Package 'seriation'

Plot a Bertin Matrix

Description

Plot a data matrix of cases and variables. Each value is represented by a symbol. Large values are highlighted. Note that Bertin arranges the cases horizontally and the variables as rows. The matrix can be rearranged using seriation techniques to make structure in the data visible (see Falguerolles et al 1997).

Usage

bertinplot(
  x,
  order = NULL,
  panel.function = panel.bars,
  highlight = TRUE,
  row_labels = TRUE,
  col_labels = TRUE,
  flip_axes = TRUE,
  ...
)

panel.bars(value, spacing, hl)

panel.circles(value, spacing, hl)

panel.rectangles(value, spacing, hl)

panel.squares(value, spacing, hl)

panel.tiles(value, spacing, hl)

panel.blocks(value, spacing, hl)

panel.lines(value, spacing, hl)

bertin_cut_line(x = NULL, y = NULL, col = "red")

ggbertinplot(
  x,
  order = NULL,
  geom = "bar",
  highlight = TRUE,
  row_labels = TRUE,
  col_labels = TRUE,
  flip_axes = TRUE,
  prop = FALSE,
  ...
)

Arguments

x	a data matrix. Note that following Bertin, columns are variables and rows are cases. This behavior can be reversed using reverse = TRUE in options.
order	an object of class ser_permutation to rearrange x before plotting. If NULL, no rearrangement is performed.
panel.function	a function to produce the symbols. Currently available functions are panel.bars (default), panel.circles, panel.rectangles, panel.tiles and panel.lines. For circles and squares neg. values are represented by a dashed border. For blocks all blocks are the same size (can be used with shading = TRUE).
highlight	a logical scalar indicating whether to use highlighting. If TRUE, all variables with values greater than the variable-wise mean are highlighted. To control highlighting, also a logical matrix or a matrix with colors with the same dimensions as x can be supplied.
row_labels, col_labels	a logical indicating if row and column labels in x should be displayed. If NULL then labels are displayed if the x contains the appropriate dimname and the number of labels is 25 or less. A character vector of the appropriate length with labels can also be supplied.
flip_axes	logical indicating whether to swap cases and variables in the plot. The default (TRUE) is to plot cases as columns and variables as rows.
...	ggbertinplot(): further parameters are passed on to ggpimage(). bertinplot(): further parameters can include: ⁠xlab, ylab⁠ labels (default: use labels from x). spacing relative space between symbols (default: 0.2). shading use gray shades to encode value instead of highlighting (default: FALSE). shading.function a function that accepts a single argument in range $[.1, .8]$ and returns a valid corresponding color (e.g., using rgb()). frame plot a grid to separate symbols (default: FALSE). mar margins (see par()). gp_labels gpar object for labels (see gpar()) gp_panels gpar object for panels (see gpar()). newpage a logical indicating whether to start the plot on a new page (see grid.newpage()). pop a logical indicating whether to pop the created viewports (see pop.viewport())?
value, spacing, hl	are used internally for the panel functions.
col, y	and x in bertin_cut_line() are for adding a line to a bertinplot() (not ggplot2-based).
geom	visualization type. Available ggplot2 geometries are: "tile", "rectangle", "circle", "line", "bar", "none".
prop	logical; change the aspect ratio so cells in the image have a equal width and height.

Details

The plot is organized as a matrix of symbols. The symbols are drawn by a panel function, where all symbols of a row are drawn by one call of the function (using vectorization). The interface for the panel function is panel.myfunction(value, spacing, hl). value is the vector of values for a row scaled between 0 and 1, spacing contains the relative space between symbols and hl is a logical vector indicating which symbol should be highlighted.

Cut lines can be added to an existing Bertin plot using bertin_cut_line(x = NULL, y = NULL). x/y is can be a number indicating where to draw the cut line between two columns/rows. If both x and y is specified then one can select a row/column and the other can select a range to draw a line which does only span a part of the row/column. It is important to call bertinplot() with the option pop = FALSE.

ggbertinplot() calls ggpimage() and all additional parameters are passed on.

Value

Nothing.

Author(s)

Michael Hahsler

References

de Falguerolles, A., Friedrich, F., Sawitzki, G. (1997): A Tribute to J. Bertin's Graphical Data Analysis. In: Proceedings of the SoftStat '97 (Advances in Statistical Software 6), 11–20.

See Also

Other plots: VAT(), dissplot(), hmap(), palette(), pimage()

Examples

data("Irish")
scale_by_rank <- function(x) apply(x, 2, rank)
x <- scale_by_rank(Irish[,-6])

# Use the the sum of absolute rank differences
order <- c(
  seriate(dist(x, "minkowski", p = 1)),
  seriate(dist(t(x), "minkowski", p = 1))
)

# Plot
bertinplot(x, order)

# Some alternative displays
bertinplot(x, order, panel = panel.tiles, shading_col = bluered(100), highlight = FALSE)
bertinplot(x, order, panel = panel.circles, spacing = -.2)
bertinplot(x, order, panel = panel.rectangles)
bertinplot(x, order, panel = panel.lines)

# Plot with cut lines (we manually set the order here)
order <- ser_permutation(c(6L, 9L, 29L, 10L, 32L, 22L, 2L, 35L,
  24L, 30L, 33L, 25L, 37L, 36L, 8L, 27L, 4L, 39L, 3L, 40L, 38L,
  1L, 31L, 34L, 28L, 23L, 5L, 11L, 7L, 41L, 13L, 26L, 17L, 15L,
  12L, 20L, 14L, 18L, 19L, 16L, 21L),
    c(4L, 2L, 1L, 6L, 7L, 8L, 5L, 3L))

bertinplot(x, order, pop=FALSE)
bertin_cut_line(, 4) ## horizontal line between rows 4 and 5
bertin_cut_line(, 7) ## separate "Right to Life" from the rest
bertin_cut_line(18, c(0, 4)) ## separate a block of large values (vertically)

# ggplot2-based plots
if (require("ggplot2")) {
  library(ggplot2)

  # Default plot uses bars and highlighting values larger than the mean
  ggbertinplot(x, order)

  # highlight values in the 4th quartile
  ggbertinplot(x, order, highlight = quantile(x, probs = .75))

  # Use different geoms. "none" lets the user specify their own geom.
  # Variables set are row, col and x (for the value).

  ggbertinplot(x, order, geom = "tile", prop = TRUE)
  ggbertinplot(x, order, geom = "rectangle")
  ggbertinplot(x, order, geom = "rectangle", prop = TRUE)
  ggbertinplot(x, order, geom = "circle")
  ggbertinplot(x, order, geom = "line")

  # Tiles with diverging color scale
  ggbertinplot(x, order, geom = "tile", prop = TRUE) +
    scale_fill_gradient2(midpoint = mean(x))

  # Custom geom (geom = "none"). Defined variables are row, col, and x for the value
  ggbertinplot(x, order, geom = "none", prop = FALSE) +
    geom_point(aes(x = col, y = row, size = x, color = x > 30), pch = 15) +
    scale_size(range = c(1, 10))

  # Use a ggplot2 theme with theme_set()
  old_theme <- theme_set(theme_minimal() +
      theme(panel.grid = element_blank())
    )
  ggbertinplot(x, order, geom = "bar")
  theme_set(old_theme)
}

---

2D Data Sets used for the CHAMELEON Clustering Algorithm

Description

Several 2D data sets created to evaluate the CHAMELEON clustering algorithm in the paper by Karypis et al (1999).

Format

chameleon_ds4: The format is a 8,000 x 2 data.frame.

chameleon_ds5: The format is a 8,000 x 2 data.frame.

chameleon_ds7: The format is a 10,000 x 2 data.frame.

chameleon_ds8: The format is a 8,000 x 2 data.frame.

References

Karypis, G., EH. Han, V. Kumar (1999): CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling, IEEE Computer, 32(8): 68–75. doi:10.1109/2.781637

See Also

Other data: Irish, Munsingen, SupremeCourt, Townships, Wood, Zoo, create_lines_data(), is.robinson()

Examples

data(Chameleon)

plot(chameleon_ds4, cex = .1)
plot(chameleon_ds5, cex = .1)
plot(chameleon_ds7, cex = .1)
plot(chameleon_ds8, cex = .1)

---

Create Simulated Data for Seriation Evaluation

Description

Several functions to create simulated data to evaluate different aspects of seriation algorithms and criterion functions.

Usage

create_lines_data(n = 250)

create_ordered_data(
  n = 250,
  k = 2,
  size = NULL,
  spacing = 6,
  path = "linear",
  sd1 = 1,
  sd2 = 0
)

Arguments

n	number of data points to create.
k	number of Gaussian components.
size	relative size (number of points) of components (length of k). If NULL then all components have the same size.
spacing	space between the centers of components. The default of 6 means that the components will barely touch at ds1 = 1 (3 standard deviations for each Gaussian component).
path	Are the components arranged along a "linear" or "circular" path?
sd1	variation in the direction along the components. A value greater than one means the components are mixing.
sd2	variation perpendicular to the direction along the components. A value greater than 0 will introduce anti-Robinson violation events.

Details

create_lines_data() recreates the lines data set used in for iVAT() in Havens and Bezdeck (2012).

create_ordered_data() (Hahsler et al, 2021) is a versatile function which creates "orderable" 2D data using Gaussian components along a linear or circular path. The components are equally spaced (spacing) along the path. The default spacing of 6 ensures that 2 adjacent components with a standard deviation of one along the direction of the path will barely touch. The standard deviation along the path is set by sd1. The standard deviation perpendicular to the path is set by sd2. A value larger than zero will result in the data not being perfectly orderable (i.e., the resulting distance matrix will not be a perfect pre-anti-Robinson matrix and contain anti-Robinson violation events after seriation). Note that a circular path always creates anti-Robinson violation since the circle has to be broken at some point to create a linear order.

Value

a data.frame with the created data.

Author(s)

Michael Hahsler

References

Havens, T.C. and Bezdek, J.C. (2012): An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm, IEEE Transactions on Knowledge and Data Engineering, 24(5), 813–822.

Michael Hahsler, Christian Buchta and Kurt Hornik (2021). seriation: Infrastructure for Ordering Objects Using Seriation. R package version 1.3.2. https://github.com/mhahsler/seriation

See Also

seriate(), criterion(), iVAT().

