Title:	Random Tanglegram Partitions
Version:	1.2
Description:	Applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and nodes that maximize phylogenetic (in)congruence. It also includes functions to compute more easily the confidence intervals of classification metrics and plot results, reducing computational time. See Llaberia-Robledillo et al., (2023) <doi:10.1093/sysbio/syad016>.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	TRUE
Depends:	phytools, parallel, R (≥ 3.5.0)
Imports:	ape, distory, paco, parallelly, stats, stringr, vegan
RoxygenNote:	7.3.1
NeedsCompilation:	no
Suggests:	testthat (≥ 3.2.1.1)
Config/testthat/edition:	3
Packaged:	2024-06-12 11:43:29 UTC; Mar
Author:	Mar Llaberia-Robledillo [aut, cre, cph], Juan A. Balbuena [aut, cph], José Ignacio Lucas-Lledó [aut, cph], Oscar Alejandro Pérez-Escobar [aut, cph]
Maintainer:	Mar Llaberia-Robledillo <mar.llaberia@uv.es>
Repository:	CRAN
Date/Publication:	2024-06-13 12:00:01 UTC

amph_trem dataset

Description

Data set of mitochondrial haplotypes of the trematode Coitocaecum parvum (Crowcroft, 1945) and those of its amphipod host, Paracalliope fluviatilis (Thomson, 1879) from several locations in South Island, New Zeland (Largue et al. 2016).

Usage

data(amph_trem)

Format

This data set compresses five objects:

am_matrix

Associations between 17 haplotypes of Coitocaecum parvum and 59 haplotypes of Paracalliope fluviatilis. A binary matrix with 59 rows (amphipod) and 17 variables (trematode).

amphipod

Paracalliope fluviatilis consensus tree. An object of class "phylo" containing a list with the details of the consensus phylogenetic tree (i.e. edges, edges length, nodes, and tips names).

trematode

Coitocaecum parvum consensus tree. An object of class "phylo" containing a list with the details of the phylogenetic tree (i.e. edges, edges length, nodes and tips names).

amphipod_1000tr

1000 Bayesian posterior probability trees of Paracalliope fluviatilis. List of class "multiphylo" containing a 1000 phylogenetic trees with their respective details (i.e. edges, edges length, nodes, and tips names).

trematode_1000tr

1000 Bayesian posterior probability trees of Coitocaecum parvum. List of class "multiphylo" containing a 1000 phylogenetic trees with their respective details (i.e. edges, edges length, nodes, and tips names).

Source

Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2022). User’s Guide Random Tanglegram Partitions V.1.0.0. Zenodo. doi:10.5281/zenodo.6327235

References

Lagrue C., Joannes A., Poulin R., Blasco-Costa I. (2016). Genetic structure and host–parasite co‐divergence: evidence for trait‐specific local adaptation. Biological Journal of the Linnean Society. 118:344–358.

Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2022). User’s Guide Random Tanglegram Partitions V.1.0.0. Zenodo. doi:10.5281/zenodo.6327235

Create an host-symbiont association matrix

Description

Creates a binary host-symbiont association matrix from a two-columns matrix or data frame of host-symbiont associations.

Usage

assoc_mat(hs)

Arguments

Value

An association binary matrix, with hosts in rows and symbionts in columns, sorted alphabetically.

Examples

data(nuc_cp)
NTaxa <- sort(NUCtr$tip.label)
CPTaxa <- sort(CPtr$tip.label)

NC <- assoc_mat(data.frame(NTaxa, CPTaxa))

Geodesic distance between trees

Description

For any trimmed matrix produced with trimHS_maxC() it prunes the host-symbiont phylogenies to conform with the trimmed matrix and computes geodesic distance between the pruned trees. NOTE: This function can only be used with strictly bifurcating trees.

Usage

geo_D(ths, treeH, treeS, strat = "sequential", cl = 1)

Arguments

Value

Geodesic distance

NOTE

The node.label object in both trees can not contain NAs or null values (i.e. no numeric value). All nodes should have a value. Else remove node labels within the "phylo" class tree with tree$node.label <- NULL. For more details, see distory::dist.multiPhylo().

  This function can not be used with the trimmed matrices produced
  with \code{\link[=trimHS_maxI]{trimHS_maxI()}} or with the algorithm
  \code{\link[=max_incong]{max_incong()}} in datasets with
  multiple host-symbiont associations.

