Nothing
#' Measuring Disparity in R
#'
#' A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics (Guillerme (2018) <doi:10.1111/2041-210X.13022>). The package also provides functions for looking at disparity in a serial way (e.g. disparity through time - Guillerme and Cooper (2018) <doi:10.1111/pala.12364>) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.
#'
#' @name dispRity-package
#'
#' @docType package
#'
#' @author Thomas Guillerme <guillert@@tcd.ie>
#'
#' @concept disparity ordination phylogeny cladistic morphometric ecology
#'
NULL
#' Beck and Lee 2014 datasets
#'
#' Example datasets from Beck and Lee 2014.
#'
#' \itemize{
#' \item \code{BeckLee_tree} A phylogenetic tree with 50 living and fossil taxa
#' \item \code{BeckLee_mat50} The ordinated matrix based on the 50 taxa cladistic distances
#' \item \code{BeckLee_mat99} The ordinated matrix based on the 50 taxa + 49 nodes cladistic distances
#' \item \code{BeckLee_ages} A list of first and last occurrence data for fossil taxa
#' \item \code{BeckLee_disparity} a \code{dispRity} object with estimated sum of variances in 120 time bins, boostrapped 100 times from the Beck and Lee data
#' }
#'
#' @format three matrices and one phylogenetic tree.
#' @doi \url{https://doi.org/10.1098/rspb.2014.1278}
#' @references Beck RMD & Lee MSY. 2014. Ancient dates or accelerated rates?
#' Morphological clocks and the antiquity of placental mammals.
#' Proc. R. Soc. B 2014 281 20141278; DOI: 10.1098/rspb.2014.1278
#' @name BeckLee
#' @aliases BeckLee_tree BeckLee_mat50 BeckLee_mat99 BeckLee_ages
#' @seealso BeckLee_disparity disparity
NULL
#' disparity
#'
#' An example of a \code{dispRity} object.
#'
#' This matrix is based on the \code{\link{BeckLee}} dataset and split into seven continuous subsets (\code{\link{chrono.subsets}}).
#' It was bootstrapped 100 times (\code{\link{boot.matrix}}) with four rarefaction levels.
#' Disparity was calculated as the \code{\link[stats]{median}} of the \code{\link{centroids}} (\code{\link{dispRity}}).
#'
#' @format one \code{dispRity} object.
#' @name disparity
#' @seealso BeckLee_disparity BeckLee
#' @examples
# set.seed(42)
#' \dontrun{
#' ## Loading the data
#' data(BeckLee_mat99)
#' data(BeckLee_tree)
#' data(BeckLee_ages)
#'
#' ## Creating the 7 subsets
#' subsets <- chrono.subsets(BeckLee_mat99, BeckLee_tree,
#' time = seq(from = 30, to = 90, by = 10),
#' method = "continuous", model = "ACCTRAN",
#' FADLAD = BeckLee_ages)
#'
#' ## Bootstrapping and rarefying
#' bootstraps <- boot.matrix(subsets, bootstraps = 100,
#' rarefaction = c(20, 15, 10, 5))
#'
#' ## Calculating disparity
#' disparity <- dispRity(bootstraps, metric = c(median, centroids))
#' }
# save(disparity, file = "../Data/disparity.rda")
NULL
#' BeckLee_disparity
#'
#' An example of a \code{dispRity} object.
#'
#' This matrix is based on the \code{\link{BeckLee}} dataset and split into 120 continuous subsets (\code{\link{chrono.subsets}}).
#' It was bootstrapped 100 times (\code{\link{boot.matrix}}) with four rarefaction levels.
#' Disparity was calculated as the \code{\link[base]{sum}} of the \code{\link{variances}} (\code{\link{dispRity}}).
#'
#' @format one \code{dispRity} object.