Other data: Chameleon, Irish, Munsingen, SupremeCourt, Townships, Wood, Zoo, is.robinson()

Examples

## lines data set from Havens and Bezdek (2011)
x <- create_lines_data(100)
plot(x, xlim = c(-5, 5), ylim = c(-3, 3), cex = .2, col = attr(x, "id"))
d <- dist(x)
pimage(d, seriate(d, "OLO_single"), col = bluered(100, bias = .5), key = TRUE)

## create_ordered_data can produce many types of "orderable" data

## perfect pre-Anti-Robinson matrix (with a single components)
x <- create_ordered_data(100, k = 1)
plot(x, cex = .2, col = attr(x, "id"))
d <- dist(x)
pimage(d, seriate(d, "MDS"), col = bluered(100, bias=.5), key = TRUE)

## separated components
x <- create_ordered_data(100, k = 5)
plot(x, cex =.2, col = attr(x, "id"))
d <- dist(x)
pimage(d, seriate(d, "MDS"), col = bluered(100, bias = .5), key = TRUE)

## overlapping components
x <- create_ordered_data(100, k = 5, sd1 = 2)
plot(x, cex = .2, col = attr(x, "id"))
d <- dist(x)
pimage(d, seriate(d, "MDS"), col = bluered(100, bias = .5), key = TRUE)

## introduce anti-Robinson violations (a non-zero y value)
x <- create_ordered_data(100, k = 5, sd1 = 2, sd2 = 5)
plot(x, cex = .2, col = attr(x, "id"))
d <- dist(x)
pimage(d, seriate(d, "MDS"), col = bluered(100, bias = .5), key = TRUE)

## circular path (has always violations)
x <- create_ordered_data(100, k = 5, path = "circular", sd1 = 2)
plot(x, cex = .2, col = attr(x, "id"))
d <- dist(x)
pimage(d, seriate(d, "OLO"), col = bluered(100, bias = .5), key = TRUE)

## circular path (with more violations violations)
x <- create_ordered_data(100, k = 5, path = "circular", sd1 = 2, sd2 = 1)
plot(x, cex=.2, col = attr(x, "id"))
d <- dist(x)
pimage(d, seriate(d, "OLO"), col = bluered(100, bias = .5), key = TRUE)

---

Criterion for a Loss/Merit Function for Data Given a Permutation

Description

Compute the value for different loss functions $L$ and merit function $M$ for data given a permutation.

Usage

criterion(x, order = NULL, method = NULL, force_loss = FALSE, ...)

## S3 method for class 'array'
criterion(x, order = NULL, method = NULL, force_loss = FALSE, ...)

## S3 method for class 'dist'
criterion(x, order = NULL, method = NULL, force_loss = FALSE, ...)

## S3 method for class 'matrix'
criterion(x, order = NULL, method = NULL, force_loss = FALSE, ...)

## S3 method for class 'data.frame'
criterion(x, order = NULL, method = NULL, force_loss = FALSE, ...)

## S3 method for class 'table'
criterion(x, order = NULL, method = NULL, force_loss = FALSE, ...)

Arguments

x	an object of class dist or a matrix (currently no functions are implemented for array).
order	an object of class ser_permutation suitable for x. If NULL, the identity permutation is used.
method	a character vector with the names of the criteria to be employed (see list_criterion_methods()), or NULL (default) in which case all available criteria are used.
force_loss	logical; should merit function be converted into loss functions by multiplying with -1?
...	additional parameters passed on to the criterion method.

Details

Criteria for distance matrices (dist)

For a symmetric dissimilarity matrix $D$ with elements $d(i,j)$ where $i, j = 1 \ldots n$, the aim is generally to place low distance values close to the diagonal. The following criteria to judge the quality of a certain permutation of the objects in a dissimilarity matrix are currently implemented (for a more detailed description and an experimental comparison see Hahsler (2017)):

• Gradient measures: "Gradient_raw", "Gradient_weighted" (Hubert et al, 2001)

  A symmetric dissimilarity matrix where the values in all rows and columns only increase when moving away from the main diagonal is called a perfect anti-Robinson matrix (Robinson 1951). A suitable merit measure which quantifies the divergence of a matrix from the anti-Robinson form is

  $M(D) = \sum_{i=1}^n \sum_{i<k<j} f(d_{ij}, d_{ik}) + \sum_{i<k<j} f(d_{ij}, d_{kj})$

  where $f(.,.)$ is a function which defines how a violation or satisfaction of a gradient condition for an object triple ($O_i, O_k, O_j$) is counted.

  Hubert et al (2001) suggest two functions. The first function is given by:

  $f(z,y) = sign(y-z) = +1 \ \text{if}\ z < y;\ 0\ \text{if}\ z = y; \ \text{and}\ -1 \ \text{if}\ z > y.$

  It results in raw number of triples satisfying the gradient constraints minus triples which violate the constraints.

  The second function is defined as:

  $f(z,y) = |y-z| sign(y-z) = y-z$

  It weights the each satisfaction or violation by the difference by its magnitude given by the absolute difference between the values.

• Anti-Robinson events: "AR_events", "AR_deviations" (Chen, 2002)

  "AR_events" counts the number of violations of the anti-Robinson form.

  $L(D) = \sum_{i=1}^n \sum_{i<k<j} f(d_{ik}, d_{ij}) + \sum_{i<k<j} f(d_{kj}, d_{ij})$

  with

  $f(z, y) = I(z, y) = 1 \ \text{if}\ z < y\ \text{and}\ 0 \ \text{otherwise,}$

  where $I(.)$ is an indicator function returning 1 only for violations. Chen (2002) presented a formulation for an equivalent loss function and called the violations anti-Robinson events.

  "AR_deviations": Chen (2002) also introduced a weighted versions of the loss function by using

  $f(z, y) = |y-z|I(z, y)$

  which weights each violation by the deviation.

• Relative generalized Anti-Robinson events: "RGAR" (Tien et al, 2008)

  Counts Anti-Robinson events in a variable band (window specified by w defaults to the maximum of $n-1$) around the main diagonal and normalizes by the maximum of possible events.

  $L(D) = 1/m \sum_{i=1}^n \sum_{(i-w)\le j<k<i} I(d_{ij} < d_{ik}) + \sum_{i<j<k\le(i+w))} I(d_{ij} > d_{ik})$

  where $m=(2/3-n)w + nw^2 - 2/3 w^3$, the maximal number of possible anti-Robinson events in the window. The window size $w$ represents the number of neighboring objects (number of entries from the diagonal of the distance matrix) are considered. The window size is $2 \le w < n$, where smaller values result in focusing on the local structure while larger values look at the global structure.

  ... parameters are:

  • w window size. Default is to use a pct of 100% of $n$.

  • pct and alternative specification of w as a percentage of $n$ in $(0, 100]$.

  • relative logical; can be set to FALSE to get the GAR, i.e., the absolute number of AR events in the window.

• Banded anti-Robinson form criterion: "BAR" (Earle and Hurley, 2015)

  Simplified measure for closeness to the anti-Robinson form in a band of size $b$ with $1 <= b < n$ around the diagonal.

  $L(D) = \sum_{|i-j|<=b} (b+1-|i-j|) d_{ij}$

  For $b = 1$ the measure reduces to the Hamiltonian path length. For $b = n-1$ the measure is equivalent to ARc defined (Earle and Hurley, 2015). Note that ARc is equivalent to the Linear Seriation criterion (scaled by 1/2).

  ... parameter is: b band size defaults to a band of 20% of $n$.

• Hamiltonian path length: "Path_length" (Caraux and Pinloche, 2005)

  The order of the objects in a dissimilarity matrix corresponds to a path through a graph where each node represents an object and is visited exactly once, i.e., a Hamilton path. The length of the path is defined as the sum of the edge weights, i.e., dissimilarities.

  $L(D) = \sum_{i=1}^{n-1} d_{i,i+1}$

  The length of the Hamiltonian path is equal to the value of the minimal span loss function (as used by Chen 2002). Both notions are related to the traveling salesperson problem (TSP).

  If order is not unique or there are non-finite distance values NA is returned.

• Lazy path length: "Lazy_path_length" (Earl and Hurley, 2015)

  A weighted version of the Hamiltonian path criterion. This loss function postpones larger distances to later in the order (i.e., a lazy traveling sales person).

  $L(D) = \sum_{i=1}^{n-1} (n-i) d_{i,i+1}$

  Earl and Hurley (2015) proposed this criterion for reordering in visualizations to concentrate on closer objects first.

• Inertia criterion: "Inertia" (Caraux and Pinloche, 2005)

  Measures the moment of the inertia of dissimilarity values around the diagonal as

  $M(D) = \sum_{i=1}^n \sum_{j=1}^n d(i,j)|i-j|^2$

  $|i-j|$ is used as a measure for the distance to the diagonal and $d(i,j)$ gives the weight. This criterion gives higher weight to values farther away from the diagonal. It increases with quality.

• Least squares criterion: "Least_squares" (Caraux and Pinloche, 2005)

  The sum of squared differences between distances and the rank differences:

  $L(D) = \sum_{i=1}^n \sum_{j=1}^n (d(i,j) - |i-j|)^2,$

  where $d(i,j)$ is an element of the dissimilarity matrix $D$ and $|i-j|$ is the rank difference between the objects.

  Note that if Euclidean distance is used to calculate $D$ from a data matrix $X$, the order of the elements in $X$ by projecting them on the first principal component of $X$ minimizes this criterion. The least squares criterion is related to unidimensional scaling.

• Linear Seriation Criterion: "LS" (Hubert and Schultz, 1976)

  Weights the distances with the absolute rank differences.

  $L(D) \sum_{i,j=1}^n d(i,j) (-|i-j|)$

• 2-Sum Criterion: "2SUM" (Barnard, Pothen and Simon, 1993)

  The 2-Sum loss criterion multiplies the similarity between objects with the squared rank differences.

  $L(D) \sum_{i,j=1}^n 1/(1+d(i,j)) (i-j)^2,$

  where $s(i,j) = 1/(1+d(i,j))$ represents the similarity between objects $i$ and $j$.

• Absolute Spearman Correlation "Rho"

  The absolute value of the Spearman rank correlation between the original distances and the rank differences in the order.

• Matrix measures: "ME", "Moore_stress", "Neumann_stress"

  These criteria are defined on general matrices (see below for definitions). The dissimilarity matrix is first converted into a similarity matrix using $S = 1/(1+D)$. If a different transformation is required, then perform the transformation first and supply a matrix instead of a dist object.