Source

Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2022). User’s Guide Random Tanglegram Partitions V.1.0.0. Zenodo.

References

Schardl C.L., Craven K.D., Speakman S., Stromberg A., Lindstrom A., Yoshida R. (2008). A Novel Test for Host-Symbiont Codivergence Indicates Ancient Origin of Fungal Endophytes in Grasses. Systematic Biology. 57:483–498.

Balbuena J.A., Perez-Escobar Ó.A., Llopis-Belenguer C., Blasco-Costa I. (2020). Random Tanglegram Partitions (Random TaPas): An Alexandrian Approach to the Cophylogenetic Gordian Knot. Systematic Biology. 69:1212–1230.

Examples

data(amph_trem)
N = 10 #for the example, we recommend 1e+4 value
n = 8

TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE)
GD <- geo_D(TAM, amphipod, trematode, strat = "sequential", cl = 1)

The Gini coefficient adjusted for negative attributes (Raffinetti, Siletti, & Vernizzi, 2015)

Description

Computes the Gini coefficient adjusted for negative (even weighted) data.

Usage

gini_RSV(y)

Arguments

Value

The value of the Gini coefficient adjusted for negative attributes.

NOTE

It produces a conventional Gini coefficient (G) (Ultsch and Lötsch 2017) if all output values are positive, or a normalized Gini coefficient (G*) (Raffinetti et al. 2015) if negative values are produced due to corrected frequencies (if res.fq = TRUE or diff.fq = TRUE). For more details see Raffinetti et al. (2015).

References

Ultsch A., Lötsch J. (2017). A data science based standardized Gini index as a Lorenz dominance preserving measure of the inequality of distributions. PLOS ONE. 12:e0181572. doi:10.1371/journal.pone.0181572

Raffinetti E., Siletti E., Vernizzi A. (2015). On the Gini coefficient normalization when attributes with negative values are considered. Stat Methods Appl. 24:507–521. doi:10.1007/s10260-014-0293-4

Examples

data(nuc_cp)
N = 10 #for the example, we recommend 1e+4 value
n = 15
# Maximizing congruence
NPc_PACo <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
               symmetric = FALSE, ei.correct = "sqrt.D",
               percentile = 0.01, res.fq = FALSE)
gini_RSV(y = NPc_PACo)

Plot the confidence intervals of Gini coefficient

Description

Computes and displays in a boxplot the Gini coefficient and their confidence intervals of the frequency (or residual/corrected frequencies) distributions of the estimated (in)congruence metric (with any of the three global-fit methods) of the individual host-symbiont associations.

Usage

gini_ci(LF_1, M01, ylab = "Gini coefficient", plot = TRUE, ...)

Arguments

Value

The Gini values obtained and their representation in a boxplot, with their confidence intervals.

NOTE

It produces a conventional Gini coefficient (G) (Ultsch and Lötsch 2017) if all output values are positive, or a normalized Gini coefficient (G*) (Raffinetti et al. 2015) if negative values are produced due to corrected frequencies (if res.fq = TRUE or diff.fq = TRUE). For more details see Raffinetti et al. (2015).

References

Ultsch A., Lötsch J. (2017). A data science based standardized Gini index as a Lorenz dominance preserving measure of the inequality of distributions. PLOS ONE. 12:e0181572. doi:10.1371/journal.pone.0181572

Raffinetti E., Siletti E., Vernizzi A. (2015). On the Gini coefficient normalization when attributes with negative values are considered. Stat Methods Appl. 24:507–521. doi:10.1007/s10260-014-0293-4

Examples

data(nuc_cp)
N = 1 #for the example, we recommend 1e+4 value
n = 15
# Maximizing congruence
NPc_PACo <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
                symmetric = FALSE, ei.correct = "sqrt.D",
                percentile = 0.01, res.fq = FALSE)

# Loaded directly from dataset
# THSC <- trimHS_maxC(N, np_matrix, n)
# pp_treesPACo_cong <- prob_statistic(ths = THSc, np_matrix, NUC_500tr[1:10],
#                         CP_500tr[1:10], freqfun = "paco", NPc_PACo,
#                         symmetric = FALSE, ei.correct = "sqrt.D",
#                         percentile = 0.01, correction = "none")

gini_ci(LF_1 = NPc_PACo, M01 = pp_treesPACo_cong,
         ylab = "Gini Coefficient (G)",
         plot = TRUE, ylim = c(0.3, 0.8))
abline(h = 1/3) # because res.fq = TRUE

Frequency of host-symbiont association

Description

Determines the frequency (or residual/corrected frequency) of each host-symbiont association in a given percentile of cases that maximize phylogenetic (in)congruence.

