Package 'CongreveLamsdell2016'

Consistency indices

Description

Consistency indices of Congreve & Lamsdell datasets.

Usage

clCI

Format

An object of class numeric of length 100.

---

Default colours for analyses.

Description

Default colours for analyses.

Usage

clColours

Format

An object of class character of length 8.

---

Initialize ternary plots for quartet plotting

Description

Sets up a blank ternary plot ready for analytical results to be added.

Usage

clInitializeTernaryQuarts(
  zoom = 1,
  padding = 0.1,
  gridLines = 10,
  fontSize = 1,
  gridCol = "#DBDBDB",
  backgroundCol = "#FDFDFE",
  xLim = c(0, 1/zoom) - 0.01,
  yLim = c(0.5 - (1/zoom), 0.5),
  isometric = TRUE
)

clInitializeTernarySplits(
  fontSize = 1,
  xLim = NULL,
  yLim = NULL,
  gridCol = "#DBDBDB",
  backgroundCol = "#FDFDFE",
  padding = 0.1,
  isometric = TRUE
)

Arguments

zoom	Level of magnification (times), used to adjust ticks and scale.
padding	Padding, passed to TernaryPlot.
gridLines	Number of grid lines, passed to TernaryPlot as grid.lines.
fontSize	Font size, passed to TernaryPlot as lab.cex.
gridCol	Colour, passed to TernaryPlot as grid.col.
backgroundCol	Background colour, passed to TernaryPlot as col.
xLim, yLim	x and y limits, passed to TernaryPlot as xlim, ylim.
isometric	Logical specifying whether plot should be isometric, passed to TernaryPlot as isometric.

Functions

• clInitializeTernarySplits(): Initialize ternary plots for partition plotting.

---

100 simulated data matrices

Description

Contains the 100 simulated matrices generated by Congreve & Lamsdell (2016) using a heterogeneous Markov-k model, generated from the clReferenceTree topology, with all branches sharing an equal length.

Usage

clPhyDat

clMatrices

Format

• clPhyDat: A list with 100 entries, each comprising a phyDat object of 55 characters for 22 taxa.

• clMatrices: A list with 100 entries, each comprising a list of character tokens for each simulated character, as read from raw nexus files using ape::read.nexus.data. The four dummy 'characters' have been removed.

An object of class list of length 100.

Source

doi:10.5061/dryad.7dq0j

References

• Congreve, C. R. & Lamsdell, J. C. (2016). Implied weighting and its utility in palaeontological datasets: a study using modelled phylogenetic matrices. Palaeontology 59(3), 447–465. doi:10.1111/pala.12236.

• Congreve, C. R. & Lamsdell, J. C. (2016). Data from: Implied weighting and its utility in palaeontological datasets: a study using modelled phylogenetic matrices. Dryad Digital Repository. doi:10.5061/dryad.7dq0j.

---

Plot results

Description

Plots the results of the analyses of the Congreve & Lamsdell (2016) datasets.

Usage

clPlotQuartets(
  dataset,
  tree,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

clPlotAverageQuartets(
  dataset,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

clPlotTheseAverageQuartets(dataset, cex = 1.1, pch = 2, col = "black", ...)

clPlotSplits(
  dataset,
  tree,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

clPlotTheseAverageSplits(dataset, cex = 1.1, pch = 2, col = "black", ...)

clPlotTheseBestAverageSplits(dataset, cex = 1.1, pch = 2, col = "black", ...)

clPlotAverageSplits(
  dataset,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

clPlotBestAverageSplits(
  dataset,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

Arguments

dataset	Dataset to plot, for example ⁠\link[=clResults]{clBootGcQuartets}⁠.
tree	Integer specifying which tree to plot.
cex, pch, ...	Graphical parameters to pass to JoinTheDots.
col	Named vector specifying colours to use to plot each analysis, named to match names(dataset).

Value

Returns invisible.

Functions

• clPlotAverageQuartets(): Plots average across all 100 trees.

• clPlotTheseAverageQuartets(): Plot average for single dataset across all 100 trees.

• clPlotSplits(): Splits equivalent of clPlotQuartets.

• clPlotTheseAverageSplits(): Splits equivalent of clPlotTheseAverageQuartets.

• clPlotTheseBestAverageSplits(): Splits equivalent of clPlotTheseBestAverageQuartets.

• clPlotAverageSplits(): Splits equivalent of clPlotAverageQuartets.

• clPlotBestAverageSplits(): Splits equivalent of clPlotAverageQuartets.

Author(s)

Martin R. Smith

---

Tree topology for matrix simulation

Description

The tree topology used to generate the matrices in clMatrices Congreve & Lamsdell (2016).

Usage

clReferenceTree

Format

A single phylogenetic tree saved as an object of class phylo.

