Package 'RMaCzek' reference manual

Title:	Czekanowski's Diagrams
Description:	Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008> and K. Bartoszek, Y. Luo (2023) <doi:10.14708/ma.v51i2.7259>.
Authors:	Albin Vasterlund [aut], Krzysztof Bartoszek [cre, aut, ths], Ying Luo [aut], Piotr Jaskulski [ctb]
Maintainer:	Krzysztof Bartoszek <[email protected]>
License:	GPL-3
Version:	1.6.0
Built:	2024-11-22 06:27:56 UTC
Source:	CRAN

Calculate the distance matrix between clusters.

Description

Calculate the distance matrix for a czek_matrix with clustering result or a data set with its clustering labels.

Usage

cluster_dist(x, y, distfun = dist, dist_method = "average")
cluster_dist(x, y, distfun = dist, dist_method = "average")

Arguments

`x`	A data set or a matrix with class czek_matrix.
`y`	If x is the data set, y is the cluster label.
`distfun`	Specifies which distance function should be used.
`dist_method`	Four linkage criteria: single, complete, average and SSD.

Value

A distance matrix.

Examples

# Clustering Result on czek_matrix
x = czek_matrix(iris[,-5], cluster = TRUE, num_cluster = 3)
dist_czek = cluster_dist(x)
plot(czek_matrix(dist_czek))

# Clustering Result on a Data Set with Clustering Labels
dist_data = cluster_dist(x = iris[,-5], y = iris$Species)
plot(czek_matrix(dist_data))

# Clustering Result on czek_matrix
x = czek_matrix(iris[,-5], cluster = TRUE, num_cluster = 3)
dist_czek = cluster_dist(x)
plot(czek_matrix(dist_czek))

# Clustering Result on a Data Set with Clustering Labels
dist_data = cluster_dist(x = iris[,-5], y = iris$Species)
plot(czek_matrix(dist_data))

Preprocess data to produce Czekanowski's Diagram.

Description

Preprocess the data to generate a matrix of category czek_matrix for generating Czekanowski's Diagram. This method also offers exact and fuzzy clustering algorithms for Czekanowski's Diagram.

Usage

czek_matrix(
  x,
  order = "OLO",
  n_classes = 5,
  interval_breaks = NULL,
  monitor = FALSE,
  distfun = dist,
  scale_data = TRUE,
  focal_obj = NULL,
  as_dist = FALSE,
  original_diagram = FALSE,
  column_order_stat_grouping = NULL,
  dist_args = list(),
  cluster = FALSE,
  cluster_type = "exact",
  num_cluster = 3,
  sig.lvl = 0.05,
  scale_bandwidth = 0.05,
  min.size = 30,
  eps = 0.01,
  pts = c(1, 5),
  alpha = 0.2,
  theta = 0.9,
  ...
)
czek_matrix(
  x,
  order = "OLO",
  n_classes = 5,
  interval_breaks = NULL,
  monitor = FALSE,
  distfun = dist,
  scale_data = TRUE,
  focal_obj = NULL,
  as_dist = FALSE,
  original_diagram = FALSE,
  column_order_stat_grouping = NULL,
  dist_args = list(),
  cluster = FALSE,
  cluster_type = "exact",
  num_cluster = 3,
  sig.lvl = 0.05,
  scale_bandwidth = 0.05,
  min.size = 30,
  eps = 0.01,
  pts = c(1, 5),
  alpha = 0.2,
  theta = 0.9,
  ...
)

