Title:	Consensus Seriation for Binary Data
Version:	1.1
Description:	Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) https://volweb.utk.edu/~scolli46/sceLakhesis.pdf.
License:	GPL (≥ 3)
Imports:	stats, Rcpp, RcppArmadillo, readr, ca, ggplot2, Rdpack, shiny, shinydashboard, bslib
RdMacros:	Rdpack
Encoding:	UTF-8
RoxygenNote:	7.3.2
LinkingTo:	Rcpp, RcppArmadillo
Depends:	R (≥ 3.5.0)
LazyData:	true
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2026-04-24 18:18:14 UTC; archaeologus
Author:	Stephen A. Collins-Elliott [aut, cre]
Maintainer:	Stephen A. Collins-Elliott <sce@utk.edu>
Repository:	CRAN
Date/Publication:	2026-04-25 09:40:28 UTC

Lakhesis Calculator

Description

Launch Lakhesis Calculator, a graphical interface to explore binary matrices via correspondence analysis, select potentially well-seriated sequences, and perform consensus seriation. Interface is made with ggplot2, shiny, shinydashboard, and bslib (Wickham 2016; Chang et al. 2024; Chang and Borges Ribeiro 2021; Sievert et al. 2024).

Usage

LC()

Details

Input is done in the calculator, via a "long" format a two-column .csv file giving pairs of row and column incidences. See im_read_csv for details. Conversion of a pre-existing incidence matrix to long format can be performed with im_long.

Results can be downloaded from the calculator as an .rds file containing a list of the following:

• consensus The consensus seriations, diagnostic coefficients of agreement and optimality criterion, and seriated incidence matrix. (lakhesize).

• strands The strands selected by the investigator.

Value

Opens the Lakhesis Calculator.

References

Chang W, Borges Ribeiro B (2021). shinydashboard: Create Dashboards with 'Shiny'. https://CRAN.R-project.org/package=shinydashboard.

Chang W, Cheng J, Allaire JJ, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2024). shiny: Web Application Framework for R. R package version 1.8.1.9001; https://github.com/rstudio/shiny, https://shiny.posit.co.

Sievert C, Cheng J, Aden-Buie G (2024). bslib: Custom ‘Bootstrap’ ‘Sass’ Themes for ‘shiny’ and ‘rmarkdown’. R package version 0.7.0, https://github.com/rstudio/bslib, https://rstudio.github.io/bslib/.

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer, New York.

Correspondence Analysis with Procrustes Fitting

Description

Fit scores of correspondence analysi on an incidence matrix to those produced by reference matrix which contain an ideal seriation using a Procrustes method (on the reference matrix, see im_ref). Rotation is determined by minimizing Euclidean distance from each row score to the nearest reference row score. Correspondence analysis is performed using the ca package (Nenadic and Greenacre 2007).

Usage

ca_procrustes(obj, symmetric = TRUE)

## S3 method for class 'matrix'
ca_procrustes(obj, symmetric = TRUE)

## S3 method for class 'incidence_matrix'
ca_procrustes(obj, symmetric = TRUE)

Arguments

Value

A list object of class strand containing the following:

• ref The Procrustes-fit coordinates of the scores of the reference seriation.

• x The coordinates of the row standard scores of the data.

• y The coordinates of the column principal scores of the data.

• x_pr The Procrustes-fit coordinates of the row standard scores of the data.

• y_pr The Procrustes-fit coordinates of the column column scores of the data.

References

Nenadic O, Greenacre MJ (2007). “Correspondence Analysis in R, with Two- and Three-dimensional Graphics: The ca Package.” Journal of Statistical Software, 20, 1–13. doi:10.18637/jss.v020.i03.

Examples

data("quattrofontanili")
s <- ca_procrustes(quattrofontanili)
# print(s)

Seriate Procrustes-Fit CA Scores

Description

Obtain a ranking of row and column scores projected onto a reference curve of an ideal seriation (row and column scores are ranked separately). Scores of correspondence analysis have been fit to those produced by reference matrix contain an ideal seriation using a Procrustes method, projecting them. Rotation is determined by minimizing Euclidean distance from each row score to the nearest reference row score. Correspondence analysis is performed using the ca package (Nenadic and Greenacre 2007).

