| Title: | Mathematical Morphology in Any Number of Dimensions |
|---|---|
| Description: | Provides tools for performing mathematical morphology operations, such as erosion and dilation, on data of arbitrary dimensionality. Can also be used for finding connected components, resampling, filtering, smoothing and other image processing-style operations. |
| Authors: | Jon Clayden |
| Maintainer: | Jon Clayden <[email protected]> |
| License: | GPL-2 |
| Version: | 1.6.3 |
| Built: | 2024-09-14 04:22:15 UTC |
| Source: | https://github.com/jonclayden/mmand |
This function binarises an array, setting all nonzero elements to unity.
binarise(x)binarise(x)
x |
An object that can be coerced to an array, or for which a
|
A morphed array with the same dimensions as the original array.
Jon Clayden <[email protected]>
morph for the function underlying this operation, and
erode for mathematical morphology functions.
This function checks whether a numeric array is binary, with only one unique nonzero value, or not.
binary(x)binary(x)
x |
An object that can be coerced to a numeric array. |
A logical value indicating whether the array is binary or not. Binary in this case means that the array contains only one unique nonzero value, which is stored with the return value in an attribute.
Jon Clayden <[email protected]>
The components function finds connected components in a numeric
array. The kernel determines which neighbours are considered connected (e.g.
including or excluding diagonal neighbours), and will usually have width 3
in each dimension.
components(x, kernel, ...) ## Default S3 method: components(x, kernel, ...)components(x, kernel, ...) ## Default S3 method: components(x, kernel, ...)
x |
Any object. For the default method, this must be coercible to an array. |
kernel |
An object representing the kernel to be used, which must be
coercible to an array. It must have odd width in all dimensions, but does
not have to be isotropic in size. The kernel's dimensionality may be less
than that of the target array, |
... |
Additional arguments to methods. |
An array of the same dimension as the original, whose integer-valued elements identify the component to which each element in the array belongs. Zero values in the original array will result in NAs.
Jon Clayden <[email protected]>
kernels for kernel-generating functions.
x <- c(0,0,1,0,0,0,1,1,1,0,0) k <- c(1,1,1) components(x,k)x <- c(0,0,1,0,0,0,1,1,1,0,0) k <- c(1,1,1) components(x,k)
This function displays a 2D greyscale or RGB colour image. It is a wrapper
around image, with more sensible defaults for images. It is (S3)
generic. A method for 3D arrays is provided, which assumes that the third
dimension corresponds to channel (grey/alpha for two channels, red/green/
blue for three, red/green/blue/alpha for four).
display(x, ...) ## Default S3 method: display(x, transpose = TRUE, useRaster = TRUE, add = FALSE, col = grey(0:255/255), ...) ## S3 method for class 'matrix' display(x, ...) ## S3 method for class 'array' display(x, max = NULL, ...)display(x, ...) ## Default S3 method: display(x, transpose = TRUE, useRaster = TRUE, add = FALSE, col = grey(0:255/255), ...) ## S3 method for class 'matrix' display(x, ...) ## S3 method for class 'array' display(x, max = NULL, ...)
x |
An R object. For the default method, it must be coercible to a numeric matrix. |
... |
Additional arguments to |
transpose |
Whether to transpose the matrix before display. This is
usually necessary due to the conventions of |
useRaster |
Whether to use raster graphics if possible. This is
generally preferred for speed. Passed to |
add |
Whether to add the image to an existing plot. If |
col |
The colour scale to use. The default is 256 grey levels. The array method overrides this appropriately. |
max |
The maximum colour value for each channel. If |
Relative to the defaults for image (from the graphics
package), this function transposes and then inverts the matrix along the
y-direction, uses a grey colour scale, fills the entire device with the
image, and tries to size the image correctly given the dot pitch of the
display. Unfortunately the latter is not always possible, due to downstream
limitations.
If x has attributes "range", "background", "asp"
or "dpi", these are respected.
This function is called for its side-effect of displaying an image on a new R device.