Arguments

`x`	A numeric matrix, data frame or a 'dist' object.
`order`	Specifies which seriation method should be applied. The standard setting is the seriation method OLO. If NA or NULL, then no seriation is done and the original ordering is saved. The user may provide their own ordering, through a number vector of indices. Also in this case no rearrangement will be done.
`n_classes`	Specifies how many classes the distances should be divided into. The standard setting is 5 classes.
`interval_breaks`	Specifies the partition boundaries for the distances. As a standard setting, each class represents an equal amount of distances. If the interval breaks are positive and sum up to 1, then it is assumed that they specify percentages of the distances in each interval. Otherwise, if provided as a numeric vector not summing up to 1, they specify the exact boundaries for the symbols representing distance groups.
`monitor`	Specifies if the distribution of the distances should be visualized. The standard setting is that the distribution will not be visualized. TRUE and "cumulativ_plot" is available.
`distfun`	Specifies which distance function should be used. Standard setting is the dist function which uses the Euclidean distance. The first argument of the function has to be the matrix or data frame containing the data.
`scale_data`	Specifies if the data set should be scaled. The standard setting is that the data will be scaled.
`focal_obj`	Numbers or names of objects (rows if x is a dataset and not 'dist' object) that are not to take part in the reordering procedure. These observations will be placed as last rows and columns of the output matrix. See Details.
`as_dist`	If TRUE, then the distance matrix of x is returned, with object ordering, instead of the matrix with the levels assigned in place of the original distances.
`original_diagram`	If TRUE, then the returned matrix corresponds as close as possible to the original method proposed by Czekanowski (1909). The levels are column specific and not matrix specific. See Details
`column_order_stat_grouping`	If original_diagram is TRUE, then here one can pass the partition boundaries for the ranking in each column.
`dist_args`	Specifies further parameters that can be passed on to the distance function.
`cluster`	If TRUE, Czekanowski's clustering is performed.
`cluster_type`	Specifies the cluster type and it can be ’exact’ or ’fuzzy’.
`num_cluster`	Specifies the number of clusters.
`sig.lvl`	The threshold for testing a change point is statistically significant. This value is passed to ecp::e.divisive().
`scale_bandwidth`	A ratio to control the width of the reaching range.
`min.size`	Minimum number of observations between change points.
`eps`	A vector of epsilon values for FDBScan.
`pts`	A vector of minimum points for FDBScan.
`alpha`	The weighting factor for density score adjustments.
`theta`	The weighting factor for density score adjustments.
`...`	Further parameters that can be passed on to the seriate function in the seriation package.

Value

The function returns a matrix with class czek_matrix. The returned object is expected to be passed to the plot function if as_dist is FALSE. If as_dist is passed as TRUE, then a czek_matrix object is returned that is not suitable for plotting. As an attribute of the output the optimized criterion value is returned. However, this is a guess based on seriation::seriate()'s and seriation::criterion()'s manuals. If something else was optimized, e.g. due to user's parameters, then this will be wrong. If unable to guess, then NA saved in the attribute.

Examples

# Set data ####
x<-mtcars

# Different type of input that give same result ############
czek_matrix(x)
czek_matrix(stats::dist(scale(x)))
## Not run: 
## below a number of other options are shown
## but they take too long to run

# Change seriation method ############
#seriation::show_seriation_methods("dist")
czek_matrix(x,order = "GW")
czek_matrix(x,order = "ga")
czek_matrix(x,order = sample(1:nrow(x)))

# Change number of classes ############
czek_matrix(x,n_classes = 3)

# Change the partition boundaries ############

#10%, 40% and 50%
czek_matrix(x,interval_breaks = c(0.1,0.4,0.5))

#[0,1] (1,4] (4,6] (6,8.48]
czek_matrix(x,interval_breaks = c(0,1,4,6,8.48))

#[0,1.7] (1.7,3.39]  (3.39,5.09] (5.09,6.78] (6.78,8.48]
czek_matrix(x,interval_breaks = "equal_width_between_classes")

# Change number of classes ############
czek_matrix(x,monitor = TRUE)
czek_matrix(x,monitor = "cumulativ_plot")

# Change distance function ############
czek_matrix(x,distfun = function(x) stats::dist(x,method = "manhattan"))

# Change dont scale the data ############
czek_matrix(x,scale_data = FALSE)
czek_matrix(stats::dist(x))

# Change additional settings to the seriation method ############
czek_matrix(x,order="ga",control=list(popSize=200, suggestions=c("SPIN_STS","QAP_2SUM")))

# Create matrix as originally described by Czekanowski (1909), with each column
# assigned levels according to how the order statistics of the  distances in it
# are grouped. The grouping below is the one used by Czekanowski (1909).
czek_matrix(x,original_diagram=TRUE,column_order_stat_grouping=c(3,4,5,6))

