Package 'archetypes' reference manual

Title:	Archetypal Analysis
Description:	The main function archetypes implements a framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm.
Authors:	Manuel J. A. Eugster [aut, cre], Friedrich Leisch [aut], Sohan Seth [ctb]
Maintainer:	Manuel J. A. Eugster <[email protected]>
License:	GPL (>= 2)
Version:	2.2-0.1
Built:	2024-11-20 06:54:06 UTC
Source:	CRAN

Extract method

Description

An extraction on a stepArchetypes object returns again a stepArchetypes object.

Usage

## S3 method for class 'stepArchetypes'
x[i]
## S3 method for class 'stepArchetypes'
x[i]

Arguments

`x`	A `stepArchetypes` object.
`i`	The indizes to extract.

Value

A stepArchetypes object containing only the parts defined in i.

Perform archetypal analysis on a data matrix.

Description

Perform archetypal analysis on a data matrix.

Usage

archetypes(data, k, weights = NULL, maxIterations = 100,
  minImprovement = sqrt(.Machine$double.eps), maxKappa = 1000,
  verbose = FALSE, saveHistory = TRUE,
  family = archetypesFamily("original"), ...)
archetypes(data, k, weights = NULL, maxIterations = 100,
  minImprovement = sqrt(.Machine$double.eps), maxKappa = 1000,
  verbose = FALSE, saveHistory = TRUE,
  family = archetypesFamily("original"), ...)

Arguments

`data`	A numeric $n \times m$ data matrix.
`k`	The number of archetypes.
`weights`	Data weights matrix or vector (used as elements of the diagonal weights matrix).
`maxIterations`	The maximum number of iterations.
`minImprovement`	The minimal value of improvement between two iterations.
`maxKappa`	The limit of kappa to report an ill-ness warning.
`verbose`	Print some details during execution.
`saveHistory`	Save each execution step in an environment for further analyses.
`family`	Blocks defining the underlying problem solving mechanisms; see `archetypesFamily`.
`...`	Additional arguments for family blocks.

Value

An object of class archetypes, see as.archetypes.

References

Cutler and Breiman. Archetypal Analysis. Technometrics, 36(4), 1994. 338-348.

Examples

data(toy)
  a <- archetypes(toy, 3)
data(toy)
  a <- archetypes(toy, 3)

Archetypes family constructor

Description

This function returns a problem solving block for each of the different conceptual parts of the algorithm.

Usage

archetypesFamily(which = c("original", "weighted", "robust"), ...)
archetypesFamily(which = c("original", "weighted", "robust"), ...)

Arguments

`which`	The kind of archetypes family.
`...`	Exchange predefined family blocks with self-defined functions.

Value

A list containing a function for each of the different parts.

Archetypal maps

Description

Two-dimensional projection of the observations based on the alpha coefficients into a space spanned by the (projected) archetypes.

Usage

archmap(object, projection = simplex_projection, projection_args = list(),
  rotate = 0, cex = 1.5, col = 1, pch = 1, xlab = "", ylab = "",
  axes = FALSE, asp = TRUE, ...)
archmap(object, projection = simplex_projection, projection_args = list(),
  rotate = 0, cex = 1.5, col = 1, pch = 1, xlab = "", ylab = "",
  axes = FALSE, asp = TRUE, ...)

Arguments

`object`	An `archetypes` object
`projection`	Projection function; see `archmap_projections`
`projection_args`	Arguments passed to the projection function; see `archmap_projections`
`rotate`	Rotation angle to rotate the projection
`cex`	Character expansion of archetypes
`col`	Color of observations
`pch`	Point character of observations
`xlab`	A label for the x-axis
`ylab`	A label for the y-axis
`axes`	Logical value to draw axes or not
`asp`	The y/x aspect ratio
`...`	Arguments passed to the underlying plot function

Value

Invisible matrix with the projected archetypes

Examples

## Not run: 
  data("skel", package = "archetypes")
  skel2 <- subset(skel, select = -Gender)

  set.seed(1981)
  a <- archetypes(skel2, k = 5)

  ## Simplex projection:
  archmap(a, col = skel$Gender)

  ## Simplex projection with archetypes arranged according to their
  ## distances:
  archmap(a, col = skel$Gender,
          projection = tspsimplex_projection)
  archmap(a, col = skel$Gender,
          projection = tspsimplex_projection,
          projection_args = list(equidist = TRUE))

