This package will order panels in scatterplot matrices and parallel coordinate displays by some merit index. The package contains various indices of merit, ordering functions, and enhanced versions of pairs and parcoord which color panels according to their merit level. For details on the methods used, consult “Clustering Visualisations of Multidimensional Data”, Journal of Computational and Graphical Statistics, vol. 13, (4), pp 788-806, 2004.
Displaying a correlation matrix
library(gclus)
#> Loading required package: cluster
data(longley)
longley.cor <- cor(longley)
longley.color <- dmat.color(longley.cor)
dmat.color assigns three colours to the correlations according to the correlation magnitude. High correlations are in pink, the middle third are in blue, and the botom third are in yellow.
par(mar=c(1,1,1,1))
plotcolors(longley.color,dlabels=rownames(longley.color))
If you want to change the colour scheme:
longley.color <- dmat.color(longley.cor, byrank=FALSE)
longley.color <- dmat.color(longley.cor, breaks=c(-1,0,.5,.8,1),
cm.colors(4))
The plot is easier to interpret if variables are reorded prior to plotting.
par(mar=c(1,1,1,1))
longley.o <- order.hclust(longley.cor)
longley.color1 <- longley.color[longley.o,longley.o]
plotcolors(longley.color1,dlabels=rownames(longley.color1))
Displaying a pairs plot with coloured panels
cpairs is a version of pairs All the high-correlation panels appear together in a block.
par(mar=c(1,1,1,1))
cpairs(longley, order= longley.o,panel.color= longley.color)
If the order is not supplied, then the variables are plotted in default dataset order.
Displaying a PCP plot with coloured panels
cparcoord is a versions of `parcoord where panels can be coloured. Again, the pink panels have high correlation, blue panels have middling correlation, and yellow panels have low correlation.
cparcoord(longley, order= longley.o,panel.color= longley.color,
horizontal=TRUE, mar=c(2,4,1,1))
Plotting re-ordered dendrograms.
eurodist is a built-in distance matrix giving the distance between European cities.
par(mar=c(1,1,1,1))
data(eurodist)
dis <- as.dist(eurodist)
hc <- hclust(dis, "ave")
plot(hc)
order.hclust re-orders a dendrogram to improve the similarity between nearby leaves. Applying it to the hc object:
par(mar=c(1,1,1,1))
hc1 <- reorder.hclust(hc, dis)
plot(hc1)
Both dendrograms correspond to the same tree structure, but the second one shows that Paris is closer to Cherbourg than Munich, and Rome is closer to Gibralter than to Barcelona.
We can also compare both orderings with an image plot of the colors. The second ordering seems to place nearby cities closer to each other.
layout(matrix(1:2,nrow=1,ncol=2))
par(mar=c(1,6,1,1))
cmat <- dmat.color(eurodist, rev(cm.colors(5)))
plotcolors(cmat[hc$order,hc$order], rlabels=labels(eurodist)[hc$order])
plotcolors(cmat[hc1$order,hc1$order], rlabels=labels(eurodist)[hc1$order])
gclus/inst/doc/gclus.Rmd 0000644 0001762 0000144 00000007262 13414704627 014711 0 ustar ligges users ---
title: "Clustering Graphics"
author: "Catherine Hurley"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Clustering Graphics}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
This package will order panels in scatterplot matrices and parallel coordinate
displays by some merit index. The package contains various indices of merit,
ordering functions, and enhanced versions of pairs and parcoord which
color panels according to their merit level.
For details on the methods used, consult "Clustering Visualisations of Multidimensional
Data", Journal of Computational and Graphical Statistics,
vol. 13, (4), pp 788-806, 2004.
## Displaying a correlation matrix
```{r}
library(gclus)
data(longley)
longley.cor <- cor(longley)
longley.color <- dmat.color(longley.cor)
```
`dmat.color` assigns three colours to the correlations according to the correlation
magnitude. High correlations are in pink, the middle third are in blue, and the
botom third are in yellow.
```{r fig.width=5, fig.height=5, fig.align='center'}
par(mar=c(1,1,1,1))
plotcolors(longley.color,dlabels=rownames(longley.color))
```
If you want to change the colour scheme:
```{r eval=F}
longley.color <- dmat.color(longley.cor, byrank=FALSE)
longley.color <- dmat.color(longley.cor, breaks=c(-1,0,.5,.8,1),
cm.colors(4))
```
The plot is easier to interpret if variables are reorded prior to plotting.
```{r fig.width=5, fig.height=5, fig.align='center'}
par(mar=c(1,1,1,1))
longley.o <- order.hclust(longley.cor)
longley.color1 <- longley.color[longley.o,longley.o]
plotcolors(longley.color1,dlabels=rownames(longley.color1))
```
## Displaying a pairs plot with coloured panels
`cpairs` is a version of `pairs` All the high-correlation panels appear
together in a block.
```{r fig.width=5, fig.height=5, fig.align='center'}
par(mar=c(1,1,1,1))
cpairs(longley, order= longley.o,panel.color= longley.color)
```
If the `order` is not supplied, then the variables are plotted in default dataset order.
## Displaying a PCP plot with coloured panels
`cparcoord` is a versions of ` `parcoord`
where panels can be coloured. Again, the pink panels have high correlation,
blue panels have middling correlation, and yellow panels have low correlation.
```{r fig.width=8, fig.height=3, fig.align='center', out.width="100%"}
cparcoord(longley, order= longley.o,panel.color= longley.color,
horizontal=TRUE, mar=c(2,4,1,1))
```
## Plotting re-ordered dendrograms.
`eurodist` is a built-in distance matrix giving the distance between European cities.
```{r fig.width=6, fig.height=4, fig.align='center'}
par(mar=c(1,1,1,1))
data(eurodist)
dis <- as.dist(eurodist)
hc <- hclust(dis, "ave")
plot(hc)
```
`order.hclust` re-orders a dendrogram to improve the similarity between
nearby leaves.
Applying it to the `hc` object:
```{r fig.width=6, fig.height=4, fig.align='center'}
par(mar=c(1,1,1,1))
hc1 <- reorder.hclust(hc, dis)
plot(hc1)
```
Both dendrograms correspond to the same tree structure,
but the second one shows that
Paris is closer to Cherbourg than Munich, and
Rome is closer to Gibralter than to Barcelona.
We can also compare both orderings with an
image plot of the colors.
The second ordering seems to place nearby cities
closer to each other.
```{r fig.width=8, fig.height=3.5, fig.align='center'}
layout(matrix(1:2,nrow=1,ncol=2))
par(mar=c(1,6,1,1))
cmat <- dmat.color(eurodist, rev(cm.colors(5)))
plotcolors(cmat[hc$order,hc$order], rlabels=labels(eurodist)[hc$order])
plotcolors(cmat[hc1$order,hc1$order], rlabels=labels(eurodist)[hc1$order])
```
gclus/NAMESPACE 0000755 0001762 0000144 00000000423 13414672620 012615 0 ustar ligges users # Default NAMESPACE created by R
# Remove the previous line if you edit this file
# Export all names
exportPattern(".")
# Import all packages listed as Imports or Depends
import(
cluster,compiler
)
S3method(reorder,hclust)
import(graphics)
import(stats)
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