# Create matrix with two focal object that will not influence seriation
czek_matrix(x,focal_obj=c("Merc 280","Merc 450SL"))
# Same results but with object indices
czek_res<-czek_matrix(x,focal_obj=c(10,13))

# we now place the two objects in a new place
czek_res_neworder<-manual_reorder(czek_res,c(1:10,31,11:20,32,21:30), orig_data=x)

# the same can be alternatively done by hand
attr(czek_res,"order")<-attr(czek_res,"order")[c(1:10,31,11:20,32,21:30)]
# and then correct the values of the different criteria so that they
# are consistent with the new ordering
attr(czek_res,"Path_length")<-seriation::criterion(stats::dist(scale(x)),
order=seriation::ser_permutation(attr(czek_res, "order")),
method="Path_length")

# Here we need to know what criterion was used for the seriation procedure
# If the seriation package was used, then see the manual for seriation::seriate()
# seriation::criterion().
# If the genetic algorithm shipped with RMaCzek was used, then it was the Um factor.
attr(czek_res,"criterion_value")<-seriation::criterion(stats::dist(scale(x)),
order=seriation::ser_permutation(attr(czek_res, "order")),method="Path_length")
attr(czek_res,"Um")<-RMaCzek::Um_factor(stats::dist(scale(x)),
order= attr(czek_res, "order"), inverse_um=FALSE)
# Czekanowski's Clusterings ############
# Exact Clustering
czek_exact = czek_matrix(x, order = "GW", cluster = TRUE, num_cluster = 2, min.size = 2)
plot(czek_exact)
attr(czek_exact, "cluster_type") # To get the clustering type.
attr(czek_exact, "cluster_res") # To get the clustering suggestion.
attr(czek_exact, "membership") # To get the membership matrix

# Fuzzy Clustering
czek_fuzzy = czek_matrix(x, order = "OLO", cluster = TRUE, num_cluster = 2,
cluster_type = "fuzzy", min.size = 2, scale_bandwidth = 0.2)
plot(czek_fuzzy)
attr(czek_fuzzy, "cluster_type") # To get the clustering type.
attr(czek_fuzzy, "cluster_res") # To get the clustering suggestion.
attr(czek_fuzzy, "membership") # To get the membership matrix

## End(Not run)

# Set data ####
x<-mtcars

# Different type of input that give same result ############
czek_matrix(x)
czek_matrix(stats::dist(scale(x)))
## Not run: 
## below a number of other options are shown
## but they take too long to run

# Change seriation method ############
#seriation::show_seriation_methods("dist")
czek_matrix(x,order = "GW")
czek_matrix(x,order = "ga")
czek_matrix(x,order = sample(1:nrow(x)))

# Change number of classes ############
czek_matrix(x,n_classes = 3)

# Change the partition boundaries ############

#10%, 40% and 50%
czek_matrix(x,interval_breaks = c(0.1,0.4,0.5))

#[0,1] (1,4] (4,6] (6,8.48]
czek_matrix(x,interval_breaks = c(0,1,4,6,8.48))

#[0,1.7] (1.7,3.39]  (3.39,5.09] (5.09,6.78] (6.78,8.48]
czek_matrix(x,interval_breaks = "equal_width_between_classes")

# Change number of classes ############
czek_matrix(x,monitor = TRUE)
czek_matrix(x,monitor = "cumulativ_plot")

# Change distance function ############
czek_matrix(x,distfun = function(x) stats::dist(x,method = "manhattan"))

# Change dont scale the data ############
czek_matrix(x,scale_data = FALSE)
czek_matrix(stats::dist(x))

# Change additional settings to the seriation method ############
czek_matrix(x,order="ga",control=list(popSize=200, suggestions=c("SPIN_STS","QAP_2SUM")))

# Create matrix as originally described by Czekanowski (1909), with each column
# assigned levels according to how the order statistics of the  distances in it
# are grouped. The grouping below is the one used by Czekanowski (1909).
czek_matrix(x,original_diagram=TRUE,column_order_stat_grouping=c(3,4,5,6))