  ## MDS projection:
  archmap(a, col = skel$Gender,
          projection = atypes_projection)

## End(Not run)
## Not run: 
  data("skel", package = "archetypes")
  skel2 <- subset(skel, select = -Gender)

  set.seed(1981)
  a <- archetypes(skel2, k = 5)

  ## Simplex projection:
  archmap(a, col = skel$Gender)

  ## Simplex projection with archetypes arranged according to their
  ## distances:
  archmap(a, col = skel$Gender,
          projection = tspsimplex_projection)
  archmap(a, col = skel$Gender,
          projection = tspsimplex_projection,
          projection_args = list(equidist = TRUE))

  ## MDS projection:
  archmap(a, col = skel$Gender,
          projection = atypes_projection)

## End(Not run)

Archetypes object constructor

Description

Archetypes object constructor

Usage

as.archetypes(object, k, alphas, rss, iters = NULL, call = NULL,
  history = NULL, kappas = NULL, betas = NULL, zas = NULL,
  family = NULL, familyArgs = NULL, residuals = NULL, weights = NULL,
  reweights = NULL, scaling = NULL)
as.archetypes(object, k, alphas, rss, iters = NULL, call = NULL,
  history = NULL, kappas = NULL, betas = NULL, zas = NULL,
  family = NULL, familyArgs = NULL, residuals = NULL, weights = NULL,
  reweights = NULL, scaling = NULL)

Arguments

`object`	The archetypes; a $p \times m$ matrix, see `parameters`.
`k`	The number of archetypes;
`alphas`	The coefficients; a $n \times p$ matrix, see `coef`.
`rss`	The residual sum of squares; see `rss.archetypes`.
`iters`	The number of iterations to the convergence.
`call`	The call of the `archetypes` function.
`history`	If `saveHistory` set then an environment with the archetypes object for each execution step;
`kappas`	The kappas for each system of linear equations.
`betas`	The data coefficients; a $p \times n$ matrix.
`zas`	The temporary archetypes.
`family`	The archetypes family.
`familyArgs`	Additional arguments for family blocks.
`residuals`	The residuals.
`weights`	The data weights.
`reweights`	The data reweights.
`scaling`	The scaling parameters of the data.

Value

A list with an element for each parameter and class attribute archetypes.

Return best model

Description

Return best model

Usage

## S3 method for class 'stepArchetypes'
bestModel(object, ...)

## S3 method for class 'repArchetypes'
bestModel(object, ...)
## S3 method for class 'stepArchetypes'
bestModel(object, ...)

## S3 method for class 'repArchetypes'
bestModel(object, ...)

Arguments

`object`	An `archetypes` object.
`...`	Ignored

Exploring relationships in body dimensions

Description

Body girth measurements and skeletal diameter measurements, as well as age, weight, height and gender, are given for 507 physically active individuals - 247 men and 260 women.

Usage

bodybody

Format

A data.frame containing 507 observations of 25 variables.

References

Heinz, Peterson, Johnson and Kerk. "Exploring relationships in body dimensions". Journal of Statistics Education, 11(2). http://www.amstat.org/publications/jse/v11n2/datasets.heinz.html

Add lines to an existing parallel coordinates plot.

Description

Add lines to an existing parallel coordinates plot.

Usage

## S3 method for class 'pcplot'
lines(x, data, col = 1, lty = 1, ...)
## S3 method for class 'pcplot'
lines(x, data, col = 1, lty = 1, ...)

Arguments

`x`	A matrix or data frame containing the additional data.
`data`	The data of the existing parallel coordinates plot.
`col`	Line colors.
`lty`	Line types.
`...`	Passed to underlying `matlines`.

Value

Undefined.

Panorma plot for archetypes.

Description

Panorma plot for archetypes.

Usage

## S3 method for class 'archetypes'
panorama(object, data, distfn = distEuclidean,
  xlab = "Index", ylab = "Distance", order = TRUE, col = 1, pch = 1,
  cex = 1, atypes.col = (seq(length = nparameters(object)) + 1),
  atypes.pch = rep(19, nparameters(object)), atypes.cex = rep(1,
  nparameters(object)), ylim = NULL, ...)
## S3 method for class 'archetypes'
panorama(object, data, distfn = distEuclidean,
  xlab = "Index", ylab = "Distance", order = TRUE, col = 1, pch = 1,
  cex = 1, atypes.col = (seq(length = nparameters(object)) + 1),
  atypes.pch = rep(19, nparameters(object)), atypes.cex = rep(1,
  nparameters(object)), ylim = NULL, ...)

