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small fixes.
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mhahsler committed Jul 19, 2023
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39 changes: 21 additions & 18 deletions README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -38,35 +38,37 @@ pkg_install(pkg)
The used example dataset contains the joint probability of disagreement
between Supreme Court Judges from 1995 to 2002. The goal
is to reveal structural information in this data.
We load the library, read the data, convert the data to distances,
We load the library, read the data, convert the data to a distance matrix,
and then use the default seriation method to reorder the objects.
```{r}
library(seriation)
data("SupremeCourt")
SupremeCourt
d <- as.dist(SupremeCourt)
d
order <- seriate(d)
order
```

Get the resulting permutation vector and visualize the permuted distance matrix.
Darker squares around the main diagonal show groups of similar objects.
After seriation, two groups are visible.
Here is the resulting permutation vector.
```{r}
get_order(order)
```

```{r seriation, fig.show="hold", out.width="50%"}
pimage(d, main = "judges in original alphabetical order")
pimage(d, order, main = "judges reordered by seriation")
```
Next, we visualize the original and permuted distance matrix.

```{r seriation, fig.show="hold", out.width="50%"}
pimage(d, main = "Judges (original alphabetical order)")
pimage(d, order, main = "Judges (reordered by seriation)")
```

Darker squares around the main diagonal indicate groups of similar objects.
After seriation, two groups are visible.

Compare the available seriation criteria. Seriation improved all (Note: some
measures are merit measures while others represent cost. See the manual page for details).
We can compare the available seriation criteria. Seriation improves all measures.
Note that some measures are merit measures while others represent cost.
See the manual page for details.
```{r}
rbind(
alphabetical = criterion(d),
Expand All @@ -75,13 +77,15 @@ rbind(
```

Some seriation methods also return a linear configuration where more similar objects are
located closer to each other. We can see a clear divide between the two groups.
```{r configuration, fig.show="hold", fig.height = 3}
located closer to each other.
```{r configuration, fig.align="center", fig.height = 3}
get_config(order)
plot_config(order)
```

We can see a clear divide between the two groups in the configuration.

## Available seriation methods to reorder dissimilarity data

Seriation methods for dissimilarity data reorder the set of objects in the data.
Expand Down Expand Up @@ -189,8 +193,7 @@ DOI: 10.18637/jss.v025.i03
* Michael Hahsler. [An experimental comparison of seriation methods for one-mode two-way data.](http://dx.doi.org/10.1016/j.ejor.2016.08.066) _European Journal of Operational Research,_ 257:133-143, 2017.
DOI: 10.1016/j.ejor.2016.08.066
(read the [preprint](https://michael.hahsler.net/research/paper/EJOR_seriation_2016.pdf))
* Hahsler, M. and Hornik, K. (2011): Dissimilarity plots: A visual exploration tool for partitional clustering. _Journal of Computational and Graphical Statistics,_ **10**(2):335–354.
[doi:10.1198/jcgs.2010.09139](http://dx.doi.org/10.1198/jcgs.2010.09139) (read the
[preprint](http://michael.hahsler.net/research/paper/dissplot_JCGS2011_preprint.pdf); [code examples](https://mhahsler.github.io/seriation/seriation_cluster_evaluation.html))
* [Reference manual for package seriation.](https://www.rdocumentation.org/packages/seriation/)
* Hahsler, M. and Hornik, K. (2011): [Dissimilarity plots: A visual exploration tool for partitional clustering.](http://dx.doi.org/10.1198/jcgs.2010.09139) _Journal of Computational and Graphical Statistics,_ **10**(2):335–354. doi:10.1198/jcgs.2010.09139 (read the
[preprint](https://michael.hahsler.net/research/paper/dissplot_JCGS2011_preprint.pdf); [code examples](https://mhahsler.github.io/seriation/seriation_cluster_evaluation.html))
* [Reference manual for package seriation.](https://mhahsler.r-universe.dev/seriation/doc/manual.html#seriation-package)

75 changes: 34 additions & 41 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -103,39 +103,28 @@ install.packages("seriation", repos = "https://mhahsler.r-universe.dev")
The used example dataset contains the joint probability of disagreement
between Supreme Court Judges from 1995 to 2002. The goal is to reveal
structural information in this data. We load the library, read the data,
convert the data to distances, and then use the default seriation method
to reorder the objects.
convert the data to a distance matrix, and then use the default
seriation method to reorder the objects.