# Create matrix with two focal object that will not influence seriation
czek_matrix(x,focal_obj=c("Merc 280","Merc 450SL"))
# Same results but with object indices
czek_res<-czek_matrix(x,focal_obj=c(10,13))

# we now place the two objects in a new place
czek_res_neworder<-manual_reorder(czek_res,c(1:10,31,11:20,32,21:30), orig_data=x)

# the same can be alternatively done by hand
attr(czek_res,"order")<-attr(czek_res,"order")[c(1:10,31,11:20,32,21:30)]
# and then correct the values of the different criteria so that they
# are consistent with the new ordering
attr(czek_res,"Path_length")<-seriation::criterion(stats::dist(scale(x)),
order=seriation::ser_permutation(attr(czek_res, "order")),
method="Path_length")

# Here we need to know what criterion was used for the seriation procedure
# If the seriation package was used, then see the manual for seriation::seriate()
# seriation::criterion().
# If the genetic algorithm shipped with RMaCzek was used, then it was the Um factor.
attr(czek_res,"criterion_value")<-seriation::criterion(stats::dist(scale(x)),
order=seriation::ser_permutation(attr(czek_res, "order")),method="Path_length")
attr(czek_res,"Um")<-RMaCzek::Um_factor(stats::dist(scale(x)),
order= attr(czek_res, "order"), inverse_um=FALSE)
# Czekanowski's Clusterings ############
# Exact Clustering
czek_exact = czek_matrix(x, order = "GW", cluster = TRUE, num_cluster = 2, min.size = 2)
plot(czek_exact)
attr(czek_exact, "cluster_type") # To get the clustering type.
attr(czek_exact, "cluster_res") # To get the clustering suggestion.
attr(czek_exact, "membership") # To get the membership matrix

# Fuzzy Clustering
czek_fuzzy = czek_matrix(x, order = "OLO", cluster = TRUE, num_cluster = 2,
cluster_type = "fuzzy", min.size = 2, scale_bandwidth = 0.2)
plot(czek_fuzzy)
attr(czek_fuzzy, "cluster_type") # To get the clustering type.
attr(czek_fuzzy, "cluster_res") # To get the clustering suggestion.
attr(czek_fuzzy, "membership") # To get the membership matrix

## End(Not run)

Data of internet_availability

Description

Data of internet_availability

Usage

internet_availability
internet_availability

Format

An object of class list of length 3.

Manually reorder Czekanowski's Diagram

Description

This is a function that allows the user to manully reorder Czekanowski's Diagram and recalculates all the factors.

Usage

manual_reorder(x, v_neworder, ...)
manual_reorder(x, v_neworder, ...)

Arguments

`x`	a matrix with class czek_matrix, czek_matrix_dist or data matrix/data.frame or dist object.
`v_neworder`	a numeric vector with the new ordering.
`...`	specifies further parameters that will be passed to the czek_matrix function.

Value

The function returns a Czekanowski's Diagram with the new order and recalculated factors.

Examples

# Set data ####
x<-mtcars

# Calculate Czekanowski's diagram
czkm<-czek_matrix(x)
czkm_dist<-czek_matrix(x,as_dist=TRUE)
# new ordering
neworder<-attr(czkm,"order")
neworder[1:2]<-neworder[2:1]
# reorder the diagram
#if the output was Czekanowski's diagram without the distances
#remembered, then the original data has to be passed so that 
#factors can be recalculated.
new_czkm<-manual_reorder(czkm,v_neworder=neworder,orig_data=x)
new_czkm_dist<-manual_reorder(czkm_dist,v_neworder=neworder)
#we can also pass the original data directly
new_czkm<-manual_reorder(x,v_neworder=neworder)
#and this is equivalent to calling
czkm<-czek_matrix(x,order=neworder)
#up to the value of the "criterion_value" attribute
#which in the second case can be lost, as no information is passed
#on which one was originally used, while in the first case it might
#be impossible to recalculate-only criteria values from seriate are supported
#if a user has a custom seriation function, then they need to recalculate this
#value themselves
# Set data ####
x<-mtcars