Jon Clayden <[email protected]>
The Euclidean distance transform produces an array like its argument, but with element values representing the Euclidean distance to the nearest nonzero element. The input is treated as logically binary, with all nonzero values treated as "on", and all zeroes as "off".
distanceTransform(x, ...) ## Default S3 method: distanceTransform(x, pixdim = TRUE, signed = FALSE, threads = getOption("mmand.threads"), ...)distanceTransform(x, ...) ## Default S3 method: distanceTransform(x, pixdim = TRUE, signed = FALSE, threads = getOption("mmand.threads"), ...)
x |
Any object. For the default method, this must be coercible to an array. |
... |
Additional arguments to methods. |
pixdim |
An optional numeric vector or logical value. In the former
case it will be taken as giving the physical size of the array elements of
|
signed |
Logical value. If |
threads |
If a positive integer, and the package is compiled with OpenMP support, the number of threads to use during the calculation. |
An array of the same dimension as the original, whose elements give the Euclidean distance from that element to the nearest "on" element in the original.
Jon Clayden <[email protected]>
This implementation is based on the "marching parabolas" algorithm described by Felzenszwalb and Huttenlocher in the paper below.
P.F. Felzenszwalb & D.P. Huttenlocher (2012). Distance transforms of sampled functions. Theory of Computing 8(19):415-428.
x <- c(0,0,1,0,0,0,1,1,1,0,0) distanceTransform(x) distanceTransform(x, pixdim=2)x <- c(0,0,1,0,0,0,1,1,1,0,0) distanceTransform(x) distanceTransform(x, pixdim=2)
These functions provide standard mathematical morphology operations, which can be applied to array data with any number of dimensions. Binary and greyscale morphology is supported.
erode(x, kernel) dilate(x, kernel) opening(x, kernel) closing(x, kernel)erode(x, kernel) dilate(x, kernel) opening(x, kernel) closing(x, kernel)
x |
An object that can be coerced to an array, or for which a
|
kernel |
An array representing the kernel to be used. See
|
The erode function uses the kernel as an eraser, centring it on each
zero-valued pixel, which has the effect of eroding the extent of nonzero
areas. Dilation has the opposite effect, extending the nonzero regions in
the array. Opening is an erosion followed by a dilation, and closing is a
dilation followed by an erosion, using the same kernel in both cases.
If the kernel has only one unique nonzero value, it is described as
“flat”. For a flat kernel, the erosion is the minimum value of x
within the nonzero region of kernel. For a nonflat kernel, this
becomes the minimum value of x - kernel. Dilation is the opposite
operation, taking the maximum within the kernel.
A morphed array with the same dimensions as the original array.
Jon Clayden <[email protected]>
morph for the function underlying all of these
operations, kernels for kernel-generating functions,
binarise for binarising an array, and
gaussianSmooth for smoothing. The EBImage
Bioconductor package also supplies functions to perform these operations,
and may be slightly faster, but only works in two dimensions.
x <- c(0,0,1,0,0,0,1,1,1,0,0) k <- c(1,1,1) erode(x,k) dilate(x,k)x <- c(0,0,1,0,0,0,1,1,1,0,0) k <- c(1,1,1) erode(x,k) dilate(x,k)
An implementation of Conway's Game of Life, a classical cellular automaton,
using the morph function. The gosperGliderGun
function provides an interesting starting configuration.
gameOfLife(init, size, density = 0.3, steps = 200, viz = FALSE, tick = 0.5) gosperGliderGun()gameOfLife(init, size, density = 0.3, steps = 200, viz = FALSE, tick = 0.5) gosperGliderGun()
init |
The initial state of the automaton, a binary matrix. If missing,
the initial state will be randomly generated, with a population density
given by |
size |
The dimensions of the board. Defaults to the size of
|
density |
The approximate population density of the starting state.
Ignored if |
steps |
The number of generations of the automaton to simulate. |
viz |
If |
tick |
The amount of time, in seconds, to pause before plotting each
successive generation. Ignored if |
Conway's Game of Life is a simple cellular automaton, based on a 2D matrix of “cells”. It shows complex behaviour based on four simple rules. These are:
Any live cell with fewer than two live neighbours dies, as if caused by under-population.
Any live cell with two or three live neighbours lives on to the next generation.
Any live cell with more than three live neighbours dies, as if by overcrowding.
Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.
Live and dead cells are represented by 1s and 0s in the matrix, respectively.
The initial state and the rules above completely determine the behaviour of the system. The Gosper glider gun is an interesting starting configuration that generates so-called “gliders”, which propagate across the board.