Usage

ca_procrustes_ser(obj, projection = "curve", samples = 10^5, symmetric = TRUE)

## S3 method for class 'incidence_matrix'
ca_procrustes_ser(obj, projection = "curve", samples = 10^5, symmetric = TRUE)

## S3 method for class 'matrix'
ca_procrustes_ser(obj, projection = "curve", samples = 10^5, symmetric = TRUE)

Arguments

Value

A list of class strand containing the following:.

• $dat A data frame with the following columns:

  • Procrustes1, Procrustes2 The location of the point on the biplot after fitting.

  • CurveIndex The orthogonal projection of the point onto the reference curve, given as the index of the point sampled along y = \beta_2 x^2 + \beta_0.

  • Distance The squared Euclidean distance of the point to the nearest point on the reference curve.

  • Rank The ranking of the row or column, a range of ⁠1:nrow`` and ⁠1:ncol“.

  • Type Either row or col.

  • sel Data frame column used in shiny app to indicate whether point is selected in biplot/curve projection.

• $im_seriated The seriated incidence matrix, of class incidence_matrix.

References

Nenadic O, Greenacre MJ (2007). “Correspondence Analysis in R, with Two- and Three-dimensional Graphics: The ca Package.” Journal of Statistical Software, 20, 1–13. doi:10.18637/jss.v020.i03.

Examples

data("quattrofontanili")
s <- ca_procrustes_ser(quattrofontanili)
# print(s)
# summary(s)

Optimality Criterion: Kendall-Doran (Column) Concentration

Description

The Kendall-Doran measure of concentration (Kendall 1963; Doran 1971). In a seriated matrix, this function computes the total number cells between the first and last non-zero value, column by column.

Usage

conc_c(obj)

## S3 method for class 'matrix'
conc_c(obj)

## S3 method for class 'incidence_matrix'
conc_c(obj)

Arguments

Value

The measure of concentration.

References

Doran J (1971). “Computer Analysis of Data from the la Tène Cemetry at Münsingen-Rain.” In Hodson FR, Kendall DG, Táutu P (eds.), Mathematics in the Archaeological and Historical Sciences, 422–431. Edinburgh University Press, Edinburgh.

Kendall DG (1963). “A Statistical Approach to Flinders Petrie's Sequence Dating.” Bulletin of the International Statistical Institute, 40, 657–680.

Examples

data("quattrofontanili")
conc_c(quattrofontanili)

Optimality Criterion: Weighted Row-Column Concentration

Description

Extends the Kendall-Doran (column) measure of concentration (see conc_c) to include rows and then weights the total measure by the total sum of values in the matrix.

Usage

conc_wrc(obj)

## S3 method for class 'matrix'
conc_wrc(obj)

## S3 method for class 'incidence_matrix'
conc_wrc(obj)

Arguments

Value

The weighted row-column coefficient of concentration.

Examples

data("quattrofontanili")
conc_wrc(quattrofontanili)

Optimality Criterion: Squared Correlation

Description

Treating each incidence of 1 in an element (i,j) of a seriated matrix as an (x,y) point, computes the squared correlation coefficient (see McCormick Jr. et al. 1969, 147-148).

Usage

cor_sq(obj)

## S3 method for class 'matrix'
cor_sq(obj)

## S3 method for class 'incidence_matrix'
cor_sq(obj)

Arguments

Value

Spearman's rank correlation coefficient.

References

McCormick Jr. WT, Deutsch SB, J.J. M, Schweitzer PJ (1969). “Identification of Data Structures and Relationships by Matrix Reordering Techniques.” Research Paper P-512, Institute for Defense Analyses.

Examples

data("quattrofontanili")
cor_sq(quattrofontanili)

Evaluating Element Fit

Description

Performs a goodness-of-fit test on individual row and column elements using deviance, using a quadratic-logistic model to fit row and column occurrences. In the case of perfect separation of 0/1 values, an NA value is assigned. Results are reported as p values for each row and column.

Usage

element_eval(obj)

## S3 method for class 'matrix'
element_eval(obj)

## S3 method for class 'incidence_matrix'
element_eval(obj)

Arguments

Value

A list containing results in data frames for row and column elements:

• RowFit a data frame containing

  • id Row element

  • p.val p values of the row elements

• ColFit a data frame containing

  • id Column element

  • p.val p values of the column elements

Examples

data("quattrofontanili")
element_eval(quattrofontanili)

Convert Incidence Matrix to Pairs (Long Format)

Description

Take an incidence matrix and convert it to a data frame of two columns, where the first column represents the row elements of the incidence matrix and the second column represents the column elements of the incidence matrix. Each row pair represents the incidence (or occurrence) of that row and column element together.