Criteria for matrices (matrix)

For a general matrix $X = x_{ij}$, $i = 1 \ldots n$ and $j = 1 \ldots m$, currently the following loss/merit functions are implemented:

• Measure of Effectiveness: "ME" (McCormick, 1972).

  The measure of effectiveness (ME) for matrix $X$, is defined as

  $M(X) = 1/2 \sum_{i=1}^{n} \sum_{j=1}^{m} x_{i,j}(x_{i,j-1}+x_{i,j+1}+x_{i-1,j}+x_{i+1,j})$

  with, by convention

  $x_{0,j}=x_{m+1,j}=x_{i,0}=x_{i,n+1}=0.$

  ME is a merit measure, i.e. a higher ME indicates a better arrangement. Maximizing ME is the objective of the bond energy algorithm (BEA). ME is not defined for matrices with negative values. NA is returned in this case.

• Weighted correlation coefficient: "Cor_R" (Deutsch and Martin, 1971)

  Developed as the Measure of Effectiveness for the Moment Ordering Algorithm. R is a merit measure normalized so that its value always lies in $[-1,1]$. For the special case of a square matrix $R=1$ corresponds to only the main diagonal being filled, $R=0$ to a random distribution of value throughout the array, and $R=-1$ to the opposite diagonal only being filled.

• Matrix Stress: "Moore_stress", "Neumann_stress" (Niermann, 2005)

  Stress measures the conciseness of the presentation of a matrix/table and can be seen as a purity function which compares the values in a matrix/table with its neighbors. The stress measure used here is computed as the sum of squared distances of each matrix entry from its adjacent entries.

  $L(X) = \sum_{i=1}^n \sum_{j=1}^m \sigma_{ij}$

  The following types of neighborhoods are available:

  • Moore: comprises the eight adjacent entries.

    $\sigma_{ij} = \sum_{k=\max(1,i-1)}^{\min(n,i+1)} \sum_{l=\max(1,j-1)}^{\min(m,j+1)} (x_{ij} - x_{kl})^2$

  • Neumann: comprises the four adjacent entries.

    $\sigma_{ij} = \sum_{k=\max(1,i-1)}^{\min(n,i+1)} (x_{ij} - x_{kj})^2 + \sum_{l=\max(1,j-1)}^{\min(m,j+1)} (x_{ij} - x_{il})^2$

  The major difference between the Moore and the Neumann neighborhood is that for the later the contribution of row and column permutations to stress are independent and thus can be optimized independently.

Value

A named vector of real values.

Author(s)

Michael Hahsler

References

Barnard, S.T., A. Pothen, and H. D. Simon (1993): A Spectral Algorithm for Envelope Reduction of Sparse Matrices. In Proceedings of the 1993 ACM/IEEE Conference on Supercomputing, 493–502. Supercomputing '93. New York, NY, USA: ACM.

Caraux, G. and S. Pinloche (2005): Permutmatrix: A Graphical Environment to Arrange Gene Expression Profiles in Optimal Linear Order, Bioinformatics, 21(7), 1280–1281.

Chen, C.-H. (2002): Generalized association plots: Information visualization via iteratively generated correlation matrices, Statistica Sinica, 12(1), 7–29.

Deutsch, S.B. and J.J. Martin (1971): An ordering algorithm for analysis of data arrays. Operational Research, 19(6), 1350–1362. doi:10.1287/opre.19.6.1350

Earle, D. and C.B. Hurley (2015): Advances in Dendrogram Seriation for Application to Visualization. Journal of Computational and Graphical Statistics, 24(1), 1–25. doi:10.1080/10618600.2013.874295

Hahsler, M. (2017): An experimental comparison of seriation methods for one-mode two-way data. European Journal of Operational Research, 257, 133–143. doi:10.1016/j.ejor.2016.08.066

Hubert, L. and J. Schultz (1976): Quadratic Assignment as a General Data Analysis Strategy. British Journal of Mathematical and Statistical Psychology, 29(2). Blackwell Publishing Ltd. 190–241. doi:10.1111/j.2044-8317.1976.tb00714.x

Hubert, L., P. Arabie, and J. Meulman (2001): Combinatorial Data Analysis: Optimization by Dynamic Programming. Society for Industrial Mathematics. doi:10.1137/1.9780898718553

Niermann, S. (2005): Optimizing the Ordering of Tables With Evolutionary Computation, The American Statistician, 59(1), 41–46. doi:10.1198/000313005X22770

McCormick, W.T., P.J. Schweitzer and T.W. White (1972): Problem decomposition and data reorganization by a clustering technique, Operations Research, 20(5), 993-1009. doi:10.1287/opre.20.5.993

Robinson, W.S. (1951): A method for chronologically ordering archaeological deposits, American Antiquity, 16, 293–301. doi:10.2307/276978

Tien, Y-J., Yun-Shien Lee, Han-Ming Wu and Chun-Houh Chen (2008): Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles, BMC Bioinformatics, 9(155), 1–16. doi:10.1186/1471-2105-9-155

See Also

Other criterion: registry_for_criterion_methods

Examples

## create random data and calculate distances
m <- matrix(runif(20),ncol=2)
d <- dist(m)

## get an order for rows (optimal for the least squares criterion)
o <- seriate(d, method = "MDS")
o

## compare the values for all available criteria
rbind(
    unordered = criterion(d),
    ordered = criterion(d, o)
)

## compare RGAR by window size (from local to global)
w <- 2:(nrow(m)-1)
RGAR <- sapply(w, FUN = function (w)
  criterion(d, o, method="RGAR", w = w))
plot(w, RGAR, type = "b", ylim = c(0,1),
  xlab = "Windows size (w)", main = "RGAR by window size")

---

Dissimilarity Plot

Description

Visualizes a dissimilarity matrix using seriation and matrix shading using the method developed by Hahsler and Hornik (2011). Entries with lower dissimilarities (higher similarity) are plotted darker. Dissimilarity plots can be used to uncover hidden structure in the data and judge cluster quality.

Usage

dissplot(
  x,
  labels = NULL,
  method = "spectral",
  control = NULL,
  lower_tri = TRUE,
  upper_tri = "average",
  diag = TRUE,
  cluster_labels = TRUE,
  cluster_lines = TRUE,
  reverse_columns = FALSE,
  options = NULL,
  ...
)

## S3 method for class 'reordered_cluster_dissimilarity_matrix'
plot(
  x,
  lower_tri = TRUE,
  upper_tri = "average",
  diag = TRUE,
  options = NULL,
  ...
)

## S3 method for class 'reordered_cluster_dissimilarity_matrix'
print(x, ...)

ggdissplot(
  x,
  labels = NULL,
  method = "spectral",
  control = NULL,
  lower_tri = TRUE,
  upper_tri = "average",
  diag = TRUE,
  cluster_labels = TRUE,
  cluster_lines = TRUE,
  reverse_columns = FALSE,
  ...
)

Arguments

x	an object of class dist.
labels	NULL or an integer vector of the same length as rows/columns in x indicating the cluster membership for each object in x as consecutive integers starting with one. The labels are used to reorder the matrix.
method	A single character string indicating the seriation method used to reorder the clusters (inter cluster seriation) as well as the objects within each cluster (intra cluster seriation). If different algorithms for inter and intra cluster seriation are required, method can be a list of two named elements (inter_cluster and intra_cluster each containing the name of the respective seriation method. Use list_seriation_methods() with kind = "dist" to find available algorithms. Set method to NA to plot the matrix as is (no or, if cluster labels are supplied, only coarse seriation). For intra cluster reordering with the special method "silhouette width" is available (for dissplot() only). Objects in clusters are then ordered by silhouette width (from silhouette plots). If no method is given, the default method of seriate.dist() is used. A third list element (named aggregation) can be added to control how inter cluster dissimilarities are computed from from the given dissimilarity matrix. The choices are "avg" (average pairwise dissimilarities; average-link), "min" (minimal pairwise dissimilarities; single-link), "max" (maximal pairwise dissimilarities; complete-link), and "Hausdorff" (pairs up each point from one cluster with the most similar point from the other cluster and then uses the largest dissimilarity of paired up points).
control	a list of control options passed on to the seriation algorithm. In case of two different seriation algorithms, control can contain a list of two named elements (inter_cluster and intra_cluster) containing each a list with the control options for the respective algorithm.
upper_tri, lower_tri, diag	a logical indicating whether to show the upper triangle, the lower triangle or the diagonal of the distance matrix. The string "average" can also be used to display within and between cluster averages in the two triangles.
cluster_labels	a logical indicating whether to display cluster labels in the plot.
cluster_lines	a logical indicating whether to draw lines to separate clusters.
reverse_columns	a logical indicating if the clusters are displayed on the diagonal from north-west to south-east (FALSE; default) or from north-east to south-west (TRUE).
options	a list with options for plotting the matrix (dissplot only). plot a logical indicating if a plot should be produced. if FALSE, the returned object can be plotted later using the function plot which takes as the second argument a list of plotting options (see options below). silhouettes a logical indicating whether to include a silhouette plot (see Rousseeuw, 1987). threshold a numeric. If used, only plot distances below the threshold are displayed. Consider also using zlim for this purpose. col colors used for the image plot. key a logical indicating whether to place a color key below the plot. zlim range of values to display (defaults to range x). axes "auto" (default; enabled for less than 25 objects), "y" or "none". main title for the plot. newpage a logical indicating whether to start plot on a new page (see grid.newpage(). pop a logical indicating whether to pop the created viewports? (see package grid) gp, gp_lines, gp_labels objects of class gpar containing graphical parameters for the plot lines and labels (see gpar().
...	dissplot(): further arguments are added to options. ggdissplot() further arguments are passed on to ggpimage().

Details

The plot can also be used to visualize cluster quality (see Ling 1973). Objects belonging to the same cluster are displayed in consecutive order. The placement of clusters and the within cluster order is obtained by a seriation algorithm which tries to place large similarities/small dissimilarities close to the diagonal. Compact clusters are visible as dark squares (low dissimilarity) on the diagonal of the plot. Additionally, a Silhouette plot (Rousseeuw 1987) is added. This visualization is similar to CLUSION (see Strehl and Ghosh 2002), however, allows for using arbitrary seriating algorithms.

Note: Since pimage() uses grid, it should not be mixed with base R primitive plotting functions.