Arguments

`object`	An `archetypes`-related object.
`data`	A matrix or data frame.
`distfn`	Distance function.
`xlab`	Label of xaxis.
`ylab`	Label of yaxis.
`order`	Order the distances.
`col`	Color of distances.
`pch`	Plot character of distances.
`cex`	magnification of the distances.
`atypes.col`	Color of archetype distances.
`atypes.pch`	Plot character of archetype distances.
`atypes.cex`	Magnification of the archetype distances.
`ylim`	The y limits of the plot.
`...`	Passed to the underlying `plot` call.

Examples

## Not run: 
  data(toy)
  a <- archetypes(toy, 3)
  panorama(a, toy)

  ## See demo(robust-ozone).
  
## End(Not run)
## Not run: 
  data(toy)
  a <- archetypes(toy, 3)
  panorama(a, toy)

  ## See demo(robust-ozone).
  
## End(Not run)

Parallel coordinates of data and archetypes.

Description

Parallel coordinates of data and archetypes.

Usage

## S3 method for class 'archetypes'
pcplot(x, data, data.col = gray(0.7), data.lwd = 1,
  atypes.col = 2, atypes.lwd = 2, atypes.lty = 1, chull = NULL,
  chull.col = 1, chull.lwd = 2, chull.lty = 1, ...)
## S3 method for class 'archetypes'
pcplot(x, data, data.col = gray(0.7), data.lwd = 1,
  atypes.col = 2, atypes.lwd = 2, atypes.lty = 1, chull = NULL,
  chull.col = 1, chull.lwd = 2, chull.lty = 1, ...)

Arguments

`x`	An `archetypes` object.
`data`	A matrix or data frame.
`data.col`	Color of data lines.
`data.lwd`	Width of data lines.
`atypes.col`	Color of archetypes lines.
`atypes.lwd`	Width of archetypes lines.
`atypes.lty`	Type of archetypes lines.
`chull`	An integer vector giving the indices of the points from `data` lying on the convex hull.
`chull.col`	Color of convex hull lines.
`chull.lwd`	Width of convex hull lines.
`chull.lty`	Type of convex hull lines.
`...`	Passed to `pcplot` and `lines.pcplot`.

Value

Undefined.

Default parallel coordinates plot.

Description

Code copied from function parcoord of package MASS to simply play arround with the visualization of archetypes. At a later date, when it is clear which visualisation is the best, the functionality is probabibly merged with the original function or it is possible with parallel coordinate plots which are available et all.

Usage

## Default S3 method:
pcplot(x, col = gray(0.7), lty = 1, var.label = TRUE,
  rx = NULL, ...)
## Default S3 method:
pcplot(x, col = gray(0.7), lty = 1, var.label = TRUE,
  rx = NULL, ...)

Arguments

`x`	A $n \times m$ matrix or data frame who columns represent variables. Missing values are allowed.
`col`	Line color.
`lty`	Line type.
`var.label`	Axes labels.
`rx`	A $2 \times m$ matrix with ranges for each dimension.
`...`	Passed to the underlying `matplot` function.

Value

Undefined.

Predict method for archetypal analysis fits

Description

This method produces predicted alpha coefficients for new data.

Usage

## S3 method for class 'archetypes'
predict(object, newdata, ...)
## S3 method for class 'archetypes'
predict(object, newdata, ...)

Arguments

`object`	An `archetypes` object; currently only `original`-family objects.
`newdata`	A data frame with data for which to predict the alpha coefficients.
`...`	Ignored.

Value

The predict alpha coefficients.

Return residuals

Description

Return residuals

Usage

## S3 method for class 'archetypes'
residuals(object, ...)
## S3 method for class 'archetypes'
residuals(object, ...)

Arguments

`object`	An `archetypes` object.
`...`	Ignored.

Value

Matrix with residuals.

Robust archetypes

Description

Robust archetypes

Usage

robustArchetypes(data, k, familyBlocks = list(), ...)
robustArchetypes(data, k, familyBlocks = list(), ...)

Arguments

`familyBlocks`	Exchange predefined family blocks; see `archetypesFamily`.
`data`	A numeric $n \times m$ data matrix.
`k`	The number of archetypes.
`...`	Additional arguments for family blocks.

Value

An object of class robustArchetypes and as.archetypes.