Usage

link_freq(
  x,
  fx,
  HS,
  percentile = 0.01,
  sep = "-",
  below.p = TRUE,
  res.fq = TRUE
)

Arguments

Value

A dataframe with host-symbiont associations in rows. The first and second columns display the names of the host and symbiont terminals, respectively. The third column designates the host-symbiont association by pasting the names of the terminals, and the fourth column displays the frequency of occurrence of each host-symbiont association. If res.fq = TRUE, column 5 displays the corrected frequencies as a residual.

NOTE

The res.fq = TRUE correction is recommended in tanglegrams with large portion of multiple (as opposed to one-to-one) host-symbiont associations. For future usage, frequencies of host-symbiont associations above a given percentile values can also be computed setting below.p = FALSE.

Examples

data(amph_trem)
N = 10 #for the example, we recommend 1e+4 value
n = 8

TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE)
PACO <- paco_ss(TAM, amphipod, trematode, symmetric = TRUE,
                ei.correct = "sqrt.D", strat = "parallel", cl = 8)
LFPACO <- link_freq(TAM, PACO, am_matrix, percentile = 0.01,
                    below.p = TRUE, res.fq = TRUE)

Confidence intervals for the frequency of host-symbiont association

Description

From the matrix obtained in prob_statistic(), compute the confidence intervals for the frequencies (or residual/corrected frequencies) of the host-symbiont associations using a set of pairs of posterior probability trees of host and symbiont.

Usage

linkf_CI(
  freqfun = "paco",
  x,
  fx,
  c.level = 95,
  barplot = TRUE,
  col.bar = "lightblue",
  col.ci = "darkblue",
  y.lim = NULL,
  ...
)

Arguments

Value

A dataframe with associations information (columns 1 and 2), the observed value of the frequencies for these associations (column 3), the mean, the minimum and the maximum value of the frequencies (columns 4, 5 and 6) obtained with the sets of posterior probability trees.

Examples

data(nuc_cp)
N = 10 #for the example, we recommend 1e+4 value
n = 8
# Maximizing incongruence
NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
                 symmetric = FALSE, ei.correct = "sqrt.D",
                 percentile = 0.99, diff.fq = TRUE,
                 strat = "parallel", cl = 8)
# Loaded directly from dataset
# THSi <- trimHS_maxI(N, np_matrix, n)
# pp_treesPACo_incong <- prob_statistic(ths = THSi, np_matrix,
#                        NUC_500tr[1:5], CP_500tr[1:5], freqfun = "paco",
#                        NPi, symmetric = FALSE, ei.correct = "sqrt.D",
#                        percentile = 0.99, diff.fq = TRUE, res.fq = FALSE,
#                        below.p = FALSE, strat = "parallel", cl = 8)
LFci <- linkf_CI (freqfun = "paco", x = pp_treesPACo_incong, fx = NPi,
                  c.level = 95, ylab = "Observed - Expected frequency")

Algortihm for maximizing congruence between two phylogenies

Description

Prunes the host (H) and symbiont (S) phylogenies to conform with trimmed matrices and computes the given global fit method, Geodesic distances (GD), Procrustes Approach to Cophylogeny (PACo) or ParaFit (Legendre et al. 2002) between the pruned trees. Then, determines the frequency or corrected residual of each host-symbiont association occurring in a given percentile of cases that maximize phylogenetic congruence.

Usage

max_cong(
  HS,
  treeH,
  treeS,
  n,
  N,
  method = "paco",
  symmetric = FALSE,
  ei.correct = "none",
  percentile = 0.01,
  res.fq = TRUE,
  strat = "sequential",
  cl = 1
)

Arguments

Value

A dataframe with host-symbiont associations in rows. The first and second columns display the names of the host and symbiont terminals, respectively. The third column designates the host-symbiont association by pasting the names of the terminals, and the fourth column displays the frequency of occurrence of each host-symbiont association in p. If res.fq = TRUE, column 5 displays the corrected frequencies as a residual.