Usage

im_long(obj)

## S3 method for class 'matrix'
im_long(obj)

## S3 method for class 'incidence_matrix'
im_long(obj)

Arguments

Value

A data frame of two columns (row and column of the incidence matrix), in which row of the data frame represents a pair of an

Examples

data(quattrofontanili)
qf <- im_long(quattrofontanili)

# to export for uploading into the Lakhesis Calculator, use write.table() to 
# remove both row and column names:

# write.table(qf, file = 'qf.csv', row.names = FALSE, col.names = FALSE, sep = ",")

Merge Two Incidence Matrices

Description

From two incidience matrices, create a single incidence matrix. Matrices may contain same row or column elements.

Usage

im_merge(obj1, obj2)

## S3 method for class 'matrix'
im_merge(obj1, obj2)

## S3 method for class 'incidence_matrix'
im_merge(obj1, obj2)

Arguments

Value

A single incidence matrix.

Examples

data(quattrofontanili)
qf1 <- quattrofontanili[1:20, 1:40]
qf1 <- qf1[rowSums(qf1) != 0, colSums(qf1) != 0]

qf2 <- quattrofontanili[30:50, 20:60]
qf2 <- qf2[rowSums(qf2) != 0, colSums(qf2) != 0]

im_merge(qf1, qf2)

Read csv File to Incidence Matrix

Description

Wrapper around the read_csv function from the readr package (Wickham et al. 2024). Read a .csv file in which the first column represents row elements and the second column represents column elements, and convert it into an incidence matrix.

Usage

im_read_csv(
  filename,
  header = FALSE,
  characterencoding = "iso-8859-1",
  remove.hapax = FALSE
)

Arguments

Value

A matrix of binary values (0 = row/column occurrence is absence; 1 = row/column occurrence is present).

References

Wickham H, Hester J, Bryan J (2024). readr: Read Rectangular Text Data. R package version 2.1.5, https://github.com/tidyverse/readr, https://readr.tidyverse.org.

Create Reference Matrix

Description

Create an ideal reference matrix of well-seriated values of the same size as the input matrix.

Usage

im_ref(obj)

## S3 method for class 'matrix'
im_ref(obj)

Arguments

Value

A matrix of size n \times k with 1s along the diagonal. If n > k, 1s are placed from cell (i,i) to (i,i+k-n), with 0 in all other cells.

Examples

im_ref(matrix(NA, 5, 5))
im_ref(matrix(1, 7, 12))

Lakhesize

Description

This function returns the row and column consensus seriation for a list object of the strands class, containing their rankings, coefficients of association, and criterion. Consensus seriation is achieved by iterative simple linear regression to handle NA vales in each strand. To initialize, a regression is performed pairwise, with every strand as the dependent y variate and every other strand as the independent x variate. The independent variate's rankings are then regressed onto f(x) = \hat{\beta}_1 x + \hat{\beta}_0. If y \neq f(x), the mean of y and f(x) is used. Then, the values of the dependent variate and those of the regressed independent varaite are re-ranked together, to form a combined ranking, which serves as the dependent variate on the next iteration. The pair of strands is chosen which minimizes a specified optimality criterion. The process is repeated until all strands have been regressed and re-ranked into a single consensus seriation.

Usage

lakhesize(strands, crit = "cor_sq", pbar = TRUE)

## S3 method for class 'strands'
lakhesize(strands, crit = "cor_sq", pbar = TRUE)

## Default S3 method:
lakhesize(strands, crit = "cor_sq", pbar = TRUE)

Arguments

Value

A list of class lakhesis containing the following:

• row A seriated vector of row elements.

• col A seriated vector of column elements

• coef A data frame containing the following columns:

  • Strand The number of the strand.

  • Agreement The measure of agreement, i.e., how well each strand accords with the consensus seriation. Using the square of Spearman's rank correlation coefficient, \rho^2, between each strand and the consensus ranking, agreement is computed as the product of \rho^2 for their row and column rankings, \rho_r^2\rho_c^2.

  • Criterion Criterion of the optimality of each strand (per the "crit" option above).