Defined generics

Description

Generics defined by the archetypes package.

Return number of parameters

Return best model

Panorama

Parallel coordinates plot

Usage

rss(object, ...)

nparameters(object, ...)

bestModel(object, ...)

panorama(object, ...)

pcplot(x, ...)
rss(object, ...)

nparameters(object, ...)

bestModel(object, ...)

panorama(object, ...)

pcplot(x, ...)

Arguments

`object`	An object
`...`	Futher arguments
`x`	An object.

Return residual sum of squares

Description

Return residual sum of squares

Usage

## S3 method for class 'archetypes'
rss(object, type = c("scaled", "single", "global"), ...)

## S3 method for class 'stepArchetypes'
rss(object, ...)

## S3 method for class 'repArchetypes'
rss(object, ...)
## S3 method for class 'archetypes'
rss(object, type = c("scaled", "single", "global"), ...)

## S3 method for class 'stepArchetypes'
rss(object, ...)

## S3 method for class 'repArchetypes'
rss(object, ...)

Arguments

`object`	An `archetypes` object.
`type`	Return scaled, single or global RSS.
`...`	Ignored.

Value

Residual sum of squares.

Screeplot of stepArchetypes.

Description

Screeplot draws the residual sum of square curve based on the best model of each step.

Usage

## S3 method for class 'stepArchetypes'
screeplot(x, type = c("lines", "barplot"), ...)
## S3 method for class 'stepArchetypes'
screeplot(x, type = c("lines", "barplot"), ...)

Arguments

`x`	A `stepArchetypes` object.
`type`	Draw lines or a barplot.
`...`	Passed to underlying plot functions.

Value

Undefined.

Archetypal map projections

Description

Archetypal map projections

Usage

simplex_projection(x, r = 10)

tspsimplex_projection(x, r = 10, equidist = FALSE, ...)

atypes_projection(x)
simplex_projection(x, r = 10)

tspsimplex_projection(x, r = 10, equidist = FALSE, ...)

atypes_projection(x)

Arguments

`x`	Archetypes matrix
`r`	Radius of the simplex projection
`equidist`	Arrange archetypes equidistantly or in relation to their distance
`...`	Parameters for the `solve_TSP` function

Value

Matrix with the projected archetypes

Simplex visualization

Description

The stochastic nature of the alpha coefficients implies that they exist on a standard (K-1)-simplex with the K archetypes Z as the corners, and the coefficients as the coordinate with respect to these corners. A standard simplex can be projected to two dimensions via a skew orthogonal projection, where all the vertices of the simplex are shown on a circle connected by edges. The individual alpha coefficients can be then projected into this circle.

Usage

simplexplot(object, radius = 10, order = NULL, labels_cex = 1,
  labels = NULL, show_labels = TRUE, points_col = "#00000044",
  points_pch = 19, points_cex = 1, projection = simplex_projection,
  show_points = TRUE, show_circle = TRUE, circle_col = "lightgray",
  show_edges = TRUE, edges_col = "lightgray", show_direction = FALSE,
  direction_length = 1, directions_col = points_col, ...)
simplexplot(object, radius = 10, order = NULL, labels_cex = 1,
  labels = NULL, show_labels = TRUE, points_col = "#00000044",
  points_pch = 19, points_cex = 1, projection = simplex_projection,
  show_points = TRUE, show_circle = TRUE, circle_col = "lightgray",
  show_edges = TRUE, edges_col = "lightgray", show_direction = FALSE,
  direction_length = 1, directions_col = points_col, ...)

Arguments

`object`	An `archetypes` object
`radius`	Radius of the projection
`order`	Order of the archetypes
`labels_cex`	Label expansion
`labels`	Labels
`show_labels`	Show labels
`points_col`	Color of the points
`points_pch`	Plot character of the points
`points_cex`	Character expansion of the points
`projection`	Projection function; see `archmap_projections`
`show_points`	Show the points
`show_circle`	Show the circle
`circle_col`	Color of the circle
`show_edges`	Show the edges
`edges_col`	Color of the edges
`direction_length`	Expansion of the direction pointers
`directions_col`	Color of the direction pointers
`show_direction`	Show direction pointers
`...`	Additional arguments; currently ignored

Value

Invisible list of all computed components needed for the simplex visualization.

References

See Section 6 in "Probabilistic Archetypal Analysis" by Seth and Eugster (2014), http://arxiv.org/abs/1312.7604.

