evolqg: MINT – R documentation

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MINT

Modularity and integration analysis tool

Description

Combines and compares many modularity hypothesis to a covariance matrix. Comparison values are ajusted to the number os zeros in the hypothesis using a linear regression. Best hypothesis can be assessed using a jack-knife procedure.

Usage

MINT(
  c.matrix,
  modularity.hypot,
  significance = FALSE,
  sample.size = NULL,
  iterations = 1000
)

JackKnifeMINT(
  ind.data,
  modularity.hypot,
  n = 1000,
  leave.out = floor(dim(ind.data)[1]/10),
  ...
)

Arguments

`c.matrix`	Correlation or covariance matrix
`modularity.hypot`	Matrix of hypothesis. Each line represents a trait and each column a module. if modularity.hypot[i,j] == 1, trait i is in module j.
`significance`	Logical. Indicates if goodness of fit test should be performed.
`sample.size`	sample size in goodness of fit simulations via MonteCarlo
`iterations`	number os goodness of fit simulations
`ind.data`	Matrix of residuals or indiviual measurments
`n`	number of jackknife resamplings
`leave.out`	number of individuals to be left out of each jackknife, default is 10%
`...`	aditional arguments to be passed to raply for the jackknife

Note

Hypothesis can be named as column names, and these will be used to make labels in the output.

Jackknife will return the best hypothesis for each sample.

References

Marquez, E.J. 2008. A statistical framework for testing modularity in multidimensional data. Evolution 62:2688-2708.

Parsons, K.J., Marquez, E.J., Albertson, R.C. 2012. Constraint and opportunity: the genetic basis and evolution of modularity in the cichlid mandible. The American Naturalist 179:64-78.

http://www-personal.umich.edu/~emarquez/morph/doc/mint_man.pdf

Examples

# Creating a modular matrix:
modules = matrix(c(rep(c(1, 0, 0), each = 5),
                 rep(c(0, 1, 0), each = 5),
                 rep(c(0, 0, 1), each = 5)), 15)
                 
cor.hypot = CreateHypotMatrix(modules)[[4]]
hypot.mask = matrix(as.logical(cor.hypot), 15, 15)
mod.cor = matrix(NA, 15, 15)
mod.cor[ hypot.mask] = runif(length(mod.cor[ hypot.mask]), 0.8, 0.9) # within-modules
mod.cor[!hypot.mask] = runif(length(mod.cor[!hypot.mask]), 0.1, 0.2) # between-modules
diag(mod.cor) = 1
mod.cor = (mod.cor + t(mod.cor))/2 # correlation matrices should be symmetric

# True hypothesis and a bunch of random ones.
hypothetical.modules = cbind(modules, matrix(sample(c(1, 0), 4*15, replace=TRUE), 15, 4))

# if hypothesis columns are not named they are assigned numbers
colnames(hypothetical.modules) <- letters[1:7]
 
MINT(mod.cor, hypothetical.modules)

random_var = runif(15, 1, 10)
mod.data = mvtnorm::rmvnorm(100, sigma = sqrt(outer(random_var, random_var)) * mod.cor)
out_jack = JackKnifeMINT(mod.data, hypothetical.modules, n = 50)

library(ggplot2)

ggplot(out_jack, aes(rank, corrected.gamma)) + geom_point() + 
       geom_errorbar(aes(ymin = lower.corrected, ymax = upper.corrected))

evolqg

Tools for Evolutionary Quantitative Genetics

v0.2-8

MIT + file LICENSE

Authors

Ana Paula Assis, Diogo Melo, Edgar Zanella, Fabio Andrade Machado, Guilherme Garcia

Initial release

2020-11-14