README.Rmd

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output: github_document
---

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```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  dpi = 400
)
```

# morphospace <img src="man/figures/morphosp_hex.png" align="right" width="200"/>


The goal of `morphospace` is to enhance representation and heuristic exploration of multivariate ordinations of shape data. This package can handle the most common types of shape data working in integration with other widely used R packages such as `Morpho` (Schlager 2017), `geomorph` (Adams et al. 2021), `shapes` (Dryden 2019), `Momocs` (Bonhomme et al. 2014) and `mvMORPH` (Clavel et al.2015), which cover other more essential steps in the geometric morphometrics pipeline (e.g. importation, normalization, statistical analysis, phylogenetic modeling).

Below there is broad-strokes account of the `morphospace` capacities; for more specific guidance, refer to [General usage](https://millacarmona.github.io/morphospace/articles/General-usage.html) and [Worked examples](https://millacarmona.github.io/morphospace/articles/Worked-examples.html).


## Installation


You can install the development and CRAN versions of `morphospace` from [GitHub](https://github.com/) with:

``` r
# Development version
# install.packages("devtools")
devtools::install_github("millacarmona/morphospace")

# CRAN version
# install.packages("morphospace") #not yet ready
```

## Concept

The basic idea behind `morphospace` is to build empirical morphospaces using multivariate ordination methods, then use the resulting ordination as a reference frame in which elements representing different aspects of morphometric variation are projected. These elements are added to both graphic representations and objects as consecutive 'layers' and list slots, respectively, using the `%>%` pipe operator from `magrittr` (Bache & Wickham 2022).

The starting point of the `morphospace` workflow is a set of shapes (i.e. morphometric data that is already free of variation due to differences in orientation, position and scale). These are fed to the `mspace` function, which generates a morphospace using a variety of multivariate methods related to Principal Component Analysis. This general workflow is outlined below using the `tails` data set from Fasanelli et al. (2022), which contains tail shapes from 281 specimens belonging to 13 species of the genus *Tyrannus*.


```{r, message = FALSE}
library(morphospace)
library(geomorph)
library(Morpho)
library(Momocs)
library(magrittr)
library(rgl)
```


```{r}
# Load tail data
data("tails")

shapes <- tails$shapes
spp <- tails$data$species
wf <- tails$links
phy <- tails$tree

# Generate morphospace
mspace(shapes, links = wf, cex.ldm = 5)
```


The ordination produced by `mspace` is used as a reference frame in which scatter points, convex hulls / confidence ellipses, a phylogeny, a set of morphometric axes or a landscape surface can be projected using the `proj_*` functions:


```{r}
# Get mean shapes of each species
spp_shapes <- expected_shapes(shapes = tails$shapes, x = tails$data$species)

# Generate morphospace and project:
msp <- mspace(shapes = shapes, links = wf, cex.ldm = 5) %>% 
  # scatter points
  proj_shapes(shapes = shapes, col = spp) %>% 
  # convex hulls enclosing groups
  proj_groups(shapes = shapes, groups = spp, alpha = 0.5) %>%
  # phylogenetic relationships
    proj_phylogeny(shapes = spp_shapes, tree = phy, lwd = 1.5, 
                 col.tips = match(phy$tip.label, levels(spp)))

```


Once the `"mspace"` object has been created, the `plot_mspace` function can be used to either regenerate/modify the plot, add a legend, or to combine morphometric axes with other non-shape variables to produce 'hybrid' morphospaces. For example, PC1 can be plotted against size to explore allometric patterns.


```{r}
# Plot PC1 against log-size, add legend
plot_mspace(msp, x = tails$sizes, axes = 1, nh = 6, nv = 6, cex.ldm = 4, 
            alpha.groups = 0.5, col.points = spp, col.groups = 1:nlevels(spp), 
            phylo = FALSE, xlab = "Log-size", legend = TRUE)
```

Or against taxonomic classification to assess patterns of intra- and/or interspecific variation.


```{r}
# Plot PC1 against species classification
plot_mspace(msp, x = spp, axes = 1, nh = 6, nv = 6, cex.ldm = 4, boxplot.groups = TRUE,
            alpha.groups = 0.5, col.points = spp, col.groups = 1:nlevels(spp), 
            phylo = FALSE, xlab = "Log-size", legend = TRUE)

```


Alternatively, ordination axes can be combined with a phylogenetic tree to create a phenogram:


```{r}
# Plot vertical phenogram using PC1, add a legend
plot_mspace(msp, y = phy, axes = 1, nh = 6, nv = 6, cex.ldm = 4, 
            col.groups = 1:nlevels(spp), ylab = "Time", legend = TRUE)
```

`morphospace` can also handle closed outlines (in the form of elliptic Fourier coefficients) and 3D landmark data, as shown below briefly using the `shells` and `shells3D` data sets:


```{r}
# Load data
data("shells")

shapes <- shells$shapes
spp <- shells$data$species

# Generate morphospace
mspace(shapes, mag = 1, nh = 5, nv = 4, bg.model = "light gray") %>%
  proj_shapes(shapes = shapes, col = spp) %>%
  proj_groups(shapes = shapes, groups = spp, alpha = 0.5, ellipse = TRUE)
```


```{r, include = FALSE, message=FALSE}
par3d(userMatrix = 
        matrix(c(-0.92221391,-0.37156740,-0.10704762,0,-0.37703809,
                 0.92551011,0.03568961,0,0.08581252,0.07327446,
                 -0.99361324,0,0,0,0,1), nrow = 4, ncol = 4, byrow = TRUE)
)
```

```{r}
# Load data
data("shells3D")

shapes <- shells3D$shapes
spp <- shells3D$data$species
mesh_meanspec <- shells3D$mesh_meanspec

# Generate surface mesh template
meanspec_shape <- shapes[,,findMeanSpec(shapes)]
meanmesh <- tps3d(x = mesh_meanspec, 
                  refmat = meanspec_shape, 
                  tarmat = expected_shapes(shapes))

# Generate morphospace
mspace(shapes, mag = 1, bg.model = "gray", cex.ldm = 0, template = meanmesh, 
       adj_frame = c(0.9, 0.85)) %>%
  proj_shapes(shapes = shapes, col = spp, pch = 16) %>%
  proj_groups(shapes = shapes, groups = spp, alpha = 0.3)

```


Aside from working with these types of morphometric data, `morphospace` provides functions to perform some useful shape operations, use TPS interpolation of curves/meshes to improve visualizations, and supports a variety of multivariate methods (bgPCA, phylogenetic PCA, PLS, phylogenetic PLS) to produce ordinations. Integration with linear models and ordinations generated with other packages (`geomorph`, `Morpho`, `Momocs`, `mvMORPH`, `RRPP`, `phytools`) is also possible. For these and other options and details, go to [General usage](https://millacarmona.github.io/morphospace/articles/General-usage.html) and [Worked examples](https://millacarmona.github.io/morphospace/articles/Worked-examples.html).


## Update 1 (August 2022)

- Different behavior for `proj_shapes` (now replaces `mspace$x` with the actual scores being projected) and `proj_axis` (now adds one or more axes into an `mspace$shape_axis`).

- New `ellipses_by_groups_2D` (uses `car::ellipse`) function as an option for `proj_groups` and `plot_mspace`.

- Morphospaces without background shape models are now an option (for both `mspace` and `plot_mspace`).

- `plot_mspace` now regenerates the original mspace plot by default (`proj_*` functions were modified such that all the relevant graphical parameters are inherited downstream to `plot_mspace`), has further flexibility regarding hybrid morphospaces (`plot_phenogram` has been updated) and allows adding a legend (and some various bugs were fixed as well).

- Univariate morphospaces and associated density distributions are now an option (all the `mspace` workflow functions have been modified accordingly, especially `proj_shapes` and `proj_groups`).
 
- `consensus` and `expected_shapes` have been merged in a single function (the name `expected_shapes` was retained as the former was clashing with `ape::consensus`), which can handle both factors and numerics.

- Both `detrend_shapes` and `expected_shapes` can now calculate phylogenetically-corrected coefficients for interspecific data sets (Revell 2009).


## Update 2 (August 2023)

- The structure of `"mspace"` objects has been reorganized and now contain 3 main slots: `$ordination` (multivariate ordination details), `$projected` (elements added using `proj_*` functions) and `$plotinfo` (used for regeneration using `plot_mspace`). This has been complemented with a `print` method for the `"mspace"` class.

- New `proj_landscape` function has been added to represent adaptive surfaces interpolated from functional or performance indices (although can be used for any numerical variable). 

- `proj_consensus` has been removed.

- New `extract_shapes` function for extracting synthetic shapes from `"mspace"` objects (background shape models, shapes along ordination axes, or specific coordinates selected interactively).

- New `burnaby` function, implementing Burnaby's approach for standardization of morphometric data by computing a shape subspace orthogonal to an arbitrary vector or variable

- New `phyalign_comp` function, implementing Phylogenetically aligned component analysis, which finds the linear combination of variables maximizing covariation between trait variation and phylogenetic structure (Collyer & Adams 2021). Still a work in progress.

- Several internal adjustments have been introduced to the `mspace`, `proj_*` and `plot_mspace` functions in order to improve visualization and make the workflow more flexible. 

- Legends created using `plot_mspace` have been improved, and scale bars for interpreting landscapes have also been made available.


## Update 3 (February 2024)

Significant changes aimed at enhancing integration with other GM/MV R packages and improving procedures involving phylogenetic data, as well as a couple of new features:

- The internal behavior of the `mspace` workflow has been modified so objects containing multivariate ordinations produced by `geomorph`, `Morpho`, `mvMORPH`, `Momocs` and `phytools` can be now used as input.

- `mspace` can now be fed with objects containing multivariate ordinations directly. This is implemented through an alternative combination of arguments for the `mspace` function (`ord` + `datype` as an alternative to `shapes` + `FUN` + `...`).

- `ax_transformation` (and by extension `proj_axis`) and `detrend_shapes` can now be fed with objects containing a linear model fitted using functions from `geomorph`, `RRPP` and `mvMORPH`. Internal phylogenetic correction of linear coefficients in `detrend_shapes` has been abandoned, relying now on the results of the (phylogenetic) linear model provided by the user.

- `phy_prcomp` and the experimental `phyalign_comp` have been removed (users interested in these methods should refer to `phytools::phyl.pca`, `geomorph::gm.prcomp` and/or `mvMORPH::mvgls.pca`).

- Estimation of ancestral shapes (performed internally by `expected_shapes`, `detrend_shapes`, `proj_phylogeny`, `pls2b`, `pls_shapes`, `burnaby` and `plot_mspace`) now relies on `mvMORPH::mvgls`, which has the advantage of allowing estimation under evolutionary models other than Brownian motion. In addition, ancestral character estimation of discrete non-shape variables (attempted internally by the phylogenetic version of `pls2b` (and by extension `pls_shapes`) and `plot_mspace` in certain situations) is performed under a simple Equal rates model via `ape::ace`.

- Introduction of boxplots and violin plots for combining shape ordination axes with categorical variables via `plot_mspace`.

- Tip and node labels can now be included in phylomorphospaces, phenograms and hybrid phylomorphospaces.

## Update 4 (February 2025)

A few relevant changes:

- `morphospace` can now generate Pareto rank ratio surfaces (Deakin et al. 2022), thanks to the code provided by Will Deakin. `proj_landscape` has been modified accordingly and can now take either two or more functions or two or more functional metrics (arguments `FUN` and `X`, respectively; see `?proj_landscape`).

- new `proj_pfront` function for projecting Pareto fronts (i.e., the subset of shapes optimizing two or more antagonistic measures of performance).

- `proj_landscape` can now accept surfaces created with `Morphoscape` (Dickson et al. 2023) through the argument `obj`.

- options for controlling the aspect ratio of morphospaces have been included (argument `asp` in `mspace`).


If you find any bugs please send me an email at `pablomillac@gmail.com`. Thanks!!


## References


Adams D.C., Collyer M.L., Kaliontzopoulou A., & Baken E.K. (2021). *geomorph: Software for geometric morphometric analyses*. R package version 4.0.2. <https://cran.r-project.org/package=geomorph>.

Bache S.F., & Wickham H. (2022). *magrittr: A Forward-Pipe Operator for R*. R package version 2.0.3. <https://CRAN.R-project.org/package=magrittr>.

Bonhomme V., Picq S., Gaucherel C., & Claude J. (2014). *Momocs: Outline Analysis Using R*. Journal of Statistical Software, 56(13), 1-24. <https://www.jstatsoft.org/v56/i13/>.

Clavel, J., Escarguel, G., & Merceron, G. (2015). *mvMORPH: an R package for fitting multivariate evolutionary models to morphometric data*. Methods in Ecology and Evolution, 6(11), 1311-1319. https://doi.org/10.1111/2041-210X.12420.

Collyer, M. L., & Adams, D. (2021). *Phylogenetically aligned component analysis*. Methods in Ecology and Evolution, 12(2), 359-372. https://doi.org/10.1111/2041-210X.13515.

Deakin, W. J., Anderson, P. S., den Boer, W., Smith, T. J., Hill, J. J., Rücklin, M., Donoghue, P. C. J.  & Rayfield, E. J. (2022). *Increasing morphological disparity and decreasing optimality for jaw speed and strength during the radiation of jawed vertebrates*. Science Advances, 8(11), eabl3644. <https://doi.org/10.1126/sciadv.abl3644>.

Dickson, B. V., Pierce, S., & Greifer, N. 2023. *Morphoscape: computation and visualization of adaptive landscapes*. R package version 1.0.2. <https://CRAN.R-project.org/package=Morphoscape>

Dryden, I.L. (2019). *shapes: statistical shape analysis*. R package version 1.2.5. <https://CRAN.R-project.org/package=shapes>.

Fasanelli M.N., Milla Carmona P.S., Soto I.M., & Tuero, D.T. (2022). *Allometry, sexual selection and evolutionary lines of least resistance shaped the evolution of exaggerated sexual traits within the genus* Tyrannus. Journal of Evolutionary Biology, in press. <https://doi.org/10.1111/jeb.14000>.

Revell, L.J. (2009). *Size-correction and principal components for interspecific comparative studies*. Evolution, 63, 3258-3268 <https://doi.org/10.1111/j.1558-5646.2009.00804.x>.

Schlager S. (2017). *Morpho and Rvcg - Shape Analysis in R*. In Zheng G., Li S., Szekely G. (eds.), *Statistical Shape and Deformation Analysis*, 217-256. Academic Press. <https://doi.org/10.1016/B978-0-12-810493-4.00011-0>.