Examples

### This example reproduces parts of the Figure 7 shown in
  ### "Probabilistic Archetypal Analysis" by Seth and Eugster (2014)

  data("toy", package = "archetypes")

  suppressWarnings(RNGversion("3.5.0"))
  set.seed(1234); a3 <- archetypes(toy, k = 3)
  set.seed(1237); a4 <- archetypes(toy, k = 4)
  set.seed(1238); a5 <- archetypes(toy, k = 5)

  simplexplot(a3)
  simplexplot(a3, show_direction = TRUE, show_points = FALSE)
  simplexplot(a4, projection = tspsimplex_projection)
  simplexplot(a5, show_direction = TRUE, show_points = FALSE,
    direction_length = 2, directions_col = "black")
### This example reproduces parts of the Figure 7 shown in
  ### "Probabilistic Archetypal Analysis" by Seth and Eugster (2014)

  data("toy", package = "archetypes")

  suppressWarnings(RNGversion("3.5.0"))
  set.seed(1234); a3 <- archetypes(toy, k = 3)
  set.seed(1237); a4 <- archetypes(toy, k = 4)
  set.seed(1238); a5 <- archetypes(toy, k = 5)

  simplexplot(a3)
  simplexplot(a3, show_direction = TRUE, show_points = FALSE)
  simplexplot(a4, projection = tspsimplex_projection)
  simplexplot(a5, show_direction = TRUE, show_points = FALSE,
    direction_length = 2, directions_col = "black")

Exploring relationships in body dimensions, skeletal measurements

Description

Skeletal diameter measurements, as well as height and gender, are given for 507 physically active individuals - 247 men and 260 women.

This is a subset of the body data set.

Usage

skelskel

Format

A data.frame containing 507 observations of 11 variables.

References

Heinz, Peterson, Johnson and Kerk. "Exploring relationships in body dimensions". Journal of Statistics Education, 11(2). http://www.amstat.org/publications/jse/v11n2/datasets.heinz.html

Skeleton plot.

Description

Displays a schematic representation of skeleton data as available in dataset skel.

Displays a generic skeleton with annotations explaining the measurements available in data set skel.

Usage

skeletonplot(x, skel.width = 100, skel.height = 200, ylab = "Height (cm)",
  base.radius = 2, xlab = "", xlim = (nrow(x) * c(0, skel.width)),
  ylim = c(0, skel.height), col = NULL, mtext = TRUE, skel.lwd = 1, ...)

jd()
skeletonplot(x, skel.width = 100, skel.height = 200, ylab = "Height (cm)",
  base.radius = 2, xlab = "", xlim = (nrow(x) * c(0, skel.width)),
  ylim = c(0, skel.height), col = NULL, mtext = TRUE, skel.lwd = 1, ...)

jd()

Arguments

`x`	Matrix or data.frame of skeleton data.
`skel.width`	Reference width for instance calculation.
`skel.height`	Reference height for instance calculation.
`base.radius`	Base radius for points.
`xlab`	The x label of the plot.
`ylab`	The y label of the plot.
`xlim`	Numeric of length 2 giving the x limits for the plot.
`ylim`	Numeric of length 2 giving the y limits for the plot.
`col`	Color of the different parts of the skeleton.
`mtext`	Label archetypes.
`skel.lwd`	Line width of skeleton.
`...`	Passed to underlying canvas plot function.

Value

List of skeleton instances.

Generic skeleton instance.

Run archetypes algorithm repeatedly

Description

Run archetypes algorithm repeatedly

Usage

stepArchetypes(..., k, nrep = 3, method = archetypes, verbose = TRUE)
stepArchetypes(..., k, nrep = 3, method = archetypes, verbose = TRUE)

Arguments

`...`	Passed to the specific archetype function.
`k`	A vector of integers passed in turn to the k argument of `archetypes`.
`nrep`	For each value of `k` run `archetypes` `nrep` times.
`method`	Archetypes function to use, typically `archetypes`, `weightedArchetypes` or `robustArchetypes`,
`verbose`	Show progress during exection.

Value

A list with k elements and class attribute stepArchetypes. Each element is a list of class repArchetypes with nrep elements; only for internal usage.