In principle the size of the board in a cellular automaton is infinite. Of course this is not easy to simulate, but this implementation adds a border of two extra cells around the board on all sides to approximate an infinite board slightly better. These are not visualised, nor returned in the final state.
A binary matrix representing the final state of the system after
steps generations.
Jon Clayden <[email protected]>
The morph function, which powers this simulation.
## Not run: gameOfLife(init=gosperGliderGun(), size=c(40,40), steps=50, viz=TRUE)## Not run: gameOfLife(init=gosperGliderGun(), size=c(40,40), steps=50, viz=TRUE)
This function smoothes an array using a Gaussian kernel with a specified standard deviation.
gaussianSmooth(x, sigma)gaussianSmooth(x, sigma)
x |
An object that can be coerced to an array, or for which a
|
sigma |
A numeric vector giving the standard deviation of the kernel in each dimension. Can have lower dimensionality than the target array. |
This implementation takes advantage of the separability of the Gaussian kernel for speed when working in multiple dimensions. It is therefore equivalent to, but much faster than, directly applying a multidimensional kernel.
A morphed array with the same dimensions as the original array.
Jon Clayden <[email protected]>
morph for the function underlying this operation,
gaussianKernel for generating Gaussian kernels (which is
also used by this function), and erode for mathematical
morphology functions.
These functions can be used to generate kernels for morphological, smoothing
or resampling operations. There are two types of kernels: kernel arrays,
which are used with morph, and kernel functions, which are
used with resample.
isKernel(object) isKernelArray(object) isKernelFunction(object) kernelArray(values) shapeKernel(width, dim = length(width), type = c("box", "disc", "diamond"), binary = TRUE, normalised = FALSE) gaussianKernel(sigma, dim = length(sigma), size = 6 * sigma, normalised = TRUE) sobelKernel(dim, axis = 1) kernelFunction(name = c("box", "triangle", "mitchell-netravali", "lanczos"), ...) boxKernel() triangleKernel() mitchellNetravaliKernel(B = 1/3, C = 1/3) mnKernel(B = 1/3, C = 1/3) lanczosKernel()isKernel(object) isKernelArray(object) isKernelFunction(object) kernelArray(values) shapeKernel(width, dim = length(width), type = c("box", "disc", "diamond"), binary = TRUE, normalised = FALSE) gaussianKernel(sigma, dim = length(sigma), size = 6 * sigma, normalised = TRUE) sobelKernel(dim, axis = 1) kernelFunction(name = c("box", "triangle", "mitchell-netravali", "lanczos"), ...) boxKernel() triangleKernel() mitchellNetravaliKernel(B = 1/3, C = 1/3) mnKernel(B = 1/3, C = 1/3) lanczosKernel()
object |
Any object. |
values |
A numeric vector or array, containing the values of the kernel array. |
width |
A numeric vector giving the width of the shape in each
dimension, in array elements. Does not need to be integer-valued, or equal
for all dimensions. Will be recycled to length |
dim |
An integer value giving the dimensionality of the kernel.
Defaults to the length of |
type |
A string giving the type of shape to produce. In one dimension, these shapes are all equivalent. |
binary |
If |
normalised |
If |
sigma |
A numeric vector giving the standard deviation of the
underlying Gaussian distribution in each dimension, in array elements.
Does not need to be equal for all dimensions. Will be recycled to length
|
size |
A numeric vector giving the width of the kernel in each
dimension, which will be rounded up to the nearest odd integer. Defaults
to four times the corresponding |
axis |
The axis along which the gradient operator will be applied. |
name |
A string giving the name of the kernel function required. |
... |
Parameters for the kernel function. |
B, C
|
Mitchell-Netravali coefficients, each of which must be between 0 and 1. |
There are two forms of kernel used by this package. Kernel arrays, otherwise
known in mathematical morphology as structuring elements, are numeric arrays
with class kernelArray. They are defined on a grid of odd width, and
are used by morph and related functions. Kernel functions, by
contrast, are represented in R as a list containing a name and, optionally,
some parameters. The real implementation is in C++. They are defined
everywhere within the support of the kernel, and are used by
resample and friends. The key distinction is in whether the
kernel will always be centred exactly on the location of an existing value
in the data (for kernel arrays) or not (for kernel functions).
The kernelArray and kernelFunction functions create objects of
the corresponding classes, while isKernelArray and
isKernelFunction test for them. In addition, isKernel returns
TRUE if its argument is of either kernel class.