Value

dissplot() returns an invisible object of class cluster_proximity_matrix with the following elements:

order	NULL or integer vector giving the order used to plot x.
cluster_order	NULL or integer vector giving the order of the clusters as plotted.
method	vector of character strings indicating the seriation methods used for plotting x.
k	NULL or integer scalar giving the number of clusters generated.
description	a data.frame containing information (label, size, average intra-cluster dissimilarity and the average silhouette) for the clusters as displayed in the plot (from top/left to bottom/right).

This object can be used for plotting via plot(x, options = NULL, ...), where x is the object and options contains a list with plotting options (see above).

ggdissplot() returns a ggplot2 object representing the plot.

The plot description as an object of class reordered_cluster_dissimilarity_matrix.

Author(s)

Michael Hahsler

References

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

Ling, R.F. (1973): A computer generated aid for cluster analysis. Communications of the ACM, 16(6), 355–361. doi:10.1145/362248.362263

Rousseeuw, P.J. (1987): Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(1), 53–65. doi:10.1016/0377-0427(87)90125-7

Strehl, A. and Ghosh, J. (2003): Relationship-based clustering and visualization for high-dimensional data mining. INFORMS Journal on Computing, 15(2), 208–230. doi:10.1287/ijoc.15.2.208.14448

See Also

Other plots: VAT(), bertinplot(), hmap(), palette(), pimage()

Examples

data("iris")

# shuffle rows
x_iris <- iris[sample(seq(nrow(iris))), -5]
d <- dist(x_iris)

# Plot original matrix
dissplot(d, method = NA)

# Plot reordered matrix using the nearest insertion algorithm (from tsp)
dissplot(d, method = "TSP", main = "Seriation (TSP)")

# Cluster iris with k-means and 3 clusters and reorder the dissimality matrix
l <- kmeans(x_iris, centers = 3)$cluster
dissplot(d, labels = l, main = "k-means")

# show only distances as lower triangle
dissplot(d, labels = l, main = "k-means", lower_tri = TRUE, upper_tri = FALSE)

# Use a grid layout to place several plots on a page
library("grid")
grid.newpage()
pushViewport(viewport(layout=grid.layout(nrow = 2, ncol = 2),
    gp = gpar(fontsize = 8)))
pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 1))

# Visualize the clustering (using Spectral between clusters and MDS within)
res <- dissplot(d, l, method = list(inter = "Spectral", intra = "MDS"),
  main = "K-Means + Seriation", newpage = FALSE)

popViewport()
pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 2))

# More visualization options. Note that we reuse the reordered object res!
# color: use 10 shades red-blue, biased towards small distances
plot(res, main = "K-Means + Seriation (red-blue + biased)",
    col= bluered(10, bias = .5), newpage = FALSE)

popViewport()
pushViewport(viewport(layout.pos.row = 2, layout.pos.col = 1))

# Threshold (using zlim) and cubic scale to highlight differences
plot(res, main = "K-Means + Seriation (cubic + threshold)",
    zlim = c(0, 2), col = grays(100, power = 3), newpage = FALSE)

popViewport()
pushViewport(viewport(layout.pos.row = 2, layout.pos.col = 2))

# Use gray scale with logistic transformation
plot(res, main = "K-Means + Seriation (logistic scale)",
  col = gray(
    plogis(seq(max(res$x_reordered), min(res$x_reordered), length.out = 100),
      location = 2, scale = 1/2, log = FALSE)
    ),
  newpage = FALSE)

popViewport(2)

# The reordered_cluster_dissimilarity_matrix object
res
names(res)

## --------------------------------------------------------------------
## ggplot-based dissplot
if (require("ggplot2")) {

library("ggplot2")

# Plot original matrix
ggdissplot(d, method = NA)

# Plot seriated matrix
ggdissplot(d, method = "TSP") +
  labs(title = "Seriation (TSP)")

# Cluster iris with k-means and 3 clusters
l <- kmeans(x_iris, centers = 3)$cluster

ggdissplot(d, labels = l) +
  labs(title = "K-means + Seriation")

# show only lower triangle
ggdissplot(d, labels = l, lower_tri = TRUE, upper_tri = FALSE) +
  labs(title = "K-means + Seriation")

# No lines or cluster labels and add a label for the color key (fill)
ggdissplot(d, labels = l, cluster_lines = FALSE, cluster_labels = FALSE) +
  labs(title = "K-means + Seriation", fill = "Distances\n(Euclidean)")

# Diverging color palette with manual set midpoint and different seriation methods
ggdissplot(d, l, method = list(inter = "Spectral", intra = "MDS")) +
  labs(title = "K-Means + Seriation", subtitle = "biased color scale") +
  scale_fill_gradient2(midpoint = median(d))

# Use manipulate scale using package scales
library("scales")

# Threshold (using limit and na.value) and cubic scale to highlight differences
cubic_dist_trans <- trans_new(
  name = "cubic",
  # note that we have to do the inverse transformation for distances
  trans = function(x) x^(1/3),
  inverse = function(x) x^3
)

ggdissplot(d, l, method = list(inter = "Spectral", intra = "MDS")) +
  labs(title = "K-Means + Seriation", subtitle = "cubic + biased color scale") +
  scale_fill_gradient(low = "black", high = "white",
    limit = c(0,2), na.value = "white",
    trans = cubic_dist_trans)

# Use gray scale with logistic transformation
logis_2_.5_dist_trans <- trans_new(
  name = "Logistic transform (location, scale)",
  # note that we have to do the inverse transformation for distances
  trans = function(x) plogis(x, location = 2, scale = .5, log = FALSE),
  inverse = function(x) qlogis(x, location = 2, scale = .5, log = FALSE),
)

ggdissplot(d, l, method = list(inter = "Spectral", intra = "MDS")) +
  labs(title = "K-Means + Seriation", subtitle = "logistic color scale") +
  scale_fill_gradient(low = "black", high = "white",
    trans = logis_2_.5_dist_trans,
    breaks = c(0, 1, 2, 3, 4))
}

---

Extracting Order Information from a Permutation Object

Description

Method to get the order information from an object of class ser_permutation or ser_permutation_vector. Order information can be extracted as a permutation vector, a vector containing each object's rank or a permutation matrix.

Usage

get_order(x, ...)

## S3 method for class 'ser_permutation_vector'
get_order(x, ...)

## S3 method for class 'ser_permutation'
get_order(x, dim = 1, ...)

## S3 method for class 'hclust'
get_order(x, ...)

## S3 method for class 'dendrogram'
get_order(x, ...)

## S3 method for class 'integer'
get_order(x, ...)

## S3 method for class 'numeric'
get_order(x, ...)

get_rank(x, ...)

get_permutation_matrix(x, ...)

Arguments

x	an object of class ser_permutation or ser_permutation_vector.
...	further arguments are ignored for get_order(). For get_rank() and for get_permutation_matrix() the additional arguments are passed on to get_order() (e.g., as dim).
dim	order information for which dimension should be returned?

Details

get_order() returns the permutation as an integer vector which arranges the objects in the seriation order. That is, a vector with the index of the first, second, $..., n$-th object in the order defined by the permutation. These permutation vectors can directly be used to reorder objects using subsetting with "[". Note: In seriation we usually use these order-based permutation vectors. Note on names: While R's order() returns an unnamed vector, get_order() returns names (if available). The names are the object label corresponding to the index at that position. Therefore, the names in the order are in the order after the permutation.

get_rank() returns the seriation as an integer vector containing the rank/position for each objects after the permutation is applied. That is, a vector with the position of the first, second, $..., n$-th object after permutation. Note: Use order() to convert ranks back to an order.

get_permutation_matrix() returns a $n \times n$ permutation matrix.

Value

Returns an integer permutation vector/a permutation matrix.

Author(s)

Michael Hahsler

See Also

Other permutation: permutation_vector2matrix(), permute(), ser_dist(), ser_permutation(), ser_permutation_vector()

Examples

## create a random ser_permutation_vector
## Note that ser_permutation_vector is a single permutation vector
x <- structure(1:10, names = paste0("X", 1:10))
o <- sample(x)
o

p <- ser_permutation_vector(o)
p

get_order(p)
get_rank(p)
get_permutation_matrix(p)

## reorder objects using subsetting, the provided permute function or by
## multiplying the with the permutation matrix. We use here
x[get_order(p)]
permute(x, p)
drop(get_permutation_matrix(p) %*%  x)

## ser_permutation contains one permutation vector for each dimension
p2 <- ser_permutation(p, sample(5))
p2

get_order(p2, dim = 2)
get_rank(p2, dim = 2)
get_permutation_matrix(p2, dim = 2)

---

Plot Heat Map Reordered Using Seriation

Description

Provides heatmaps reordered using several different seriation methods. This includes dendrogram based reordering with optimal leaf order and matrix seriation-based heat maps.

Usage

hmap(
  x,
  distfun = stats::dist,
  method = "OLO_complete",
  control = NULL,
  scale = c("none", "row", "column"),
  plot_margins = "auto",
  col = NULL,
  col_dist = grays(power = 2),
  row_labels = NULL,
  col_labels = NULL,
  ...
)

gghmap(
  x,
  distfun = stats::dist,
  method = "OLO_complete",
  control = NULL,
  scale = c("none", "row", "column"),
  prop = FALSE,
  ...
)

Arguments

x	a matrix or a dissimilarity matrix of class dist. If a dissimilarity matrix is used, then the distfun is ignored.
distfun	function used to compute the distance (dissimilarity) between both rows and columns. For gghmap(), this parameter is passed on in control.
method	a character strings indicating the used seriation algorithm (see seriate.dist()). If the method results in a dendrogram then stats::heatmap() is used to show the dendrograms, otherwise reordered distance matrices are shown instead.
control	a list of control options passed on to the seriation algorithm specified in method.
scale	character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. Default is none.
plot_margins	character indicating what to show in the margins. Options are: "auto", "dendrogram", "distances", or "none".
col	a list of colors used.
col_dist	colors used for displaying distances.
row_labels, col_labels	a logical indicating if row and column labels in x should be displayed. If NULL then labels are displayed if the x contains the appropriate dimname and the number of labels is 25 or less. A character vector of the appropriate length with labels can also be supplied.
...	further arguments passed on to stats::heatmap().
prop	logical; change the aspect ratio so cells in the image have a equal width and height.