NOTE

If the node.label object in both trees contains NAs or empty values (i.e. no numeric value). All nodes should have a value. Else remove node labels within the "phylo" class tree with tree$node.label <- NULL. For more details, see distory::dist.multiPhylo()

Examples

data(nuc_pc)
N = 1 #for the example, we recommend 1e+4 value
n = 15
NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
                symmetric = FALSE, ei.correct = "sqrt.D",
                percentile = 0.01, res.fq = FALSE)

Algortihm for maximizing incongruence between two phylogenies

Description

Prunes the host (H) and symbiont (S) phylogenies to conform with the trimmed matrix and computes the given global-fit method (PACo or ParaFit) between the pruned trees. Then, determines the frequency of each host-symbiont association occurring in a given percentile of cases that maximize phylogenetic incongruence.

Usage

max_incong(
  HS,
  treeH,
  treeS,
  n,
  N,
  method = "paco",
  symmetric = FALSE,
  ei.correct = "none",
  percentile = 0.99,
  diff.fq = FALSE,
  strat = "sequential",
  cl = 1
)

Arguments

Value

A dataframe with host-symbiont associations in rows. The first and second columns display the names of the host and symbiont terminals, respectively. The third column designates the host-symbiont association by pasting the names of the terminals, and the fourth column displays the frequency of occurrence of each host-symbiont association in p. If diff.fq = TRUE, column 5 displays the corrected frequencies.

NOTE

The node.label object in both trees can not contain NAs or null values (i.e. no numeric value). All nodes should have a value. Else remove node labels within the "phylo" class tree with tree$node.label <- NULL. For more details, see distory::dist.multiPhylo().

  \code{GD} method can not be used with the trimmed matrices produced
  with \code{\link[=trimHS_maxI]{trimHS_maxI()}} or with the algorithm
  \code{\link[=max_incong]{max_incong()}} for those datasets with
  multiple associations.

Examples

data(nuc_pc)
N = 1 #for the example, we recommend 1e+4 value
n = 15
NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
                  symmetric = FALSE, ei.correct = "sqrt.D",
                  percentile = 0.99, diff.fq = TRUE)

Nuclear and chloroplast dataset of orchids

Description

Data set of nuclear and chloroplast loci of 52 orchid taxa from Kew DNA and Tissue Collection, https://dnabank.science.kew.org/homepage.html (Perez-Escobar et al. 2021).

Usage

data(nuc_cp)

Format

This data set consists of seven objects:

np_matrix

Associations one-to-one between the 52 orchid taxa. A binary matrix with 52 rows (nuclear) and 52 columns (chloroplast).

NUCtr

Phylogeny constructed by sequence data of nuclear loci of orchids (Perez-Escobar et al. 2021). An object of class "phylo" containing the details of the phylogenetic tree (i.e. edge, edge length, nodes and tips names).

CPtr

Phylogeny constructed by sequence data of chloroplast loci of orchids (Perez-Escobar et al. 2021). An object of class "phylo" containing the details of the phylogenetic tree (i.e. edge, edge length, nodes and tips names).

NUC_500tr

500 bootstrap replicates trees from Perez-Escobar et al. (2021). Object of class "multiphylo" containing a 500 phylogenetic trees with their respective details (i.e. edges, edges length, nodes, and tips names).

CP_500tr

500 bootstrap replicates trees from Perez-Escobar et al. (2021). Object of class "multiphylo" containing a 500 phylogenetic trees with their respective details (i.e. edges, edges length, nodes, and tips names).

pp_treesPACo_cong

Matrix with the value of the PACo statistics generated for each pair (H and S) of posterior probability trees maximizing congruence between them.

pp_treesPACo_incong

Matrix with the value of the PACo statistics generated for each pair (H and S) of posterior probability trees maximizing incongruence between them.

Source

Perez-Escobar O.A., Dodsworth S., Bogarin D., Bellot S., Balbuena J.A., Schley R., Kikuchi I., Morris S.K., Epitawalage N., Cowan R., Maurin O., Zuntini A., Arias T., Serna A., Gravendeel B., Torres M.F., Nargar K., Chomicki G., Chase M.W., Leitch I.J., Forest F., Baker W.J. (2021). Hundreds of nuclear and plastid loci yield novel insights into orchid relationships. American Journal of Botany, 108(7), 1166-1180.

