| Title: | A Maximum Likelihood Approach to the Analysis of Modularity |
|---|---|
| Description: | Fit models of modularity to morphological landmarks. Perform model selection on results. Fit models with a single within-module correlation or with separate within-module correlations fitted to each module. |
| Authors: | Anjali Goswami, Tim Lucas, Prabu Sivasubramaniam, John Finarelli |
| Maintainer: | Tim Lucas <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.3 |
| Built: | 2024-10-09 03:58:59 UTC |
| Source: | https://github.com/timcdlucas/emmli |
Calculates the AICc values, model likelihoods, and posterior probabilities of different models of modularity, as described in Goswami and Finarelli (2016).
EMMLi(corr, N_sample, mod, saveAs = NULL, abs = TRUE, pprob = 0.05)EMMLi(corr, N_sample, mod, saveAs = NULL, abs = TRUE, pprob = 0.05)
corr |
Lower triangle or full correlation matrix. n x n square matrix for n landmarks. |
N_sample |
The number of specimens |
mod |
A data frame defining the models. The first column should contain the landmark names. Subsequent columns should define which landmarks are contained within each module with integers, factors or characters. If a landmark should be ignored for a specific model (i.e., it is unintegrated in any module), the element should be NA. |
saveAs |
A character string defining the filename and path for where to save output. If NULL, the output is not saved to file |
abs |
Logical denoting whether absolute values should be used. Default is TRUE, as in Goswami and Finarelli (2016) |
pprob |
posterior probability cutoff for reporting of models. Default is 0.05, as suggested in Goswami and Finarelli (2016) |
The publication describing this analysis is A. Goswami and J. Finarelli (2016) EMMLi: A maximum likelihood approach to the analysis of modularity. Evolution http://onlinelibrary.wiley.com/doi/10.1111/evo.12956/abstract.
A list containing two elements. The first (results) gives the AIC results for each model. The second (rho) gives the within and between module correlations. Optionally, the output is saved to the file defined by the saveAs argument with only models with a posterior probability > 0.01 being saved.
set.seed(1) # Chose a filename and directory for output dir <- tempdir() file <- paste0(dir, 'EMMLiTest.csv') # Examine a correlation matrix and model dataframe dim(macacaCorrel) head(macacaModels) # run EMMLi output <- EMMLi(macacaCorrel, 20, macacaModels, file) unlink(file) # run EMMLi without writing output output <- EMMLi(macacaCorrel, 20, macacaModels) # Raw data example to illustrate pitfalls corrPath <- system.file("extdata", "M1lmcorrel.csv", package = "EMMLi") corr <- read.csv(corrPath, header = FALSE) modelPath <- system.file("extdata", "macaca_landmarklist.csv", package = "EMMLi") mod <- read.csv(modelPath, header = TRUE, row.names = 1) # First column should be character or factor. Subsequent columns integer sapply(mod, class) out <- EMMLi(corr, 42, mod)set.seed(1) # Chose a filename and directory for output dir <- tempdir() file <- paste0(dir, 'EMMLiTest.csv') # Examine a correlation matrix and model dataframe dim(macacaCorrel) head(macacaModels) # run EMMLi output <- EMMLi(macacaCorrel, 20, macacaModels, file) unlink(file) # run EMMLi without writing output output <- EMMLi(macacaCorrel, 20, macacaModels) # Raw data example to illustrate pitfalls corrPath <- system.file("extdata", "M1lmcorrel.csv", package = "EMMLi") corr <- read.csv(corrPath, header = FALSE) modelPath <- system.file("extdata", "macaca_landmarklist.csv", package = "EMMLi") mod <- read.csv(modelPath, header = TRUE, row.names = 1) # First column should be character or factor. Subsequent columns integer sapply(mod, class) out <- EMMLi(corr, 42, mod)
3D coordinates for 61 landmarks on the crania of 42 juvenile Japanese macaque (Macaca fuscata) from the Primate Research Institute at Inuyama, Japan, was aligned and scaled with Generalized Procrustes superimposition. The vector congruence coefficient correlation was then calculated for each pair of landmarks
macacaCorrelmacacaCorrel
A 61 x 61 matrix
#'@seealso macacaModels
Goswami, A. and P. D. Polly. 2010. Methods for studying morphological integration and modularity. Pp. 213-243 in J. Alroy, and E. G. Hunt, eds. Quantitative Methods in Paleobiology. Paleontological Society Special Publications.
Seven models of cranial landmark modularity. These models group the 61
landmarks in macacaCorrel into modules. See Goswami and
Finarelli (2016) for more details.
macacaModelsmacacaModels
A data frame with 61 rows (one for each landmark) and 8 coluumns.
X.1Landmark names (character or factor)
Other columnsModel specification. Integers determine which module each landmark is in. NAs indicate that a landmark is not in any module.
A. Goswami and J. Finarelli (2016) EMMLi: A maximum likelihood approach to the analysis of modularity. Evolution http://onlinelibrary.wiley.com/doi/10.1111/evo.12956/abstract.