Function quantifies the degree of phylogenetic morphological covariation between two or more sets of Procrustes shape variables using partial least squares.
phylo.integration(
A,
A2 = NULL,
phy,
partition.gp = NULL,
iter = 999,
seed = NULL,
print.progress = TRUE
)Objects of class "pls" from integration.test return a list of the following:
The estimate of morphological integration: PLS.corr. The mean of pairwise PLS correlations between partitions is used when there are more than two partitions.
The pairwise r.pls, if the number of partitions is greater than 2.
The empirically calculated P-value from the resampling procedure.
The multivariate effect size associated with sigma.d.ratio.
The singular vectors of the left (x) block (for 2 modules only).
The singular vectors of the right (y) block (for 2 modules only).
The correlation coefficients found in each random permutation of the resampling procedure.
Values of left (x) block projected onto singular vectors (for 2 modules only).
Values of right (y) block projected onto singular vectors (for 2 modules only).
The singular value decomposition of the cross-covariances (for 2 modules only).
Input values for the left block (for 2 modules only).
Input values for the right block (for 2 modules only).
Left block (matrix) found from A1 (for 2 modules only).
Right block (matrix) found from A2 (for 2 modules only).
The phylogenetic transformation matrix, needed for certain other analyses.
The number of random permutations used in the resampling procedure.
The match call.
A 2D array (n x [p1 x k1]) or 3D array (p1 x k1 x n) containing Procrustes shape variables for the first block
An optional 2D array (n x [p2 x k2]) or 3D array (p2 x k2 x n) containing Procrustes shape variables for the second block
A phylogenetic tree of class = "phylo" - see read.tree in library ape
A list of which landmarks (or variables) belong in which partition: (e.g. A, A, A, B, B, B, C, C, C). This is required when only 1 dataset provided.
Number of iterations for significance testing
An optional argument for setting the seed for random permutations of the resampling procedure. If left NULL (the default), the exact same P-values will be found for repeated runs of the analysis (with the same number of iterations). If seed = "random", a random seed will be used, and P-values will vary. One can also specify an integer for specific seed values, which might be of interest for advanced users.
A logical value to indicate whether a progress bar should be printed to the screen. This is helpful for long-running analyses.
Dean Adams
The function quantifies the degree of phylogenetic morphological integration between two or more sets of Procrustes shape variables.
The approach is based on a Brownian motion model of evolution. It is
assumed that the landmarks have previously been aligned using
Generalized Procrustes Analysis (GPA) [e.g., with gpagen].
The function estimates the degree of morphological covariation between two or sets of variables while accounting for phylogeny using partial least squares (Adams and Felice 2014), and under a Brownian motion model of evolution. If more than two partitions are defined, the average pairwise PLS correlation is utilized as the test statistic. The observed value is statistically assessed using permutation, where data for one partition are permuted relative to the other partitions. In addition, a multivariate effect size describing the strength of the effect is estimated from the empirically-generated sampling distribution (see details in Adams and Collyer 2019). Note that this permutation is performed on phylogenetically- transformed data, so that the probability of phylogenetic association of A vs. B is similar to that of B vs. A: i.e., prob(A,B|phy)~prob(B,A|phy); thus, shuffling the correct exchangeable units under the null hypothesis of no integration (Adams and Collyer 2018).
Input for the analysis can take one of two forms. First, one can input a single dataset (as a matrix or 3D array, along with a vector describing which variables correspond to which partitions (for the case of a 3D array, which landmarks belong to which partitions is specified). Alternatively, when evaluating the integration between two structures or partitions, two datasets may be provided.
The generic functions, print, summary, and plot all work with phylo.integration.
The generic function, plot, produces a two-block.pls plot. This function calls plot.pls, which produces an ordination plot.
An additional argument allows one to include a vector to label points. Starting with version 3.1.0, warpgrids are no longer available with plot.pls
but after making a plot, the function returns values that can be used with picknplot.shape or a combination of
shape.predictor and plotRefToTarget to visualize shape changes in the plot (via warpgrids).
two.b.pls and compare.pls Note that phylo.integration performed on two matrices or arrays returns the same results as a phylogenetic variation of
two.b.pls. It might be of interest with 3+ modules to perform separate phylogenetic integration tests
between all pairwise comparisons of modules. This can be done, test by test, and the levels of integration can be compared with
compare.pls. Such results are different than using the average amount of integration when more than two modules
are input, as found with phylo.integration.
Compared to previous versions of geomorph, users might notice differences in effect sizes. Previous versions used z-scores calculated with expected values of statistics from null hypotheses (sensu Collyer et al. 2015); however Adams and Collyer (2016) showed that expected values for some statistics can vary with sample size and variable number, and recommended finding the expected value, empirically, as the mean from the set of random outcomes. Geomorph 3.0.4 and subsequent versions now center z-scores on their empirically estimated expected values and where appropriate, log-transform values to assure statistics are normally distributed. This can result in negative effect sizes, when statistics are smaller than expected compared to the average random outcome. For ANOVA-based functions, the option to choose among different statistics to measure effect size is now a function argument.
Adams, D.C. and R. Felice. 2014. Assessing phylogenetic morphological integration and trait covariation in morphometric data using evolutionary covariance matrices. PLOS ONE. 9(4):e94335.
Collyer, M.L., D.J. Sekora, and D.C. Adams. 2015. A method for analysis of phenotypic change for phenotypes described by high-dimensional data. Heredity. 115:357-365.
Adams, D.C. and M.L. Collyer. 2016. On the comparison of the strength of morphological integration across morphometric datasets. Evolution. 70:2623-2631.
Adams, D.C. and M.L. Collyer. 2018. Multivariate comparative methods: evaluations, comparisons, and recommendations. Systematic Biology. 67:14-31.
Adams, D.C. and M.L. Collyer. 2019. Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data. Evolution. 73:2352-2367.
integration.test, modularity.test, and
two.b.pls
if (FALSE) {
data(plethspecies)
Y.gpa <- gpagen(plethspecies$land) #GPA-alignment
land.gps <- c("A","A","A","A","A","B","B","B","B","B","B")
IT <- phylo.integration(Y.gpa$coords, partition.gp = land.gps,
phy = plethspecies$phy)
summary(IT) # Test summary
P <- plot(IT) # PLS plot
# Block 1
minx <- min(P$plot_args$x)
maxx <- max(P$plot_args$x)
preds <- shape.predictor(P$A1,
x = P$plot.args$x,
min = minx, max = maxx)
plotRefToTarget(mshape(P$A1), preds$min)
plotRefToTarget(mshape(P$A1), preds$max)
# Block 2
miny <- min(P$plot_args$y)
maxy <- max(P$plot_args$y)
preds <- shape.predictor(P$A2,
x = P$plot.args$y,
min = miny, max = maxy)
plotRefToTarget(mshape(P$A2), preds$min)
plotRefToTarget(mshape(P$A2), preds$max)
### Visualize shape variation using picknplot.shape Because picknplot
### requires user decisions, the following example
### is not run.
### For detailed options, see the picknplot help file
# picknplot.shape(P)
}
Run the code above in your browser using DataLab