References

Perez-Escobar O.A., Dodsworth S., Bogarin D., Bellot S., Balbuena J.A., Schley R., Kikuchi I., Morris S.K., Epitawalage N., Cowan R., Maurin O., Zuntini A., Arias T., Serna A., Gravendeel B., Torres M.F., Nargar K., Chomicki G., Chase M.W., Leitch I.J., Forest F., Baker W.J. (2021). Hundreds of nuclear and plastid loci yield novel insights into orchid relationships. American Journal of Botany, 108(7), 1166-1180.

Maximum number of unique one-to-one association over a number of runs

Description

For a binary matrix of host-symbiont associations, it finds the maximum number of host-symbiont pairs, n, for which one-to-one unique associations can be chosen.

Usage

one2one_f(
  HS,
  reps = 10000,
  interval = NULL,
  strat = "sequential",
  cl = 1,
  plot = TRUE
)

Arguments

Value

The maximum number of unique one-to-one associations (n).

NOTE

It can be used to decide the best n prior to application of max_cong().

Examples

N = 10  #for the example, we recommend 1e+4 value
data(amph_trem)
n <- one2one_f(am_matrix, reps = N, interval = c(2, 10), plot = TRUE)

Procrustes Approach to Cophylogeny (PACo) of the host and symbiont configurations

Description

For any trimmed matrix produced with trimHS_maxC() or trimHS_maxI(), it prunes the host (H) and symbiont (S) phylogenies to conform with the trimmed matrix and runs Procruste Approach to Cophylogeny (PACo) to produce the squared sum of residuals of the Procrustes superimposition of the host and symbiont configurations in Euclidean space.

Usage

paco_ss(
  ths,
  treeH,
  treeS,
  symmetric = FALSE,
  proc.warns = FALSE,
  ei.correct = "none",
  strat = "sequential",
  cl = 1
)

Arguments

Value

A sum of squared residuals.

Source

Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2022). User’s Guide Random Tanglegram Partitions V.1.0.0. Zenodo.

References

Balbuena J.A., Miguez-Lozano R., Blasco-Costa I. (2013). PACo: A Novel Procrustes Application to Cophylogenetic Analysis. PLOS ONE. 8:e61048.

Balbuena J.A., Perez-Escobar Ó.A., Llopis-Belenguer C., Blasco-Costa I. (2020). Random Tanglegram Partitions (Random TaPas): An Alexandrian Approach to the Cophylogenetic Gordian Knot. Systematic Biology. 69:1212–1230.

Examples

data(amph_trem)
N = 10 #for the example, we recommend 1e+4 value
n = 8

TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE)
PACO <- paco_ss(TAM, amphipod, trematode, symmetric = TRUE,
                ei.correct = "sqrt.D", strat = "parallel", cl = 8)

Test of host-symbiont coevolution

Description

For any trimmed matrix produced with trimHS_maxC() or trimHS_maxI(), it prunes the host (H) and symbiont (S) phylogenies to conform with the trimmed matrix and runs ape::parafit() (Legendre et al. 2002) to calculate the ParaFitGlobal Statistic.

Usage

paraF(ths, treeH, treeS, ei.correct = "none", strat = "sequential", cl = 1)

Arguments

Value

A number object with the ParaFitGlobal Statistic of host-symbiont test for the N trimmed matrix.

References

Legendre P., Desdevises Y., Bazin E. (2002). A Statistical Test for Host–Parasite Coevolution. Systematic Biology. 51:217–234.

Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2020). Random Tanglegram Partitions (Random TaPas): An Alexandrian Approach to the Cophylogenetic Gordian Knot. Systematic Biology. 69:1212–1230.

Examples

data(amph_trem)
N = 10 #for the example, we recommend 1e+4 value
n = 8

TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE)
PF <- paraF(TAM, amphipod, trematode, ei.correct = "sqrt.D",
           strat = "parallel", cl = 8)

Frequencies of the associations for the posterior probability trees

Description

Computes frequencies (or residual/corrected frequencies) of the host-symbiont associations for pairs (H and S) of posterior probability trees from the statistics generatedwith GD (Geodesic Distances), PACo (PACo) or ParaFit(ParaFit).