Details

For dendrogram based heat maps, the arguments are passed on to stats::heatmap() in stats. The following arguments for heatmap() cannot be used: margins, Rowv, Colv, hclustfun, reorderfun.

For seriation-based heat maps further arguments include:

• gp an object of class gpar containing graphical parameters (see gpar() in package grid).

• newpage a logical indicating whether to start plot on a new page

• prop a logical indicating whether the height and width of x should be plotted proportional to its dimensions.

• showdist Display seriated dissimilarity matrices? Values are "none", "both", "rows" or "columns".

• key logical; show a colorkey?

• key.lab Label plotted next to the color key.

• margins bottom and right-hand-side margins are calculated automatically or can be specifies as a vector of two numbers (in lines).

• zlim range of values displayed.

• col, col_dist color palettes used.

Value

An invisible list with elements:

rowInd, colInd	index permutation vectors.
reorder_method	name of the method used to reorder the matrix.

The list may contain additional elements (dendrograms, colors, etc).

Author(s)

Michael Hahsler

See Also

Other plots: VAT(), bertinplot(), dissplot(), palette(), pimage()

Examples

data("Wood")

# regular heatmap from package stats
heatmap(Wood, main = "Wood (standard heatmap)")

# Default heatmap does Euclidean distance, hierarchical clustering with
# complete-link and optimal leaf ordering. Note that the rows are
# ordered top-down in the seriation order (stats::heatmap orders in reverse)
hmap(Wood, main = "Wood (opt. leaf ordering)")
hmap(Wood, plot_margins = "distances", main = "Wood (opt. leaf ordering)")
hmap(Wood, plot_margins = "none", main = "Wood (opt. leaf ordering)")

# Heatmap with correlation-based distance, green-red color (greenred is
# predefined) and optimal leaf ordering and no row label
dist_cor <- function(x) as.dist(sqrt(1 - cor(t(x))))
hmap(Wood, distfun = dist_cor, col = greenred(100),
  main = "Wood (reorded by corr. between obs.)")

# Heatmap with order based on the angle in two-dimensional MDS space.
hmap(Wood, method = "MDS_angle", col = greenred(100), row_labels = FALSE,
  main = "Wood (reorderd using ange in MDS space)")

# Heatmap for distances
d <- dist(Wood)
hmap(d, main = "Wood (Euclidean distances)")

# order-based with dissimilarity matrices
hmap(Wood, method = "MDS_angle",
  col = greenred(100), col_dist = greens(100, power = 2),
  keylab = "norm. Expression", main = "Wood (reorderd with distances)")

# without the distance matrices
hmap(Wood, method = "MDS_angle", plot_margins = "none",
  col = greenred(100), main = "Wood (reorderd without distances)")

# Manually create a simple heatmap with pimage.
o <- seriate(Wood, method = "heatmap",
   control = list(dist_fun = dist, seriation_method = "OLO_ward"))
o

pimage(Wood, o)

# Note: method heatmap calculates reorderd hclust objects which can be used
#       for many heatmap implementations like the standard implementation in
#       package stats.
heatmap(Wood, Rowv = as.dendrogram(o[[1]]), Colv = as.dendrogram(o[[2]]))

# ggplot 2 version does not support dendrograms in the margin (for now)
if (require("ggplot2")) {
  library("ggplot2")

  gghmap(Wood) + labs(title = "Wood", subtitle = "Optimal leaf ordering")

  # More parameters (see ? ggpimage): reverse column order and flip axes, make a proportional plot
  gghmap(Wood, reverse_columns = TRUE) +
    labs(title = "Wood", subtitle = "Optimal leaf ordering")

  gghmap(Wood, flip_axes = TRUE) +
    labs(title = "Wood", subtitle = "Optimal leaf ordering")

  gghmap(Wood, flip_axes = TRUE, prop = TRUE) +
    labs(title = "Wood", subtitle = "Optimal leaf ordering")

  dist_cor <- function(x) as.dist(sqrt(1 - cor(t(x))))
  gghmap(Wood, distfun = dist_cor) +
    labs(title = "Wood", subtitle = "Reorded by correlation between observations") +
    scale_fill_gradient2(low = "darkgreen", high = "red")

  gghmap(d, prop = TRUE) +
    labs(title = "Wood", subtitle = "Euclidean distances, reordered")

  # Note: the ggplot2-based version currently cannot show distance matrices
  #      in the same plot.

  # Manually seriate and plot as pimage.
  o <- seriate(Wood, method = "heatmap", control = list(dist_fun = dist,
    seriation_method = "OLO_ward"))
  o

  ggpimage(Wood, o)
}

---

Irish Referendum Data Set

Description

A data matrix containing the results of 8 referenda for 41 Irish communities used in Falguerolles et al (1997).

Format

The format is a 41 x 9 matrix. Two values are missing.

Details

Column 6 contains the size of the Electorate in 1992.

Source

The data was kindly provided by Guenter Sawitzki.

References

de Falguerolles, A., Friedrich, F., Sawitzki, G. (1997) A Tribute to J. Bertin's Graphical Data Analysis. In: Proceedings of the SoftStat '97 (Advances in Statistical Software 6), 11–20.

See Also

Other data: Chameleon, Munsingen, SupremeCourt, Townships, Wood, Zoo, create_lines_data(), is.robinson()

Examples

data(Irish)

---

Create and Recognize Robinson and Pre-Robinson Matrices

Description

Provides functions to create and recognize (anti) Robinson and pre-Robinson matrices. A (anti) Robinson matrix has strictly decreasing (increasing) values when moving away from the main diagonal. A pre-Robinson matrix is a matrix which can be transformed into a perfect Robinson matrix using simultaneous permutations of rows and columns.

Usage

is.robinson(x, anti = TRUE, pre = FALSE)

random.robinson(n, anti = TRUE, pre = FALSE, noise = 0)

Arguments

x	a symmetric, positive matrix or a dissimilarity matrix (a dist object).
anti	logical; check for anti Robinson structure? Note that for distances, anti Robinson structure is appropriate.
pre	logical; recognize/create pre-Robinson matrices.
n	number of objects.
noise	noise intensity between 0 and 1. Zero means no noise. Noise more than zero results in non-Robinson matrices.

Details

Note that the default matrices are anti Robinson matrices. This is done because distance matrices (the default in R) are typically anti Robinson matrices with values increasing when moving away from the diagonal.

Robinson matrices are recognized using the fact that they have zero anti Robinson events. For pre-Robinson matrices we use spectral seriation first since spectral seriation is guaranteed to perfectly reorder pre-Robinson matrices (see Laurent and Seminaroti, 2015).

Random pre-Robinson matrices are generated by reversing the process of unidimensional scaling. We randomly (uniform distribution with range $[0,1]$) choose $x$ coordinates for n points on a straight line and calculate the pairwise distances. For Robinson matrices, the points are sorted first according to $x$. For noise, $y$ coordinates is added. The coordinates are chosen uniformly between 0 and noise, with noise between 0 and 1.

Value

A single logical value.

References

M. Laurent, M. Seminaroti (2015): The quadratic assignment problem is easy for Robinsonian matrices with Toeplitz structure, Operations Research Letters 43(1), 103–109.

See Also

Other data: Chameleon, Irish, Munsingen, SupremeCourt, Townships, Wood, Zoo, create_lines_data()

Examples

## create a perfect anti Robinson structure
m <- random.robinson(10)
pimage(m)

is.robinson(m)

## permute the structure to make it not Robinsonian. However,
## it is still pre-Robinson.
o <- sample(10)
m2 <- permute(m, ser_permutation(o,o))
pimage(m2)

is.robinson(m2)
is.robinson(m2, pre = TRUE)

## create a binary random Robinson matrix (not anti Robinson)
m3 <- random.robinson(10, anti = FALSE) > .7
pimage(m3)
is.robinson(m3, anti = FALSE)

## create matrices with noise (as distance matrices)
m4 <- as.dist(random.robinson(50, pre = FALSE, noise = .1))
pimage(m4)
criterion(m4, method = "AR")

m5 <- as.dist(random.robinson(50, pre = FALSE, noise = .5))
pimage(m5)
criterion(m5, method = "AR")

---

Locally Linear Embedding (LLE)

Description

Performs the non linear dimensionality reduction method locally linear embedding proposed in Roweis and Saul (2000).

Usage

lle(x, m, k, reg = 2)

Arguments

x	a matrix.
m	dimensions of the desired embedding.
k	number of neighbors.
reg	regularization method. 1, 2 and 3, by default 2. See details.

Details

LLE tries to find a lower-dimensional projection which preserves distances within local neighborhoods. This is done by (1) find for each object the k nearest neighbors, (2) construct the LLE weight matrix which represents each point as a linear combination of its neighborhood, and (2) perform partial eigenvalue decomposition to find the embedding.

The reg parameter allows the decision between different regularization methods. As one step of the LLE algorithm, the inverse of the Gram-matrix $G\in R^{kxk}$ has to be calculated. The rank of $G$ equals $m$ which is mostly smaller than $k$ - this is why a regularization $G^{(i)}+r\cdot I$ should be performed. The calculation of regularization parameter $r$ can be done using different methods:

• reg = 1: standardized sum of eigenvalues of $G$ (Roweis and Saul; 2000)

• reg = 2 (default): trace of Gram-matrix divided by $k$ (Grilli, 2007)

• reg = 3: constant value 3*10e-3

Value

a matrix of vector with the embedding.

Author(s)

Michael Hahsler (based on code by Holger Diedrich and Markus Abel)

References

Roweis, Sam T. and Saul, Lawrence K. (2000), Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 290(5500), 2323–2326. doi:10.1126/science.290.5500.2323

Grilli, Elisa (2007) Automated Local Linear Embedding with an application to microarray data, Dissertation thesis, University of Bologna. doi:10.6092/unibo/amsdottorato/380

Examples

data(iris)
x <- iris[, -5]

# project iris on 2 dimensions
conf <- lle(x, m = 2, k = 30)
conf

plot(conf, col = iris[, 5])

# project iris onto a single dimension
conf <- lle(x, m = 1, k = 30)
conf

plot_config(conf, col = iris[, 5], labels = FALSE)

---

Neighborhood functions for Seriation Method SA

Description

Definition of different local neighborhood functions for the method "SA" for seriate().