Source

Congreve & Lamsdell (2016).

References

• Congreve, C. R. & Lamsdell, J. C. (2016). Implied weighting and its utility in palaeontological datasets: a study using modelled phylogenetic matrices. Palaeontology 59(3), 447–465. doi:10.1111/pala.12236.

• Congreve, C. R. & Lamsdell, J. C. (2016). Data from: Implied weighting and its utility in palaeontological datasets: a study using modelled phylogenetic matrices. Dryad Digital Repository. doi:10.5061/dryad.7dq0j.

Examples

data(clReferenceTree)
if (requireNamespace("ape", quietly = TRUE)) plot(clReferenceTree)

---

Congreve and Lamsdell tree distances

Description

Distance of CL trees from generative tree.

Usage

clBremQuartets

clBremPartitions

clMkvPartitions

clMkvQuartets

clBootFreqPartitions

clBootFreqQuartets

clJackFreqPartitions

clJackFreqQuartets

clBootGcPartitions

clBootGcQuartets

clJackGcPartitions

clJackGcQuartets

Format

An object of class list of length 7.

An object of class list of length 7.

An object of class array of dimension 21 x 8 x 100.

An object of class array of dimension 21 x 7 x 100.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

Details

For each of the 100 matrices generated by Congreve & Lamsdell (2016), I conducted phylogenetic analysis under different methods:

Mkv:

using the Markov K model in MrBayes;

eq:

using equal weights in TNT;

k1, k2, k3, k5, kX:

using implied weights in TNT, with the concavity constant (k) set to 1, 2, 3, 5, or 10;

kC:

by taking the strict consensus of all trees recovered by implied weights parsimony analysis under the k values 2, 3, 5 and 10 (but not 1).

For each analysis, I recorded the strict consensus of all optimal trees, and also the consensus of trees that were suboptimal by a specified degree.

I then calculated, of the total number of quartets or partitions that were resolved in the reference tree, how many were the same or different in the tree that resulted from the phylogenetic analysis, and how many were not resolved in this tree (r2).

The data object contains a list whose elements are named after the methods, as listed above.

Each list entry is a three-dimensional array, whose dimensions are:

1. The suboptimality of the tree. Different measures of node support are employed:

        * `Mkv`: Posterior probabilities, at 2.5\% intervals (50\%, 52.5\%, ...
         97.5\%, 100\%).
   
        * `Brem`: Bremer supports: the consensus of all trees that are
          (equal weights) 0, 1, .... 19, 20 steps less optimal than the optimal
          tree (implied weights: the consensus of all trees that are 0.73^(19:0)
          less optimal than the optimal tree).
   
        * `Boot`: Bootstrap supports (symmetric resampling, _p_ = 0.33).
   
        * `Jack`: Jackknife supports (_p_ = 0.36).
   
          `Boot` and `Jack` results are reported both as the `freq`uency of splits
          among replicates, and using the `gc` (Groups Present / Contradicted)
          measure (Goloboff _et al_. 2003); frequency columns correspond to
          100\%, 97.5\%, 95\% ... 0\% support; gc columns correspond to 100\%, 95\%,
          ... 0\% present, 5\%, 10\%, ... 100\% contradicted.
   

2. Counts of the condition of each quartet or partition:

     * `Q`: The total number of quartets defined on 22 taxa.
   
     * `N`: The total number of partitions present, counting each tree separately.
   
     * `P1`: The number of partitions in tree 1 (the reconstructed tree).
   
     * `P2`: The number of partitions in tree 2 (the generative tree).
   
     * `s`: The number of quartets or partitions resolved identically in
            each tree.
     * `d`: The number of quartets resolved differently in each tree.
   
     * `d1`: The number of partitions resolved in tree 1, but contradicted by
             tree 2.
   
     * `d2`: The number of partitions resolved in tree 2, but contradicted by
             tree 1.
   
     * `r1`: The number of partitions or quartets resolved in tree 1 that are
             neither present in nor contradicted by tree 2.
   
     * `r2`: The number of partitions or quartets resolved in tree 2 that are
             neither present in nor contradicted by tree 1.
   
     * `u`: The number of quartets that are not resolved in either tree.
   

3. The number of the matrix, from 1 to 100.

Source

Congreve, C. R. & Lamsdell, J. C. (2016). Implied weighting and its utility in palaeontological datasets: a study using modelled phylogenetic matrices. Palaeontology 59(3), 447–465. doi:10.1111/pala.12236.

References

Goloboff, P. A., J. S. Farris, M. Källersjö, B. Oxelman, M. J. Ramírez, and C. A. Szumik. 2003. Improvements to resampling measures of group support. Cladistics 19, 324–332. doi:10.1016/S0748-3007(03)00060-4.

See Also

clMatrices, clReferenceTree.

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