Usage

prob_statistic(
  ths,
  HS,
  mTreeH,
  mTreeS,
  freqfun = "paco",
  fx,
  percentile = 0.01,
  correction = "none",
  symmetric = FALSE,
  ei.correct = "none",
  algm = "maxcong",
  proc.warns = FALSE,
  strat = "sequential",
  cl = 1
)

Arguments

Value

A matrix with the value of the statistics for each of the probability trees.

Examples

data("nuc_cp")
N = 10 #for the example, we recommend 1e+4 value
n = 15
# Maximizing congruence (not run)
NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
                symmetric = FALSE, ei.correct = "sqrt.D",
                percentile = 0.01, strat = "parallel", cl = 8)
THSc <- trimHS_maxC(N, np_matrix, n)
pp_treesPACOo_cong <- prob_statistic(THSc, np_matrix, NUC_500tr[1:10],
                         CP_500tr[1:10], freqfun = "paco", NPc,
                         percentile = 0.01, correction = "none",
                         algm = "maxcong", symmetric = FALSE,
                         ei.correct = "sqrt.D",
                         strat = "parallel", cl = 8)

# Maximizing incongruence
NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
                  symmetric = FALSE, ei.correct = "sqrt.D",
                  percentile = 0.99, diff.fq = TRUE)
THSi <- trimHS_maxI(N, np_matrix, n)
pp_treesPACOo_incong <- prob_statistic(THSi, np_matrix, NUC_500tr[1:5],
                                 CP_500tr[1:5], freqfun = "paco", NPi,
                                 percentile = 0.99, correction = "diff.fq",
                                 symmetric = FALSE, ei.correct = "sqrt.D",
                                 strat = "parallel", cl = 8)

Tanglegram of the host-symbiont frequencies

Description

Maps the estimated (in)congruence metrics of the individual host-symbiont associations as heatmap on a tanglegram. It also plots the average frequency (or residual/corrected frequency) of occurrence of each terminal and optionally, the fast maximum likelihood estimators of ancestral states of each node.

Usage

tangle_gram(
  treeH,
  treeS,
  HS,
  fqtab,
  colscale = "diverging",
  colgrad,
  nbreaks = 50,
  node.tag = TRUE,
  cexpt = 1,
  link.lwd = 1,
  link.lty = 1,
  fsize = 0.5,
  pts = FALSE,
  link.type = "straight",
  ftype = "i",
  ...
)

Arguments

Value

A tanglegram with quantitative information displayed as heatmap.

NOTE

In order to calculate the ancestral states in the phylogenies, all nodes of the trees (node.label) must have a value (NA or empty values are not allowed). In addition, the trees must be time-calibrated and preferably rooted. If one of these elements is missing, an error will be generated and nodes and points of terminals will be displayed as black.

Examples

data(nuc_cp)
N = 10 #for the example, we recommend 1e+4 value
n = 8
NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
                symmetric = TRUE, ei.correct = "sqrt.D",
                percentile = 0.01, res.fq = FALSE,
                strat = "parallel", cl = 4)
col = c("darkorchid4", "gold")
tangle_gram(NUCtr, CPtr, np_matrix, NPc, colscale = "sequential",
            colgrad = col, nbreaks = 50, node.tag = TRUE)

Trims the H-S association matrix maximizing the congruence

Description

For N runs, it randomly chooses n unique one-to-one associations and trims the H-S association matrix to include only the n associations.

Usage

trimHS_maxC(N, HS, n, check.unique = TRUE, strat = "sequential", cl = 1)

Arguments

Value

A list of the N trimmed matrices.

Examples

data(nuc_cp)
N = 10  #for the example, we recommend 1e+4 value
n = 15
TNC <- trimHS_maxC(N, np_matrix, n, check.unique = TRUE)

Trims the H-S association matrix maximizing the incongruence

Description

For N runs, it randomly chooses n associations and trims the H-S association matrix to include them, allowing both single and multiple associations.

Usage

trimHS_maxI(N, HS, n, check.unique = TRUE, strat = "sequential", cl = 1)

Arguments

Value

A list of the N trimmed matrices.

Examples

data(nuc_cp)
N = 10  #for the example, we recommend 1e+4 value
n = 15
TNC <- trimHS_maxI(N, np_matrix, n, check.unique = TRUE)