Usage

LS_swap(o, pos = sample.int(length(o), 2))

LS_insert(o, pos = sample.int(length(o), 2))

LS_reverse(o, pos = sample.int(length(o), 2))

LS_mixed(o, pos = sample.int(length(o), 2))

Arguments

o	an integer vector with the order
pos	random positions used for the local move.

Details

Local neighborhood functions are LS_insert, LS_swap, LS_reverse, and LS_mix (1/3 insertion, 1/3 swap and 1/3 reverse). Any neighborhood function can be defined.

Value

returns the new order vector representing the random neighbor.

---

Hodson's Munsingen Data Set

Description

This data set contains a grave times artifact incidence matrix for the Celtic Münsingen-Rain cemetery in Switzerland as provided by Hodson (1968) and published by Kendall 1971.

Format

A 59 x 70 0-1 matrix. Rows (graves) and columns (artifacts) are in the order determined by Hodson (1968).

References

Hodson, F.R. (1968). The La Tene Cemetery at Münsingen-Rain, Stämpfli, Bern.

Kendall, D.G. (1971): Seriation from abundance matrices. In: Hodson, F.R., Kendall, D.G. and Tautu, P., (Editors), Mathematics in the Archaeological and Historical Sciences, Edinburgh University Press, Edinburgh, 215–232.

See Also

Other data: Chameleon, Irish, SupremeCourt, Townships, Wood, Zoo, create_lines_data(), is.robinson()

Examples

data("Munsingen")

## Seriation method after Kendall (1971)
## Kendall's square symmetric matrix S and SoS
S <- function(x, w = 1) {
  sij <- function(i , j) w * sum(pmin(x[i,], x[j,]))
  h <- nrow(x)
  r <- matrix(ncol = h, nrow =h)
  for(i in 1:h) for (j in 1:h)  r[i,j] <- sij(i,j)
  r
}

SoS <- function(x) S(S(x))

## Kendall's horse shoe (Hamiltonian arc)
horse_shoe_plot <- function(mds, sigma, threshold = mean(sigma), ...) {
    plot(mds, main = paste("Kendall's horse shoe with th =", threshold), ...)
    l <- which(sigma > threshold, arr.ind=TRUE)
    for(i in 1:nrow(l))  lines(rbind(mds[l[i,1],], mds[l[i,2],]))
}

## shuffle data
x <- Munsingen[sample(nrow(Munsingen)),]

## calculate matrix and do isoMDS (from package MASS)
sigma <- SoS(x)
library("MASS")
mds <- isoMDS(1/(1+sigma))$points

## plot Kendall's horse shoe
horse_shoe_plot(mds, sigma)

## find order using a TSP
library("TSP")
tour <- solve_TSP(insert_dummy(TSP(dist(mds)), label = "cut"),
    method = "2-opt", control = list(rep = 15))
tour <- cut_tour(tour, "cut")
lines(mds[tour,], col = "red", lwd = 2)

## create and plot order
order <- ser_permutation(tour, 1:ncol(x))
bertinplot(x, order, options= list(panel=panel.circles,
    rev = TRUE))

## compare criterion values
rbind(
    random = criterion(x),
    reordered = criterion(x, order),
    Hodson = criterion(Munsingen)
   )

---

Different Useful Color Palettes

Description

Defines several color palettes for pimage(), dissplot() and hmap().

Usage

bluered(n = 100, bias = 1, power = 1, ...)

greenred(n = 100, bias = 1, power = 1, ...)

reds(n = 100, bias = 1, power = 1, ...)

blues(n = 100, bias = 1, power = 1, ...)

greens(n = 100, bias = 1, power = 1, ...)

greys(n = 100, bias = 1, power = 1, ...)

grays(n = 100, bias = 1, power = 1, ...)

Arguments

n	number of different colors produces.
bias	a positive number. Higher values give more widely spaced colors at the high end.
power	used to control how chroma and luminance is increased (1 = linear, 2 = quadratic, etc.)
...	further parameters are passed on to colorspace::sequential_hcl() or colorspace::diverging_hcl().

Details

The color palettes are created with colorspace::sequential_hcl() and colorspace::diverging_hcl().

The two sequential palettes are: reds() and grays() (or greys()).

The two diverging palettes are: bluered() and greenred().

Value

A vector with n colors.

Author(s)

Michael Hahsler

See Also

Other plots: VAT(), bertinplot(), dissplot(), hmap(), pimage()

Examples

m <- outer(1:10,1:10)
m

pimage(m)
pimage(m, col = greys(100, power = 2))
pimage(m, col = greys(100, bias = 2))
pimage(m, col = bluered(100))
pimage(m, col = bluered(100, power = .5))
pimage(m, col = bluered(100, bias = 2))
pimage(m - 25, col = greenred(20, bias = 2))

## choose your own color palettes
library(colorspace)
hcl_palettes(plot = TRUE)

## blues (with 20 shades)
pimage(m,
  col = colorspace::sequential_hcl(20, "Blues", rev = TRUE))
## blue to green (aka "Cork")
pimage(m,
  col = colorspace::diverging_hcl(100, "Cork"))

---

Conversion Between Permutation Vector and Permutation Matrix

Description

Converts between permutation vectors and matrices.

Usage

permutation_vector2matrix(x)

permutation_matrix2vector(x)

Arguments

x	A permutation vector (any object that can be converted into a permutation vector, e.g., a integer vector or a hclust object) or a matrix representing a permutation. Arguments are checked.

Value

• permutation_vector2matrix(): returns a permutation matrix.

• permutation_matrix2vector(): returns the permutation as a integer vector.

Author(s)

Michael Hahsler

See Also

Other permutation: get_order(), permute(), ser_dist(), ser_permutation(), ser_permutation_vector()

Examples

## create a random permutation vector
pv <- structure(sample(5), names = paste0("X", 1:5))
pv

## convert into a permutation matrix
pm <- permutation_vector2matrix(pv)
pm

## convert back
permutation_matrix2vector(pm)

---

Permute the Order in Various Objects

Description

Provides the generic function and methods for permuting the order of various objects including vectors, lists, dendrograms (also hclust objects), the order of observations in a dist object, the rows and columns of a matrix or data.frame, and all dimensions of an array given a suitable ser_permutation object.

Usage

permute(x, order, ...)

## S3 method for class 'array'
permute(x, order, margin = NULL, ...)

## S3 method for class 'matrix'
permute(x, order, margin = NULL, ...)

## S3 method for class 'data.frame'
permute(x, order, margin = NULL, ...)

## S3 method for class 'table'
permute(x, order, margin = NULL, ...)

## S3 method for class 'numeric'
permute(x, order, ...)

## S3 method for class 'character'
permute(x, order, ...)

## S3 method for class 'list'
permute(x, order, ...)

## S3 method for class 'dist'
permute(x, order, ...)

## S3 method for class 'dendrogram'
permute(x, order, dist = NULL, ...)

## S3 method for class 'hclust'
permute(x, order, dist = NULL, ...)

Arguments

x	an object (a list, a vector, a dist object, a matrix, an array or any other object which provides dim and standard subsetting with "[").
order	an object of class ser_permutation which contains suitable permutation vectors for x. Alternatively, a character string with the name of a seriation method appropriate for x can be specified (see seriate()). This will perform seriation and permute x. The value TRUE will permute using the default seriation method.
...	if order is the name of a seriation method, then additional arguments are passed on to seriate().
margin	specifies the dimensions to be permuted as a vector with dimension indices. If NULL, order needs to contain a permutation for all dimensions. If a single margin is specified, then order can also contain a single permutation vector. margin are ignored.
dist	the distance matrix used to create the dendrogram. Only needed if order is the name of a seriation method.

Details

The permutation vectors in ser_permutation are suitable if the number of permutation vectors matches the number of dimensions of x and if the length of each permutation vector has the same length as the corresponding dimension of x.

For 1-dimensional/1-mode data (list, vector, dist), order can also be a single permutation vector of class ser_permutation_vector or data which can be automatically coerced to this class (e.g. a numeric vector).

For dendrogram and hclust, subtrees are rotated to represent the order best possible. If the order is not achieved perfectly then the user is warned. See also reorder.hclust() for reordering hclust objects.

Value

A permuted object of the same class as x.

Author(s)

Michael Hahsler

See Also

Other permutation: get_order(), permutation_vector2matrix(), ser_dist(), ser_permutation(), ser_permutation_vector()

Examples

# List data types for permute
methods("permute")

# Permute matrix
m <- matrix(rnorm(10), 5, 2, dimnames = list(1:5, LETTERS[1:2]))
m

# Permute rows and columns
o <- ser_permutation(5:1, 2:1)
o

permute(m, o)

## permute only columns
permute(m, o, margin = 2)

## permute using PCA seriation
permute(m, "PCA")

## permute only rows using PCA
permute(m, "PCA", margin = 1)

# Permute data.frames using heatmap seration (= hierarchical
#  clustering + optimal leaf ordering)
df <- as.data.frame(m)
permute(df, "Heatmap")

# Permute objects in a dist object
d <- dist(m)
d

permute(d, c(3, 2, 1, 4, 5))

permute(d, "Spectral")

# Permute a list
l <- list(a = 1:5, b = letters[1:3], c = 0)
l

permute(l, c(2, 3, 1))

# Permute to reorder dendrogram (see also reorder.hclust)
hc <- hclust(d)
plot(hc)

plot(permute(hc, 5:1))
plot(permute(hc, 5:1, incompartible = "stop"))

plot(permute(hc, "OLO", dist = d))
plot(permute(hc, "GW", dist = d))
plot(permute(hc, "MDS", dist = d))
plot(permute(hc, "TSP", dist = d))

---

Permutation Image Plot

Description

Provides methods for matrix shading, i.e., displaying a color image for matrix (including correlation matrices and data frames) and dist objects given an optional permutation. The plot arranges colored rectangles to represent the values in the matrix. This visualization is also know as a heatmap. Implementations based on the grid graphics engine and based n ggplot2 are provided.

Usage

pimage(x, order = FALSE, ...)