The remaining functions generate special-case kernels: shapeKernel
generates arrays with nonzero elements in a box, disc or diamond shape for
use with morphology functions; gaussianKernel generates
Gaussian coefficients and is used by gaussianSmooth;
sobelKernel generates the Sobel-Feldman gradient operator, for use by
sobelFilter; boxKernel is used for “nearest
neighbour” resampling, and triangleKernel for linear, bilinear, etc.
The Mitchell-Netravali kernel, a.k.a. BC-spline, is based on a family of
piecewise-cubic polynomial functions, with support of four times the pixel
separation in each dimension. The default parameters are the ones
recommended by Mitchell and Netravali as a good trade-off between various
artefacts, but other well-known special cases include B=1, C=0 (the cubic
B-spline) and B=0, C=0.5 (the Catmull-Rom spline). mnKernel is a
shorter alias for mitchellNetravaliKernel. Finally, the Lanczos
kernel is a five-lobe windowed sinc function.
For isKernel, isKernelArray and
isKernelFunction, a logical value. For kernelArray,
shapeKernel, gaussianKernel and sobelKernel, a kernel
array. For kernelFunction, boxKernel, triangleKernel,
mitchellNetravaliKernel and mnKernel, a kernel function.
Jon Clayden <[email protected]>
The Mitchell-Netravali kernel is described in the following paper.
D.P. Mitchell & A.N. Netravali (1988). Reconstruction filters in computer graphics. Computer Graphics 22(4):221-228.
morph for general application of kernel arrays to
data, morphology for mathematical morphology functions,
resample for resampling, and gaussianSmooth
for smoothing. Also see sampleKernelFunction for kernel
sampling and plotting.
shapeKernel(c(3,5), type="diamond") gaussianKernel(c(0.3,0.3)) mnKernel()shapeKernel(c(3,5), type="diamond") gaussianKernel(c(0.3,0.3)) mnKernel()
These functions apply mean, median or Sobel filters to an array.
meanFilter(x, kernel) medianFilter(x, kernel) sobelFilter(x, dim, axis = 0)meanFilter(x, kernel) medianFilter(x, kernel) sobelFilter(x, dim, axis = 0)
x |
An object that can be coerced to an array, or for which a
|
kernel |
A kernel array, indicating the scope of the filter. |
dim |
For |
axis |
For |
A morphed array with the same dimensions as the original array.
Jon Clayden <[email protected]>
morph for the function underlying these operations,
and kernels for kernel-generating functions.
The morph function applies a kernel to a target array. Optionally,
applying the kernel to a particular array element can be made conditional on
its value, or the number of nonzero immediate neighbours that it has. The
morph function is (S3) generic.
morph(x, kernel, ...) ## Default S3 method: morph(x, kernel, operator = c("+", "-", "*", "i", "1", "0", "=="), merge = c("sum", "min", "max", "mean", "median", "all", "any"), value = NULL, valueNot = NULL, nNeighbours = NULL, nNeighboursNot = NULL, renormalise = TRUE, ...)morph(x, kernel, ...) ## Default S3 method: morph(x, kernel, operator = c("+", "-", "*", "i", "1", "0", "=="), merge = c("sum", "min", "max", "mean", "median", "all", "any"), value = NULL, valueNot = NULL, nNeighbours = NULL, nNeighboursNot = NULL, renormalise = TRUE, ...)
x |
Any object. For the default method, this must be coercible to an array. |
kernel |
An object representing the kernel to be applied, which must be
coercible to an array. It must have odd width in all dimensions, but does
not have to be isotropic in size. The kernel's dimensionality may be less
than that of the target array, |
... |
Additional arguments to methods. |
operator |
The operator applied elementwise within the kernel, as a
function of the original image value and the kernel value. Arithmetic
operators are as usual; |
merge |
The operator applied to combine the elements into a final value for the centre pixel. All have their usual meanings. |
value |
An optional vector of values in the target array for which to
apply the kernel. Takes priority over |
valueNot |
An optional vector of values in the target array for which not to apply the kernel. |
nNeighbours |
An optional numeric vector giving allowable numbers of
nonzero neighbours (including diagonal neighbours) for array elements
where the kernel will be applied. Takes priority over
|
nNeighboursNot |
An optional numeric vector giving nonallowable numbers of nonzero neighbours (including diagonal neighbours) for array elements where the kernel will be applied. |
renormalise |
If |
A morphed array with the same dimensions as the original array.