# Calculate Czekanowski's diagram
czkm<-czek_matrix(x)
czkm_dist<-czek_matrix(x,as_dist=TRUE)
# new ordering
neworder<-attr(czkm,"order")
neworder[1:2]<-neworder[2:1]
# reorder the diagram
#if the output was Czekanowski's diagram without the distances
#remembered, then the original data has to be passed so that 
#factors can be recalculated.
new_czkm<-manual_reorder(czkm,v_neworder=neworder,orig_data=x)
new_czkm_dist<-manual_reorder(czkm_dist,v_neworder=neworder)
#we can also pass the original data directly
new_czkm<-manual_reorder(x,v_neworder=neworder)
#and this is equivalent to calling
czkm<-czek_matrix(x,order=neworder)
#up to the value of the "criterion_value" attribute
#which in the second case can be lost, as no information is passed
#on which one was originally used, while in the first case it might
#be impossible to recalculate-only criteria values from seriate are supported
#if a user has a custom seriation function, then they need to recalculate this
#value themselves

Produce a Czekanowski's Diagram

Description

This is a function that can produce a Czekanowski's Diagram and present clustering findings.

Usage

## S3 method for class 'czek_matrix'
plot(
  x,
  values = NULL,
  type = "symbols",
  plot_title = "Czekanowski's diagram",
  tl.cex = 1,
  tl.offset = 0.4,
  tl.srt = 90,
  pal = brewer.pal(n = 8, name = "Dark2"),
  alpha = 0.3,
  ps_power = 0.6,
  col_size = 1,
  cex.main = 1,
  ...
)
## S3 method for class 'czek_matrix'
plot(
  x,
  values = NULL,
  type = "symbols",
  plot_title = "Czekanowski's diagram",
  tl.cex = 1,
  tl.offset = 0.4,
  tl.srt = 90,
  pal = brewer.pal(n = 8, name = "Dark2"),
  alpha = 0.3,
  ps_power = 0.6,
  col_size = 1,
  cex.main = 1,
  ...
)

Arguments

`x`	a matrix with class czek_matrix.
`values`	specifies the color or the size of the symbols in the graph. The standard setting is a grey scale for a color graph and a vector with the values 2,1,0.5,0.25 and 0 for a graph with symbols.
`type`	specifies if the graph should use color or symbols. The standard setting is symbols.
`plot_title`	specifies the main title in the graph.
`tl.cex`	Numeric, for the size of text label.
`tl.offset`	Numeric, for text label.
`tl.srt`	Numeric, for text label, string rotation in degrees.
`pal`	The colour vector representing the clusters.
`alpha`	Factor modifying the opacity, alpha, typically in [0,1].
`ps_power`	A power value to adjust point size.
`col_size`	When type="col", the size of each point (maximum is 1).
`cex.main`	Specify the size of the title text.
`...`	specifies further parameters that can be passed on to the plot function.

Examples

# Set data ####
# Not Cluster
czek = czek_matrix(mtcars)
# Exact Clustering
czek_exact = czek_matrix(mtcars, order = "GW", cluster = TRUE, num_cluster = 2, min.size = 2)
# Fuzzy Clustering
czek_fuzzy = czek_matrix(mtcars, order = "OLO", cluster = TRUE, num_cluster = 2,
cluster_type = "fuzzy", min.size = 2, scale_bandwidth = 0.2)

# Standard plot ############
plot(czek_exact)
plot.czek_matrix(czek_fuzzy)

# Edit diagram title
plot(czek, plot_title = "mtcars", cex.main = 2)

# Change point size ############
# Specify values
plot(czek, values = c(1, 0.8, 0.5, 0.2, 0))
plot(czek, values = grDevices::colorRampPalette(c("black", "red", "white"))(5))

# set point size for 'symbols' type by setting power value
plot(czek, type = "symbols", ps_power = 1)

# set point size for 'col' type
plot(czek, type = "col", col_size = 0.6)

# Specify type ############
plot(czek, type = "symbols")
plot(czek, type = "col")

# Edit cluster ############
# Edit colors
plot(czek_exact, pal = c("red", "blue"))
# Edit opacity
plot(czek_exact, alpha = 0.5)