Examples

## Not run: 
  data(skel)
  skel2 <- subset(skel, select=-Gender)
  as <- stepArchetypes(skel2, k=1:5, verbose=FALSE)

  ## Residual sum of squares curve:
  screeplot(as)

  ## Select three archetypes and from that the best
  ## recurrence:
  a3 <- bestModel(as[[3]])
  
## End(Not run)
## Not run: 
  data(skel)
  skel2 <- subset(skel, select=-Gender)
  as <- stepArchetypes(skel2, k=1:5, verbose=FALSE)

  ## Residual sum of squares curve:
  screeplot(as)

  ## Select three archetypes and from that the best
  ## recurrence:
  a3 <- bestModel(as[[3]])
  
## End(Not run)

Summary method for stepArchetypes object

Description

Summary method for stepArchetypes object

Usage

## S3 method for class 'stepArchetypes'
summary(object, ...)
## S3 method for class 'stepArchetypes'
summary(object, ...)

Arguments

`object`	A `stepArchetypes` object.
`...`	Ignored.

Value

Undefined.

Toy data set

Description

A simple artificial two-dimensional data set.

Usage

toytoy

Format

A data.frame containing 250 observations of 2 variables.

Weighted archetypes

Description

Weighted archetypes

Usage

weightedArchetypes(data, k, weights = NULL, familyBlocks = list(), ...)
weightedArchetypes(data, k, weights = NULL, familyBlocks = list(), ...)

Arguments

`weights`	Data weights matrix.
`familyBlocks`	Exchange predefined family blocks; see `archetypesFamily`.
`data`	A numeric $n \times m$ data matrix.
`k`	The number of archetypes.
`...`	Additional arguments for family blocks.

Value

An object of class weightedArchetypes and as.archetypes.

Return weights

Description

Return weights

Usage

## S3 method for class 'archetypes'
weights(object, type = c("weights", "reweights"), ...)
## S3 method for class 'archetypes'
weights(object, type = c("weights", "reweights"), ...)

Arguments

`object`	An `archetypes` object.
`type`	Return global weights (weighted archetypes) or weights calculated during the iterations (robust archetypes).
`...`	Ignored.

Value

Vector of weights.

Two-dimensional plot.

Description

Two-dimensional plot.

Usage

xyplot(x, ...)
xyplot(x, ...)

Arguments

`x`	An object.
`...`	Further arguments.

Value

Undefined.

Plot of two-dimensional data and archetypes.

Description

Plot of two-dimensional data and archetypes.

Usage

## S3 method for class 'archetypes'
xyplot(x, y, data.col = 1, data.pch = 19,
  data.bg = NULL, atypes.col = 2, atypes.pch = 19, ahull.show = TRUE,
  ahull.col = atypes.col, chull = NULL, chull.col = gray(0.7),
  chull.pch = 19, adata.show = FALSE, adata.col = 3, adata.pch = 13,
  link.col = data.col, link.lty = 1, ...)
## S3 method for class 'archetypes'
xyplot(x, y, data.col = 1, data.pch = 19,
  data.bg = NULL, atypes.col = 2, atypes.pch = 19, ahull.show = TRUE,
  ahull.col = atypes.col, chull = NULL, chull.col = gray(0.7),
  chull.pch = 19, adata.show = FALSE, adata.col = 3, adata.pch = 13,
  link.col = data.col, link.lty = 1, ...)

Arguments

`x`	An `archetypes` object.
`y`	A matrix or data frame.
`data.col`	Color of data points.
`data.pch`	Type of data points.
`data.bg`	Background of data points.
`atypes.col`	Color of archetypes points.
`atypes.pch`	Type of archetypes points.
`ahull.show`	Show approximated convex hull.
`ahull.col`	Color of approximated convex hull line.
`chull`	An integer vector giving the indices of the points from `data` lying on the convex hull.
`chull.col`	Color of convex hull points.
`chull.pch`	Type of convex hull points.
`adata.show`	Show approximated data with link to the original data.
`adata.col`	Color of approximated data points.
`adata.pch`	Type of approximated data points.
`link.col`	Color of link between approximated and original data points.
`link.lty`	Line type of link between approximated and original data points.
`...`	Passed to the underlying plot functions.

Value

Undefined.

Note

The link between approximated and original data is based on an idea and Matlab source code of Bernard Pailthorpe.

Plot of two-dimensional data and robust archetypes.

Description

Plot of two-dimensional data and robust archetypes.

Usage

## S3 method for class 'robustArchetypes'
xyplot(x, y, ...)
## S3 method for class 'robustArchetypes'
xyplot(x, y, ...)

Arguments

`x`	An `archetypes` object.
`y`	A matrix or data frame.
`...`	Arguments of `xyplot.weightedArchetypes` and `xyplot.robustArchetypes`

Plot of two-dimensional data and stepArchetypes.