• im_seriated The seriated incidence matrix, of class incidence_matrix.

Examples

data("qf_strands")
L <- lakhesize(qf_strands, pbar = FALSE)
# summary(L) 

Quattro Fontanili - Strands

Description

Three seriated strands selected from quattrofontanili data, identified by the package author as an example for the documentation of functions.

Usage

data("qf_strands")

Format

A strands object containing strands output by ca_procrustes.

Examples

data("qf_strands")
print(qf_strands)

Quattro Fontanili

Description

The seriation of tombs from necropoleis at Veii, primarily Quattro Fontanili, but also Valle la Fata, Vaccareccia, and Picazzano, in southern Etruria, established by Close-Brooks and Ridgway (1979).

Usage

data("quattrofontanili")

Format

A seriated incidence matrix of 81 rows (tombs) and 82 columns (types).

Data entered from Close-Brooks and Ridgway (1979), an English translation of the authors' original publication in Notizie degli Scavi (1963). Descriptions of types may be found in that paper.

References

Close-Brooks J, Ridgway D (1979). “Veii in the Iron Age.” In Ridgway D, Ridgway FR (eds.), Italy Before the Romans, 95–127. Academic Press, London.

Examples

data("quattrofontanili")
print(quattrofontanili)

Spearman Correlation Squared

Description

The square of Spearman's rank correlation coefficient applied to two rankings (Spearman 1904). Rows with NA values are automatically removed.

Usage

spearman_sq(r1, r2)

## S3 method for class 'numeric'
spearman_sq(r1, r2)

Arguments

Value

The square of Spearman's rank correlation coefficient with NA values removed.

References

Spearman C (1904). “The Proof and Measurement of Association between Two Things.” American Journal of Psychology, 15, 72–101. doi:10.2307/1412159.

Examples

# e.g., for two partial seriations:
x <- c(1, 2, 3, 4, NA, 5, 6, NA, 7.5, 7.5, 9)
y <- c(23, 17, 19, NA, 21, 22, 25, 26, 27, 36, 32)
spearman_sq(x, y)

Add Strand to List of Strands

Description

Given a list of strands, remove a row or column element and re-run seriation by correspondence analysis with Procrustes fitting (ca_procrustes_ser) to generate a new list of strands that exclude the specified elements. If the resulting strand lacks sufficient points to perform correspondence analysis, that strand is deleted in the output.

Usage

strand_add(strand, strands)

## S3 method for class 'strand'
strand_add(strand, strands)

Arguments

Value

A list of class strands.

Create Strand Object from Seriated Incidence Matrix

Description

Given a seriated incidence matrix with unique row and column names, create a strand object.

Usage

strand_create(obj, method = NULL)

## S3 method for class 'matrix'
strand_create(obj, method = NULL)

## S3 method for class 'incidence_matrix'
strand_create(obj, method = NULL)

Arguments

Value

A list of class strands.

Strand Extract

Description

From a list of strands produced by ca_procrustes_ser, extract two matrices containing the ranks of the rows and columns. The row/column elements are contained in the rows, and the strands are contained in the columns. NA values are entered where a given row/column element is missing from that strand.

Usage

strand_extract(strands)

Arguments

Value

A list of two matrices:

• Row A matrix of the ranks of the row elements.

• Col A matrix of the ranks of the column elements.

Examples

data("quattrofontanili")
data("qf_strands")
strand_extract(qf_strands)

Suppress Element from Strands

Description

Given a list of strands produced by correspondence analysis with Procrustes fitting (ca_procrustes_ser), remove one or more row or column elements, re-seriating each strand. This generates a new list of strands that exclude the specified elements. If a resulting strand lacks sufficient points to perform correspondence analysis, that strand is deleted in the output.

Usage

strand_suppress(strands, elements)

## S3 method for class 'strands'
strand_suppress(strands, elements)

## Default S3 method:
strand_suppress(strands, elements)

Arguments

Value

A list of the strands.

Examples

data("qf_strands")
strand_suppress(qf_strands, "QF II 15-16")

strand_suppress(qf_strands, c("QF II 15-16", "I", "XIV"))

Create List of Strands

Description

Given one or more individual strand objects, create a single list of class strands.

Usage

strands_create(strands)

## S3 method for class 'list'
strands_create(strands)

Arguments

Value

A list of class strands.