# Set data ####
# Not Cluster
czek = czek_matrix(mtcars)
# Exact Clustering
czek_exact = czek_matrix(mtcars, order = "GW", cluster = TRUE, num_cluster = 2, min.size = 2)
# Fuzzy Clustering
czek_fuzzy = czek_matrix(mtcars, order = "OLO", cluster = TRUE, num_cluster = 2,
cluster_type = "fuzzy", min.size = 2, scale_bandwidth = 0.2)

# Standard plot ############
plot(czek_exact)
plot.czek_matrix(czek_fuzzy)

# Edit diagram title
plot(czek, plot_title = "mtcars", cex.main = 2)

# Change point size ############
# Specify values
plot(czek, values = c(1, 0.8, 0.5, 0.2, 0))
plot(czek, values = grDevices::colorRampPalette(c("black", "red", "white"))(5))

# set point size for 'symbols' type by setting power value
plot(czek, type = "symbols", ps_power = 1)

# set point size for 'col' type
plot(czek, type = "col", col_size = 0.6)

# Specify type ############
plot(czek, type = "symbols")
plot(czek, type = "col")

# Edit cluster ############
# Edit colors
plot(czek_exact, pal = c("red", "blue"))
# Edit opacity
plot(czek_exact, alpha = 0.5)

Prints information concerning Czekanowski's Diagram

Description

This is a function that prints out information on a Czekanowski's Diagram.

Usage

## S3 method for class 'czek_matrix'
print(x, print_raw = FALSE, ...)
## S3 method for class 'czek_matrix'
print(x, print_raw = FALSE, ...)

Arguments

`x`	a matrix with class czek_matrix.
`print_raw`	logical, if TRUE print out raw, as if base::print() was called, in particular this prints out the matrix itself, if FALSE (default) print out a summary. Furthermore, with print_raw=TRUE the attributes "levels", "partition_boundaries" and "n_classes" defining the diagram will be printed out.
`...`	specifies further parameters that can be passed on to the print function.

Value

The function returns a Czekanowski's Diagram.

Examples

# Set data ####
x<-czek_matrix(mtcars)


# Standard print ############
print(x)
print.czek_matrix(x)
# Print out the raw object ############
print(x,print_raw=TRUE)
print.czek_matrix(x,print_raw=TRUE)
# Set data ####
x<-czek_matrix(mtcars)


# Standard print ############
print(x)
print.czek_matrix(x)
# Print out the raw object ############
print(x,print_raw=TRUE)
print.czek_matrix(x,print_raw=TRUE)

Prints information concerning Czekanowski's Diagram

Description

This is a function that prints out information on a Czekanowski's Diagram, but when the actual distances were saved.

Usage

## S3 method for class 'czek_matrix_dist'
print(x, print_raw = FALSE, ...)
## S3 method for class 'czek_matrix_dist'
print(x, print_raw = FALSE, ...)

Arguments

`x`	a matrix with class czek_matrix_dist.
`print_raw`	logical, if TRUE print out raw, as if base::print() was called, in particular this prints out the matrix itself, if FALSE (default) print out a summary. Furthermore, with print_raw=TRUE the attributes "levels", "partition_boundaries" and "n_classes" defining the diagram will be printed out.
`...`	specifies further parameters that can be passed on to the print function.

Value

The function returns a Czekanowski's Diagram.

Examples

# Set data ####
x<-czek_matrix(mtcars,as_dist=TRUE)


# Standard print ############
print(x)
print.czek_matrix(x)
# Print out the raw object ############
print(x,print_raw=TRUE)
print.czek_matrix(x,print_raw=TRUE)
# Set data ####
x<-czek_matrix(mtcars,as_dist=TRUE)


# Standard print ############
print(x)
print.czek_matrix(x)
# Print out the raw object ############
print(x,print_raw=TRUE)
print.czek_matrix(x,print_raw=TRUE)

function to load data from an mdt file (MaCzek 3.3 - http://www.antropologia.uw.edu.pl/MaCzek/maczek.html)

Description

This software comes AS IS in the hope that it will be useful WITHOUT ANY WARRANTY, NOT even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Please understand that there may still be bugs and errors. Use it at your own risk. We take no responsibility for any errors or omissions in this package or for any misfortune that may befall you or others as a result of its use. Please send comments and report bugs to Krzysztof Bartoszek at [email protected] .