#' @name BeckLee_disparity
#' @seealso BeckLee disparity
#' @examples
# set.seed(42)
#' \dontrun{
#' ## Loading the data
#' data(BeckLee_mat99)
#' data(BeckLee_tree)
#' data(BeckLee_ages)
#'
#' ## Creating the 7 subsets
#' subsets <- chrono.subsets(BeckLee_mat99, BeckLee_tree,
#' time = seq(from = 0, to = 120, by = 1),
#' method = "continuous", model = "proximity",
#' FADLAD = BeckLee_ages)
#'
#' ## Bootstrapping and rarefying
#' bootstraps <- boot.matrix(subsets, bootstraps = 100)
#'
#' ## Calculating disparity
#' BeckLee_disparity <- dispRity(bootstraps, metric = c(sum, variances))
#' }
# save(BeckLee_disparity, file = "../Data/BeckLee_disparity.rda")
NULL
#' @title Demo datasets
#'
#' @description A set six trait spaces with different groups and different dimensions.
#'
#' @details
#'
#' The content of these datasets and the pipeline to build them is described in details in Guillerme et al 2020.
#'
#' \itemize{
#' \item \code{beck} A palaeobiology study of mammals. The data is a 105 dimensions ordination (PCO) of the distances between 106 mammals based on discrete morphological characters.
#' \item \code{wright} A palaeobiology study of crinoids. The data is a 41 dimensions ordination (PCO) of the distances between 42 crinoids based on discrete morphological characters.
#' \item \code{marcy} A geometric morphometric study of gophers (rodents). The data is a 134 dimensions ordination (PCA) the Procrustes superimposition of landmarks from 454 gopher skulls.
#' \item \code{hopkins} A geometric morphometric study of trilobites. The data is a 134 dimensions ordination (PCA) the Procrustes superimposition of landmarks from 46 trilobites cephala.
#' \item \code{jones} An ecological landscape study. The data is a 47 dimensions ordination (PCO) of the Jaccard distances between 48 field sites based on species composition.
#' \item \code{healy} A life history analysis of the pace of life in animals. The data is a 6 dimensions ordination (PCA) of 6 life history traits from 285 animal species.
#' }
#'
#' @source \doi{https://doi.org/10.1002/ece3.6452}
#' @references Guillerme T, Puttick MN, Marcy AE, Weisbecker V. \bold{2020} Shifting spaces: Which disparity or dissimilarity measurement best summarize occupancy in multidimensional spaces?. Ecol Evol. 2020;00:1-16. (doi:10.1002/ece3.6452)
#' @references Beck, R. M., & Lee, M. S. (2014). Ancient dates or accelerated rates? Morphological clocks and the antiquity of placental mammals. Proceedings of the Royal Society B: Biological Sciences, 281(1793), 20141278.
#' @references Wright, D. F. (2017). Bayesian estimation of fossil phylogenies and the evolution of early to middle Paleozoic crinoids (Echinodermata). Journal of Paleontology, 91(4), 799-814.
#' @references Marcy, A. E., Hadly, E. A., Sherratt, E., Garland, K., & Weisbecker, V. (2016). Getting a head in hard soils: convergent skull evolution and divergent allometric patterns explain shape variation in a highly diverse genus of pocket gophers (Thomomys). BMC evolutionary biology, 16(1), 207.
#' @references Hopkins, M.J. and Pearson, J.K., 2016. Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics. Palaeontologia Electronica, 19(3), pp.1-54.
#' @references Jones, N. T., Germain, R. M., Grainger, T. N., Hall, A. M., Baldwin, L., & Gilbert, B. (2015). Dispersal mode mediates the effect of patch size and patch connectivity on metacommunity diversity. Journal of Ecology, 103(4), 935-944.
#' @references Healy, K., Ezard, T.H., Jones, O.R., Salguero-Gomez, R. and Buckley, Y.M., 2019. Animal life history is shaped by the pace of life and the distribution of age-specific mortality and reproduction. Nature ecology & evolution, p.1.