## S3 method for class 'matrix'
pimage(
  x,
  order = FALSE,
  col = NULL,
  main = "",
  xlab = "",
  ylab = "",
  zlim = NULL,
  key = TRUE,
  keylab = "",
  symkey = TRUE,
  upper_tri = TRUE,
  lower_tri = TRUE,
  diag = TRUE,
  row_labels = NULL,
  col_labels = NULL,
  prop = isSymmetric(x),
  flip_axes = FALSE,
  reverse_columns = FALSE,
  ...,
  newpage = TRUE,
  pop = TRUE,
  gp = NULL
)

## S3 method for class 'table'
pimage(x, order = NULL, ...)

## S3 method for class 'data.frame'
pimage(x, order = NULL, ...)

## S3 method for class 'dist'
pimage(
  x,
  order = NULL,
  col = NULL,
  main = "",
  xlab = "",
  ylab = "",
  zlim = NULL,
  key = TRUE,
  keylab = "",
  symkey = TRUE,
  upper_tri = TRUE,
  lower_tri = TRUE,
  diag = TRUE,
  row_labels = NULL,
  col_labels = NULL,
  prop = TRUE,
  flip_axes = FALSE,
  reverse_columns = FALSE,
  ...,
  newpage = TRUE,
  pop = TRUE,
  gp = NULL
)

ggpimage(x, order = NULL, ...)

## S3 method for class 'matrix'
ggpimage(
  x,
  order = NULL,
  zlim = NULL,
  upper_tri = TRUE,
  lower_tri = TRUE,
  diag = TRUE,
  row_labels = NULL,
  col_labels = NULL,
  prop = isSymmetric(x),
  flip_axes = FALSE,
  reverse_columns = FALSE,
  ...
)

## S3 method for class 'dist'
ggpimage(
  x,
  order = NULL,
  zlim = NULL,
  upper_tri = TRUE,
  lower_tri = TRUE,
  diag = TRUE,
  row_labels = NULL,
  col_labels = NULL,
  prop = TRUE,
  flip_axes = FALSE,
  reverse_columns = FALSE,
  ...
)

Arguments

x	a matrix, a data.frame, or an object of class dist.
order	a logical where FALSE means no reordering and TRUE applies a permutation using the default seriation method for the type of x. Alternatively, any object that can be coerced to class ser_permutation can be supplied.
...	if order is the name of a seriation method then further arguments are passed on to the seriation method, otherwise they are ignored.
col	a list of colors used. If NULL, a gray scale is used (for matrix larger values are displayed darker and for dist smaller distances are darker). For matrices containing logical data, black and white is used. For matrices containing negative values a symmetric diverging color palette is used.
main	plot title.
xlab, ylab	labels for the x and y axes.
zlim	vector with two elements giving the range (min, max) for representing the values in the matrix.
key	logical; add a color key? No key is available for logical matrices.
keylab	string plotted next to the color key.
symkey	logical; if x contains negative values, should the color palate be symmetric (zero is in the middle)?
upper_tri, lower_tri, diag	a logical indicating whether to show the upper triangle, the lower triangle or the diagonal of the (distance) matrix.
row_labels, col_labels	a logical indicating if row and column labels in x should be displayed. If NULL then labels are displayed if the x contains the appropriate dimname and the number of labels is 25 or less. A character vector of the appropriate length with labels can also be supplied.
prop	logical; change the aspect ratio so cells in the image have a equal width and height.
flip_axes	logical; exchange rows and columns for plotting.
reverse_columns	logical; revers the order of how the columns are displayed.
newpage, pop, gp	Start plot on a new page, pop the viewports after plotting, and use the supplied gpar object (see grid).

Details

Plots a matrix in its original row and column orientation (image in stats reverses the rows). This means, in a plot the columns become the x-coordinates and the rows the y-coordinates (in reverse order).

Grid-based plot: The viewports used for plotting are called: "plot", "image" and "colorkey". Use grid functions to manipulate the plots (see Examples section).

ggplot2-based plot: A ggplot2 object is returned. Colors, axis limits and other visual aspects can be added using standard ggplot2 functions (labs, scale_fill_continuous, labs, etc.).

Value

Nothing.

Author(s)

Christian Buchta and Michael Hahsler

See Also

Other plots: VAT(), bertinplot(), dissplot(), hmap(), palette()

Examples

set.seed(1234)
data(iris)
x <- as.matrix(iris[sample(nrow(iris), 20) , -5])

pimage(x)

# Show all labels and flip axes, reverse columns, or change colors
pimage(x, prop = TRUE)
pimage(x, flip_axes = TRUE)
pimage(x, reverse_columns = TRUE)
pimage(x, col = grays(100))

# A matrix with positive and negative values
x_scaled <- scale(x)
pimage(x_scaled)

# Use reordering
pimage(x_scaled, order = TRUE)
pimage(x_scaled, order = "Heatmap")

## Example: Distance Matrix
# Show a reordered distance matrix (distances between rows).
# Dark means low distance. The aspect ratio is automatically fixed to 1:1
# using prop = TRUE
d <- dist(x)
pimage(d)
pimage(d, order = TRUE)

# Supress the upper triangle and diagonal
pimage(d, order = TRUE, upper = FALSE, diag = FALSE)

# Show only distances that are smaller than 2 using limits on z.
pimage(d, order = TRUE, zlim = c(0, 3))

## Example: Correlation Matrix
# we calculate correlation between rows and seriate the matrix
# and seriate by converting the correlations into distances.
# pimage reorders then rows and columns with c(o, o).
r <- cor(t(x))
o <- seriate(as.dist(sqrt(1 - r)))
pimage(r, order = c(o, o),
  upper = FALSE, diag = FALSE,
  zlim = c(-1, 1),
  reverse_columns = TRUE,
  main = "Correlation matrix")

# Add to the plot using functions in package grid
# Note: pop = FALSE allows us to manipulate viewports
library("grid")
pimage(x, order = TRUE, pop = FALSE)

# available viewports are: "main", "colorkey", "plot", "image"
current.vpTree()

# Highlight cell 2/2 with a red arrow
# Note: columns are x and rows are y.
downViewport(name = "image")
grid.lines(x = c(1, 2), y = c(-1, 2), arrow = arrow(),
  default.units = "native", gp = gpar(col = "red", lwd = 3))

# add a red box around the first 4 rows of the 2nd column
grid.rect(x = 1 + .5 , y = 4 + .5, width = 1, height = 4,
  hjust = 0, vjust = 1,
  default.units = "native", gp = gpar(col = "red", lwd = 3, fill = NA))

## remove the viewports
popViewport(0)

## put several pimages on a page (use grid viewports and newpage = FALSE)
# set up grid layout
library(grid)
grid.newpage()
top_vp <- viewport(layout = grid.layout(nrow = 1, ncol = 2,
  widths = unit(c(.4, .6), unit = "npc")))
col1_vp <- viewport(layout.pos.row = 1, layout.pos.col = 1, name = "col1_vp")
col2_vp <- viewport(layout.pos.row = 1, layout.pos.col = 2, name = "col2_vp")
splot <- vpTree(top_vp, vpList(col1_vp, col2_vp))
pushViewport(splot)

seekViewport("col1_vp")
o <- seriate(d)
pimage(x, c(o, NA), col_labels = FALSE, main = "Data",
  newpage = FALSE)

seekViewport("col2_vp")
## add the reordered dissimilarity matrix for rows
pimage(d, o, main = "Distances",
  newpage = FALSE)

popViewport(0)

##-------------------------------------------------------------
## ggplot2 Examples
if (require("ggplot2")) {

library("ggplot2")

set.seed(1234)
data(iris)
x <- as.matrix(iris[sample(nrow(iris), 20) , -5])

ggpimage(x)

# Show all labels and flip axes, reverse columns
ggpimage(x, prop = TRUE)
ggpimage(x, flip_axes = TRUE)
ggpimage(x, reverse_columns = TRUE)


# A matrix with positive and negative values
x_scaled <- scale(x)
ggpimage(x_scaled)

# Use reordering
ggpimage(x_scaled, order = TRUE)
ggpimage(x_scaled, order = "Heatmap")

## Example: Distance Matrix
# Show a reordered distance matrix (distances between rows).
# Dark means low distance. The aspect ratio is automatically fixed to 1:1
# using prop = TRUE
d <- dist(x)
ggpimage(d)
ggpimage(d, order = TRUE)

# Supress the upper triangle and diagonal
ggpimage(d, order = TRUE, upper = FALSE, diag = FALSE)

# Show only distances that are smaller than 2 using limits on z.
ggpimage(d, order = TRUE, zlim = c(0, 2))

## Example: Correlation Matrix
# we calculate correlation between rows and seriate the matrix
r <- cor(t(x))
o <- seriate(as.dist(sqrt(1 - r)))
ggpimage(r, order = c(o, o),
  upper = FALSE, diag = FALSE,
  zlim = c(-1, 1),
  reverse_columns = TRUE) + labs(title = "Correlation matrix")

## Example: Custom themes and colors
# Reorder matrix, use custom colors, add a title,
# and hide colorkey.
ggpimage(x) +
  theme(legend.position = "none") +
  labs(title = "Random Data") + xlab("Variables")

# Add lines
ggpimage(x) +
  geom_hline(yintercept = seq(0, nrow(x)) + .5) +
  geom_vline(xintercept = seq(0, ncol(x)) + .5)

# Use ggplot2 themes with theme_set
old_theme <- theme_set(theme_linedraw())
ggpimage(d)
theme_set(old_theme)

# Use custom color palettes: Gray scale, Colorbrewer (provided in ggplot2) and colorspace
ggpimage(d, order = seriate(d), upper_tri = FALSE) +
  scale_fill_gradient(low = "black", high = "white", na.value = "white")

ggpimage(d, order = seriate(d), upper_tri = FALSE) +
  scale_fill_distiller(palette = "Spectral", direction = +1, na.value = "white")

ggpimage(d, order = seriate(d), upper_tri = FALSE) +
  colorspace::scale_fill_continuous_sequential("Reds", rev = FALSE, na.value = "white")
}

---

Results of 24 Psychological Test for 8th Grade Students

Description

A data set collected by Holzinger and Swineford (1939) which consists of the results of 24 psychological tests given to 145 seventh and eighth grade students in a Chicago suburb. This data set contains the correlation matrix for the 24 test results. The data set was also used as an example for visualization of cluster analysis by Ling (1973).

Format

A 24 x 24 correlation matrix.

References

Holzinger, K. L., Swineford, F. (1939): A study in factor analysis: The stability of a bi-factor solution. Supplementary Educational Monograph, No. 48. Chicago: University of Chicago Press.

Ling, R. L. (1973): A computer generated aid for cluster analysis. Communications of the ACM, 16(6), pp. 355–361.