Jon Clayden <[email protected]>
kernels for kernel-generating functions, and
morphology for more specific mathematical morphology
functions. gameOfLife shows how this function can be used
for non-morphological purposes, in that case to power a cellular
automaton. See also the kernel and kernapply functions in
the stats package, particularly if you want to smooth time series.
This function provides information about the structure of a neighbourhood of a given width within a specified array.
neighbourhood(x, width)neighbourhood(x, width)
x |
An object that can be coerced to an array. |
width |
A vector giving the width of the neighbourhood in each dimension, which will be recycled if necessary. Must not be greater than the size of the array. Even values are rounded up to the next odd integer. |
A list with the following elements.
widths |
The width of the neighbourhood along each dimension. Currently all elements of this vector will be the same. |
size |
The number of pixels within the neighbourhood. |
locs |
A matrix giving the coordinates of each neighbourhood pixel relative to the centre pixel, one per row. |
offsets |
Vector offsets of the neighbourhood values within
|
Jon Clayden <[email protected]>
The resample function uses a kernel function to resample a target
array. This can be thought of as a generalisation of array indexing which
allows fractional indices. It is (S3) generic. The rescale function
is an alternative interface for the common case where the image is being
scaled to a new size.
resample(x, points, kernel, ...) ## Default S3 method: resample(x, points, kernel, pointType = c("auto", "general", "grid"), threads = getOption("mmand.threads"), ...) rescale(x, factor, kernel, ...)resample(x, points, kernel, ...) ## Default S3 method: resample(x, points, kernel, pointType = c("auto", "general", "grid"), threads = getOption("mmand.threads"), ...) rescale(x, factor, kernel, ...)
x |
Any object. For the default method, this must be coercible to an array. |
points |
Either a matrix giving the points to sample at, one per row, or a list giving the locations on each axis, which will be made into a grid. |
kernel |
A kernel function object, used to provide coefficients for each resampled value, or the name of one. |
... |
Additional options, such as kernel parameters. |
pointType |
A string giving the type of the point specification being
used. Usually can be left as |
threads |
If a positive integer, and the package is compiled with OpenMP support, the number of threads to use during the calculation. |
factor |
A vector of scale factors, which will be recycled to the
dimensionality of |
If a generalised sampling scheme is used (i.e. with points a
matrix), the result is a vector of sampled values. For a grid scheme (i.e.
with points a list, including for rescale), it is a
resampled array.
Jon Clayden <[email protected]>
kernels for kernel-generating functions.
resample(c(0,0,1,0,0), seq(0.75,5.25,0.5), triangleKernel())resample(c(0,0,1,0,0), seq(0.75,5.25,0.5), triangleKernel())
These functions can be used to sample and plot kernel profiles.
sampleKernelFunction(kernel, values) ## S3 method for class 'kernelArray' plot(x, y, axis = 1, lwd = 2, col = "red", ...) ## S3 method for class 'kernelFunction' plot(x, y, xlim = c(-2, 2), lwd = 2, col = "red", ...)sampleKernelFunction(kernel, values) ## S3 method for class 'kernelArray' plot(x, y, axis = 1, lwd = 2, col = "red", ...) ## S3 method for class 'kernelFunction' plot(x, y, xlim = c(-2, 2), lwd = 2, col = "red", ...)
kernel |
A kernel function object. |
values |
A vector of values to sample the function at. These are in units of pixels, with zero representing the centre of the kernel. |
x |
A kernel object of the appropriate class. |
y |
Ignored. |
axis |
The axis to profile along. |
lwd |
The line width to use for the kernel profile. |
col |
The line colour to use for the kernel profile. |
... |
Additional plot parameters. |
xlim |
The limits of the range used to profile the kernel. |
For sampleKernelFunction a vector of kernel values at the
locations requested. The plot methods are called for their
side-effects.