#'
#' @name demo_data
#'
#' @examples
#' data(demo_data)
#'
#' ## Loading the Beck and Lee 2014 demo data
#' demo_data$beck
#'
#' ## Loading the Wright 2017 demo data
#' demo_data$wright
#'
#' ## Loading the Marcy et al. 2015 demo data
#' demo_data$marcy
#'
#' ## Loading the Hopkins and Pearson 2016 demo data
#' demo_data$hopkins
#'
#' ## Loading the Jones et al. 2015 demo data
#' demo_data$jones
#'
#' ## Loading the Healy et al. 2019 demo data
#' demo_data$healy
NULL
#' @title Charadriiformes
#' @name charadriiformes
#'
#' @description An example of a \code{\link[MCMCglmm]{MCMCglmm}} model.
#'
#' @details This dataset is based on a random subset of 359 Charadriiformes (gulls, plovers and sandpipers) from Cooney et al 2017 and trees from Jetz et al 2012.
#' It contains:
#' \itemize{
#' \item \code{data} A \code{"data.frame"} .
#' \item \code{tree} A consensus tree of 359 charadriiformes species (\code{"phylo"}).
#' \item \code{posteriors} The posteriors from a \code{"MCMCglmm"} model (see example below).
#' \item \code{tree_distribution} A random distribution of 10 trees of the 359 charadriiformes species (\code{"multiPhylo"}).
#' }
#'
#' @format one \code{data.frame}, one \code{phylo} and one \code{MCMCglmm}.
#'
#' @references Cooney CR, Bright JA, Capp EJ, Chira AM,Hughes EC, Moody CJ, Nouri LO, Varley ZK, Thomas GH. Mega-evolutionary dynamics of the adaptive radiation of birds. Nature. 2017 Feb;542(7641):344-7.
#' @references Jetz W, Thomas GH, Joy JB, Hartmann K, Mooers AO. The global diversity of birds in space and time. Nature. 2012 Nov;491(7424):444-8.
#'
#' @examples
# set.seed(42)
#' \dontrun{
#' ## Reproducing the MCMCglmm model
#' require(MCMCglmm)
#' data(charadriiformes)
#'
#' ## Setting up the model parameters:
#' ## 1 - The formula (the first three PC axes)
#' model_formula <- cbind(PC1, PC2, PC3) ~ trait:clade-1
#' ## 2 - The residual term
#' model_residuals <- ~us(trait):units
#' ## 3 - The random terms
#' ## (one per clade and one for the whole phylogeny)
#' model_randoms <- ~ us(at.level(clade,1):trait):animal
#' + us(at.level(clade,2):trait):animal
#' + us(at.level(clade,3):trait):animal
#' + us(trait):animal
#'
#' ## Flat priors for the residuals and random terms
#' flat_priors <- list(
#' ## The residuals priors
#' R = list(
#' R1 = list(V = diag(3), nu = 0.002)),
#' ## The random priors (the phylogenetic terms)
#' G = list(
#' G1 = list(V = diag(3), nu = 0.002),
#' G2 = list(V = diag(3), nu = 0.002),
#' G3 = list(V = diag(3), nu = 0.002),
#' G4 = list(V = diag(3), nu = 0.002)))
#'
#' ## Run the model for 110000 iterations
#' ## sampled every 100 with a burnin (discard)
#' ## of the first 10000 iterations)
#' model <- MCMCglmm(formula = model_formula,
#' rcov = model_residual,
#' random = model_randoms,
#' family = rep("gaussian", 3),
#' prior = flat_priors,
#' nitt = 110000,
#' burnin = 10000,
#' thin = 100,
#' pedigree = charadriiformes$tree,
#' data = charadriiformes$data)
#' }
# charadriiformes <- list(data = charadriiformes$data, tree = charadriiformes$tree, model = model)
# save(charadriiformes, file = "../Data/charadriiformes.rda")
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.