``` r
library(seriation)
data("SupremeCourt")

SupremeCourt
d <- as.dist(SupremeCourt)
d
```

## Breyer Ginsburg Kennedy OConnor Rehnquist Scalia Souter Stevens
## Breyer 0.00 0.120 0.25 0.21 0.30 0.353 0.118 0.16
## Ginsburg 0.12 0.000 0.27 0.25 0.31 0.370 0.096 0.15
## Kennedy 0.25 0.267 0.00 0.16 0.12 0.188 0.248 0.33
## OConnor 0.21 0.252 0.16 0.00 0.16 0.207 0.220 0.33
## Rehnquist 0.30 0.308 0.12 0.16 0.00 0.143 0.293 0.40
## Scalia 0.35 0.370 0.19 0.21 0.14 0.000 0.338 0.44
## Souter 0.12 0.096 0.25 0.22 0.29 0.338 0.000 0.17
## Stevens 0.16 0.145 0.33 0.33 0.40 0.438 0.169 0.00
## Thomas 0.36 0.368 0.18 0.21 0.14 0.066 0.331 0.44
## Thomas
## Breyer 0.359
## Ginsburg 0.368
## Kennedy 0.177
## OConnor 0.205
## Rehnquist 0.137
## Scalia 0.066
## Souter 0.331
## Stevens 0.436
## Thomas 0.000
## Ginsburg 0.120
## Kennedy 0.250 0.267
## OConnor 0.209 0.252 0.156
## Rehnquist 0.299 0.308 0.122 0.162
## Scalia 0.353 0.370 0.188 0.207 0.143
## Souter 0.118 0.096 0.248 0.220 0.293 0.338
## Stevens 0.162 0.145 0.327 0.329 0.402 0.438 0.169
## Thomas 0.359 0.368 0.177 0.205 0.137 0.066 0.331 0.436

``` r
d <- as.dist(SupremeCourt)
order <- seriate(d)
order
```
Expand All @@ -146,9 +135,7 @@ order
## vector length seriation method
## 1 9 Spectral

Get the resulting permutation vector and visualize the permuted distance
matrix. Darker squares around the main diagonal show groups of similar
objects. After seriation, two groups are visible.
Here is the resulting permutation vector.

``` r
get_order(order)
Expand All @@ -159,16 +146,21 @@ get_order(order)
## Stevens
## 8

Next, we visualize the original and permuted distance matrix.

``` r
pimage(d, main = "judges in original alphabetical order")
pimage(d, order, main = "judges reordered by seriation")
pimage(d, main = "Judges (original alphabetical order)")
pimage(d, order, main = "Judges (reordered by seriation)")
```

<img src="inst/README_files/seriation-1.png" width="50%" /><img src="inst/README_files/seriation-2.png" width="50%" />

Compare the available seriation criteria. Seriation improved all (Note:
some measures are merit measures while others represent cost. See the
manual page for details).
Darker squares around the main diagonal indicate groups of similar
objects. After seriation, two groups are visible.

We can compare the available seriation criteria. Seriation improves all
measures. Note that some measures are merit measures while others
represent cost. See the manual page for details.

``` r
rbind(alphabetical = criterion(d), seriated = criterion(d, order))
Expand All @@ -185,8 +177,7 @@ rbind(alphabetical = criterion(d), seriated = criterion(d, order))
## seriated 2.5 1.3 1.1 0.03 0.913

Some seriation methods also return a linear configuration where more
similar objects are located closer to each other. We can see a clear
divide between the two groups.
similar objects are located closer to each other.

``` r
get_config(order)
Expand All @@ -201,7 +192,9 @@ get_config(order)
plot_config(order)
```

![](inst/README_files/configuration-1.png)<!-- -->
<img src="inst/README_files/configuration-1.png" style="display: block; margin: auto;" />

We can see a clear divide between the two groups in the configuration.

## Available seriation methods to reorder dissimilarity data

Expand Down Expand Up @@ -338,13 +331,13 @@ to reorder objects (rows). The same approach can be applied to columns.
*European Journal of Operational Research,* 257:133-143, 2017. DOI:
10.1016/j.ejor.2016.08.066 (read the
[preprint](https://michael.hahsler.net/research/paper/EJOR_seriation_2016.pdf))
- Hahsler, M. and Hornik, K. (2011): Dissimilarity plots: A visual
exploration tool for partitional clustering. *Journal of Computational
and Graphical Statistics,* **10**(2):335–354.
[doi:10.1198/jcgs.2010.09139](http://dx.doi.org/10.1198/jcgs.2010.09139)
(read the
[preprint](http://michael.hahsler.net/research/paper/dissplot_JCGS2011_preprint.pdf);
- Hahsler, M. and Hornik, K. (2011): [Dissimilarity plots: A visual
exploration tool for partitional
clustering.](http://dx.doi.org/10.1198/jcgs.2010.09139) *Journal of
Computational and Graphical Statistics,* **10**(2):335–354.
<doi:10.1198/jcgs.2010.09139> (read the
[preprint](https://michael.hahsler.net/research/paper/dissplot_JCGS2011_preprint.pdf);
[code
examples](https://mhahsler.github.io/seriation/seriation_cluster_evaluation.html))
- [Reference manual for package
seriation.](https://www.rdocumentation.org/packages/seriation/)
seriation.](https://mhahsler.r-universe.dev/seriation/doc/manual.html#seriation-package)

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