Jon Clayden <[email protected]>
kernels for kernel-generating functions.
sampleKernelFunction(mnKernel(), -2:2) plot(mnKernel())sampleKernelFunction(mnKernel(), -2:2) plot(mnKernel())
Skeletonisation is the process of thinning a shape to a medial line or surface, and can be achieved using elementary mathematical morphology operations in a number of ways. Three methods are available through this function. They are all iterative and therefore relatively time-consuming.
skeletonise(x, kernel = NULL, method = c("lantuejoul", "beucher", "hitormiss"))skeletonise(x, kernel = NULL, method = c("lantuejoul", "beucher", "hitormiss"))
x |
An object that can be coerced to an array, or for which a
|
kernel |
An array representing the kernel to be used for the underlying
morphology operations. The kernel is fixed for the |
method |
A string giving the method to use. See Details. |
The default method is Lantuéjoul's formula, a union across repeated
erosions, which works in any number of dimensions and may produce
reasonable results on greyscale images, but does not in general produce a
connected skeleton. Beucher introduced an alternative which may produce a
better result (although again the skeleton may not be connected), but this
implementation of the latter algorithm only applies to binary arrays. The
final method uses the so-called hit-or-miss transform, which searches for
exact patterns in the source array. This is guaranteed to produce a
connected skeleton, which is often desirable, but uses fixed kernels (so the
kernel argument is ignored) and is currently only implemented for 2D
binary arrays.
A skeletonised array with the same dimensions as the original array.
Jon Clayden <[email protected]>
C. Lantuéjoul (1977). Sur le modèle de Johnson-Mehl généralisé. Technical report, Centre de Morphologie Mathématique, Fontainebleau, France.
S. Beucher (1994). Digital skeletons in Euclidean and geodesic spaces. Signal Processing 38(1):127-141. doi:10.1016/0165-1684(94)90061-2.
x <- c(0,0,1,0,0,0,1,1,1,0,0) k <- c(1,1,1) skeletonise(x,k)x <- c(0,0,1,0,0,0,1,1,1,0,0) k <- c(1,1,1) skeletonise(x,k)
This function prints a rough, text-only representation of an image argument to the R terminal, mapping image intensities to a 10-level pseudo-greyscale. The image is first rescaled to fit into the terminal or other specified width, and downsampled in the row direction to correct for nonsquare character shapes.
sketch(x, invert = FALSE, width = getOption("width"), squash = 0.5)sketch(x, invert = FALSE, width = getOption("width"), squash = 0.5)
x |
An object that can be coerced to a numeric matrix or array. 3D arrays with third dimension no greater than 4 will be taken as multichannel 2D images, and their channels averaged before display. Plain vectors and 1D arrays will be treated as single-row matrices. |
invert |
By default the mapping uses heavier type for brighter areas.
If this option is |
width |
The width of sketch to draw, in characters. |
squash |
The factor by which to scale the row direction of the image. Generally this should be markedly less than one, to preserve the aspect ratio of the image, since most fixed-width font characters are taller than they are wide. |
The result is a compact representation of a matrix that can be used for visualising kernel arrays, sparse matrices and other non-images.
This function is called for the side-effect of printing an ASCII representation of its argument.
If the terminal does not used a fixed-width font, the result is unlikely to be useful.
Jon Clayden <[email protected]>
sketch(shapeKernel(c(9,15), type="diamond")) sketch(shapeKernel(c(9,15), type="diamond"), squash=1)sketch(shapeKernel(c(9,15), type="diamond")) sketch(shapeKernel(c(9,15), type="diamond"), squash=1)
This function checks whether a numeric array is symmetric, in the sense of transposition. This is tested by comparing the reversed vectorised array to the unreversed equivalent.
symmetric(x)symmetric(x)
x |
An object that can be coerced to a numeric array. |
A logical value indicating whether the array is symmetric or not.
Jon Clayden <[email protected]>
This function thresholds an array or vector, setting elements below the threshold value to zero. The threshold can be given literally or calculated using k-means clustering.
threshold(x, level, method = c("literal", "kmeans"), binarise = TRUE)threshold(x, level, method = c("literal", "kmeans"), binarise = TRUE)
x |
A numeric vector or array. |
level |
The literal threshold level, if required. |
method |
The method to use to calculate the threshold. If
|
binarise |
Whether to set suprathreshold elements to unity (if
|
Jon Clayden <[email protected]>
x <- c(0.1, 0.05, 0.95, 0.85, 0.15, 0.9) threshold(x, method="kmeans") threshold(x, 0.5)x <- c(0.1, 0.05, 0.95, 0.85, 0.15, 0.9) threshold(x, method="kmeans") threshold(x, 0.5)