Examples

data("Psych24")

## create a dist object and also get rid of the one negative entry in the
## correlation matrix
d <- as.dist(1 - abs(Psych24))

pimage(d)

## do hclust as in Ling (1973)
hc <- hclust(d, method = "complete")
plot(hc)

pimage(d, hc)

## use seriation
order <- seriate(d, method = "tsp")
#order <- seriate(d, method = "tsp", control = list(method = "concorde"))
pimage(d, order)

---

Register Seriation Methods from Package DendSer

Description

Register the DendSer dendrogram seriation method and the ARc criterion (Earle and Hurley, 2015) for use with seriate().

Usage

register_DendSer()

Details

Registers the method "DendSer" for seriate. DendSer is a fast heuristic for reordering dendrograms developed by Earle and Hurley (2015) able to use different criteria.

control for seriate() with method "DendSer" accepts the following parameters:

• "h" or "method": A dendrogram or a method for hierarchical clustering (see hclust). Default: complete-link.

• "criterion": A seriation criterion to optimize (see list_criterion_methods("dist"). Default: "BAR" (Banded anti-Robinson from with 20% band width).

• "verbose": a logical; print progress information?

• "DendSer_args": additional arguments for DendSer::DendSer().

For convenience, the following methods (for different cost functions) are also provided:

• "DendSer_ARc" (anti-robinson form),

• "DendSer_BAR" (banded anti-Robinson form),

• "DendSer_LPL" (lazy path length),

• "DendSer_PL" (path length).

Note: Package DendSer needs to be installed.

Value

Nothing.

Author(s)

Michael Hahsler based on code by Catherine B. Hurley and Denise Earle

References

D. Earle, C. B. Hurley (2015): Advances in dendrogram seriation for application to visualization. Journal of Computational and Graphical Statistics, 24(1), 1–25.

See Also

DendSer::DendSer()

Other seriation: register_GA(), register_optics(), register_smacof(), register_tsne(), register_umap(), registry_for_seriation_methods, seriate(), seriate_best()

Examples

## Not run: 
register_DendSer()
get_seriation_method("dist", "DendSer")

d <- dist(random.robinson(20, pre=TRUE))

## use Banded AR form with default clustering (complete-link)
o <- seriate(d, "DendSer_BAR")
pimage(d, o)

## use different hclust method (Ward) and AR as the cost function for
## dendrogram reordering
o <- seriate(d, "DendSer", control = list(method = "ward.D2", criterion = "AR"))
pimage(d, o)

## End(Not run)

---

Register a Genetic Algorithm Seriation Method

Description

Register a GA-based seriation metaheuristic for use with seriate().

Usage

register_GA()

gaperm_mixedMutation(ismProb = 0.8)

Arguments

ismProb

probability to use GA::gaperm_ismMutation() (inversion) versus GA::gaperm_simMutation() (simple insertion).

Details

Registers the method "GA" for seriate(). This method can be used to optimize any criterion in package seriation.

The GA uses by default the ordered cross-over (OX) operator. For mutation, the GA uses a mixture of simple insertion and simple inversion operators. This mixed operator is created using seriation::gaperm_mixedMutation(ismProb = .8), where ismProb is the probability that the simple insertion mutation operator is used. See package GA for a description of other available cross-over and mutation operators for permutations. The appropriate operator functions in GA start with gaperm_.

We warm start the GA using "suggestions" given by several heuristics. Set "suggestions" to NA to start with a purely random initial population.

See Example section for available control parameters.

Note: Package GA needs to be installed.

Value

Nothing.

Author(s)

Michael Hahsler

References

Luca Scrucca (2013): GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1–37. URL doi:10.18637/jss.v053.i04.

See Also

Other seriation: register_DendSer(), register_optics(), register_smacof(), register_tsne(), register_umap(), registry_for_seriation_methods, seriate(), seriate_best()

Examples

## Not run: 
register_GA()
get_seriation_method("dist", "GA")

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

## optimize for linear seriation criterion (LS)
o <- seriate(d, "GA", criterion = "LS", verbose = TRUE)
pimage(d, o)

## Note that by default the algorithm is already seeded with a LS heuristic.
## This run is no warm start (no suggestions) and increase run to 100
o <- seriate(d, "GA", criterion = "LS", suggestions = NA, run = 100,
  verbose = TRUE)
pimage(d, o)

o <- seriate(d, "GA", criterion = "LS", suggestions = NA, run = 100,
  verbose = TRUE,  )

pimage(d, o)

## End(Not run)

---

Register Seriation Based on OPTICS

Description

Use ordering points to identify the clustering structure (OPTICS) for seriate().

Usage

register_optics()

Details

Registers the method "optics" for seriate(). This method applies the OPTICS ordering algorithm implemented in dbscan::optics() to create an ordering.

Note: Package dbscan needs to be installed.

Value

Nothing.

References

Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander (1999). OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD international conference on Management of data, ACM Press, pp. 49-60. doi:10.1145/304181.304187

See Also

dbscan::optics().

Other seriation: register_DendSer(), register_GA(), register_smacof(), register_tsne(), register_umap(), registry_for_seriation_methods, seriate(), seriate_best()

Examples

## Not run: 
register_optics()
get_seriation_method("dist", "optics")

d <- dist(random.robinson(50, pre=TRUE, noise=.1))

o <- seriate(d, method = "optics")
pimage(d, o)

## End(Not run)

---

Register Seriation Methods from Package smacof

Description

Registers the "MDS_smacof" method for seriate() based on multidimensional scaling using stress majorization and the corresponding "smacof_stress0" criterion implemented in package smacof (de Leeuw & Mair, 2009).

Usage

register_smacof()

Details

Seriation method "smacof" implements stress majorization with several transformation functions. These functions are passed on as the type control parameter. We default to "ratio", which together with "interval" performs metric MDS. "ordinal" can be used for non-metric MDS. See smacof::smacofSym() for details on the control parameters.

The corresponding criterion called "smacof_stress0" is also registered. There additional parameter type is used to specify the used transformation function. It should agree with the function used for seriation. See smacof::stress0() for details on the stress calculation.

Note: Package smacof needs to be installed.

Value

Nothing.

References

Jan de Leeuw, Patrick Mair (2009). Multidimensional Scaling Using Majorization: SMACOF in R. Journal of Statistical Software, 31(3), 1-30. doi:10.18637/jss.v031.i03

See Also

Other seriation: register_DendSer(), register_GA(), register_optics(), register_tsne(), register_umap(), registry_for_seriation_methods, seriate(), seriate_best()

Examples

## Not run: 
register_smacof()

get_seriation_method("dist", "MDS_smacof")

d <- dist(random.robinson(20, pre = TRUE))

## use Banded AR form with default clustering (complete-link)
o <- seriate(d, "MDS_smacof", verbose = TRUE)
pimage(d, o)

# recalculate stress for the order
MDS_stress(d, o)

# ordinal MDS. stress needs to be calculated using the correct type with stress0
o <- seriate(d, "MDS_smacof", type = "ordinal", verbose = TRUE)
criterion(d, o, method = "smacof_stress0", type = "ordinal")

## End(Not run)

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Register Seriation Based on 1D t-SNE

Description

Use t-distributed stochastic neighbor embedding (t-SNE) for seriate().

Usage

register_tsne()

Details

Registers the method "tsne" for seriate(). This method applies 1D t-SNE to a data matrix or a distance matrix and extracts the order from the 1D embedding. To speed up the process, an initial embedding is created using 1D multi-dimensional scaling (MDS) or principal comonents analysis (PCA) which is improved by t-SNE.

The control parameter "mds" or "pca" controls if MDS (for distances) or PCA (for data matrices) is used to create an initial embedding. See Rtsne::Rtsne() to learn about the other available control parameters.

Perplexity is automatically set as the minimum between 30 and the number of observations. It can be also specified using the control parameter "preplexity".

Note: Package Rtsne needs to be installed.

Value

Nothing.

References

van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605.

See Also

Rtsne::Rtsne()

Other seriation: register_DendSer(), register_GA(), register_optics(), register_smacof(), register_umap(), registry_for_seriation_methods, seriate(), seriate_best()

Examples

## Not run: 
register_tsne()

# distances
get_seriation_method("dist", "tsne")

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

o <- seriate(d, method = "tsne", verbose = TRUE)
pimage(d, o)

# look at the returned configuration and plot it
attr(o[[1]], "configuration")
plot_config(o)

# the t-SNE results are also available as an attribute (see ? Rtsne::Rtsne)
attr(o[[1]], "model")

## matrix
get_seriation_method("matrix", "tsne")

data("Zoo")
x <- Zoo

x[,"legs"] <- (x[,"legs"] > 0)

# t-SNE does not allow duplicates
x <- x[!duplicated(x), , drop = FALSE]

class <- x$class
label <- rownames(x)
x <- as.matrix(x[,-17])

o <- seriate(x, method = "tsne", eta = 10, verbose = TRUE)
pimage(x, o, prop = FALSE, row_labels = TRUE, col_labels = TRUE)

# look at the row embedding
plot_config(o[[1]], col = class)

## End(Not run)

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Register Seriation Based on 1D UMAP

Description

Use uniform manifold approximation and projection (UMAP) to embed the data on the number line and create a order for seriate().

Usage

register_umap()

Details

Registers the method "umap" for seriate(). This method applies 1D UMAP to a data matrix or a distance matrix and extracts the order from the 1D embedding.

Control parameter n_epochs can be increased to find a better embedding.

The returned seriation permutation vector has an attribute named embedding containing the umap embedding.

Note: Package umap needs to be installed.

Value

Nothing.

References

McInnes, L and Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018.

See Also

umap::umap() in umap.

Other seriation: register_DendSer(), register_GA(), register_optics(), register_smacof(), register_tsne(), registry_for_seriation_methods, seriate(), seriate_best()

Examples

## Not run: 
register_umap()

## distances
get_seriation_method("dist", "umap")

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

o <- seriate(d, method = "umap", verbose = TRUE)
pimage(d, o)

# look at the returned embedding and plot it
attr(o[[1]], "configuration")
plot_config(o)

## matrix
get_seriation_method("matrix", "umap")

data("Zoo")
Zoo[,"legs"] <- (Zoo[,"legs"] > 0)
x <- as.matrix(Zoo[,-17])
label <- rownames(Zoo)
class <- Zoo$class

o <- seriate(x, method = "umap", verbose = TRUE)
pimage(x, o)

plot_config(o[[1]], col = class)

## End(Not run)

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