Description

Plot of two-dimensional data and stepArchetypes.

Usage

## S3 method for class 'stepArchetypes'
xyplot(x, y, data.col = gray(0.7), data.pch = 19,
  atypes.col = (seq_len(length(x) * length(x[[1]])) + 1), atypes.pch = 19,
  ahull.show = TRUE, ahull.col = atypes.col, ...)
## S3 method for class 'stepArchetypes'
xyplot(x, y, data.col = gray(0.7), data.pch = 19,
  atypes.col = (seq_len(length(x) * length(x[[1]])) + 1), atypes.pch = 19,
  ahull.show = TRUE, ahull.col = atypes.col, ...)

Arguments

`x`	An `stepArchetypes` object.
`y`	A matrix or data frame.
`data.col`	Color of data points.
`data.pch`	Type of data points.
`atypes.col`	Color of archetypes points.
`atypes.pch`	Type of archetypes points.
`ahull.show`	Show approximated convex hull.
`ahull.col`	Color of approximated convex hull line.
`...`	Passed to the underlying plot functions.

Value

Undefined.

Plot of two-dimensional data and weighted archetypes.

Description

Plot of two-dimensional data and weighted archetypes.

Usage

## S3 method for class 'weightedArchetypes'
xyplot(x, y, data.col = 1, data.pch = 21,
  data.bg = gray, link.col = NULL, link.lty = NULL,
  weights.type = "weights", ...)
## S3 method for class 'weightedArchetypes'
xyplot(x, y, data.col = 1, data.pch = 21,
  data.bg = gray, link.col = NULL, link.lty = NULL,
  weights.type = "weights", ...)

Arguments

`x`	An `archetypes` object.
`y`	A matrix or data frame.
`data.col`	Color of data points.
`data.pch`	Type of data points.
`data.bg`	Background of data points.
`link.col`	Color of link between approximated and original data points.
`link.lty`	Line type of link between approximated and original data points.
`weights.type`	Weights to display; see `weights.archetypes`.
`...`	Arguments of `xyplot.archetypes`.

`height`	An `archetypes` object.
`data`	The original data matrix.
`which`	`below` creates a barplot for each archetype, `beside` creates one barplot with bars side by side.
`which.beside`	Barplot according to `atypes` or `variables`.
`which.below`	`compressed` plots the labels only once.
`percentiles`	Show real values or percentile profiles.
`below.compressed.height`	Height of additional tail subplot.
`below.compressed.srt`	Rotations of the x-labels.
`col.atypes`	Color of archetypes; only used in `below.compressed`.
`...`	Passed to the underlying `barplot` call.

`zs`	An `archetypes` object.
`data`	The data matrix.
`show`	Show archetypes or approximated data.
`ssleep`	Seconds to sleep before start.
`bsleep`	Seconds to sleep between each plot.
`postfn`	Post plot function; is called in each iteration after the plot call.
`rwdata.col1`	If `show = 'rwdata'`: color of base data set.
`rwdata.col2`	If `show = 'rwdata'`: color of weighted data set.
`...`	Passed to underlying plot functions.
`zas.col`	Color of the intermediate archetypes.
`zas.pch`	Type of the intermediate archetypes points.
`old.col`	Color of the archetypes on step further.

Package 'archetypes'

Help Index

Extract method

Description

Usage

Arguments

Value

Perform archetypal analysis on a data matrix.

Description

Usage

Arguments

Value

References

See Also

Examples

Archetypes family constructor

Description

Usage

Arguments

Value

See Also

Archetypal maps

Description

Usage

Arguments

Value

See Also

Examples

Archetypes object constructor

Description

Usage

Arguments

Value

See Also

Barplot of archetypes.

Description

Usage

Arguments

Value

Return best model

Description

Usage

Arguments

Exploring relationships in body dimensions

Description

Usage

Format

References

See Also

Return coefficients

Description

Usage

Arguments

Value

Return fitted data

Description

Usage

Arguments

Value

Return kappa

Description

Usage

Arguments

Value

Add lines to an existing parallel coordinates plot.

Description

Usage

Arguments

Value

Archetypes movies.

Description

Usage

Arguments

Value

Return number of archetypes

Description

Usage

Arguments

Value

Panorma plot for archetypes.