Usage

read_maczek_file(filepath)
read_maczek_file(filepath)

Arguments

filepath

path to file *.mdt.

Value

data.frame.

Author(s)

Piotr Jaskulski

RMaCzek: A package that can produce Czekanowski's diagram

Description

This package produces Czekanowski's diagram

The packages functions

czek_matrix: A function that returns a distance matrix where the distances are divided into classes. It also offers exact and fuzzy Czekanowski's clustering algorithm. The return from the function is expected to be passed into the plot function.
plot.czek_matrix: A function that returns Czekanowski's Diagram.

Author(s)

Albin Vasterlund, Krzysztof Bartoszek, Ying Luo

Data of seals_similarities

Description

Data of seals_similarities

Usage

seals_similarities
seals_similarities

Format

An object of class matrix (inherits from array) with 37 rows and 37 columns.

Data of skulls_distances

Description

Data of skulls_distances

Usage

skulls_distances
skulls_distances

Format

An object of class matrix (inherits from array) with 13 rows and 13 columns.

Calculate the Um factor

Description

The function calculates the Um factor associated with an ordering of the rows and columns of a distance matrix. Lower values indicate a better grouping of similar objects. This was the original objective function proposed in the MaCzek program for producing Czekanowski's Diagram.

Usage

Um_factor(
  distMatrix,
  order = NULL,
  matrix_conversion_coefficient = 1,
  inverse_um = TRUE
)
Um_factor(
  distMatrix,
  order = NULL,
  matrix_conversion_coefficient = 1,
  inverse_um = TRUE
)

Arguments

`distMatrix`	a 'dist' object, matrix of distances between observations.
`order`	a vector, if NULL, then the value of the factor is calculate for the distance matrix as is, otherwise the rows and columns are reordered according to the vector order.
`matrix_conversion_coefficient`	numeric, value to be added to the distances, so that a division by 0 error is not thrown.
`inverse_um`	logical, if TRUE, then the negative is returned. Default TRUE as the function is called in the genetic algorithm maximization procedures.

Value

The function returns a numeric value equalling the Um_factor.

Examples

# Set data ####
x<-mtcars

mD<-stats::dist(scale(x))
mCz<-czek_matrix(x)
Um_factor(mD)
Um_factor(mD,order=attr(mCz,"order"))
# Set data ####
x<-mtcars

mD<-stats::dist(scale(x))
mCz<-czek_matrix(x)
Um_factor(mD)
Um_factor(mD,order=attr(mCz,"order"))

Data of urns

Description

Data of urns

Usage

urns
urns

Format

An object of class matrix (inherits from array) with 15 rows and 9 columns.

Package 'RMaCzek'

Help Index

Calculate the distance matrix between clusters.

Description

Usage

Arguments

Value

Examples

Preprocess data to produce Czekanowski's Diagram.

Description

Usage

Arguments

Value

Examples

Data of internet_availability

Description

Usage

Format

Manually reorder Czekanowski's Diagram

Description

Usage

Arguments

Value

Examples

Produce a Czekanowski's Diagram

Description

Usage

Arguments

Examples

Prints information concerning Czekanowski's Diagram

Description

Usage

Arguments

Value

Examples

Prints information concerning Czekanowski's Diagram

Description

Usage

Arguments

Value

Examples

function to load data from an mdt file (MaCzek 3.3 - http://www.antropologia.uw.edu.pl/MaCzek/maczek.html)

Description

Usage

Arguments

Value

Author(s)

RMaCzek: A package that can produce Czekanowski's diagram

Description

The packages functions

Author(s)

Data of seals_similarities

Description

Usage

Format

Data of skulls_distances

Description

Usage

Format

Calculate the Um factor

Description

Usage

Arguments

Value

Examples

Data of urns

Description

Usage

Format