,] 17 50
[5,] 17 40
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.