MINT: Modularity and integration analysis tool

In lem-usp/EvolQG: Evolutionary Quantitative Genetics

Modularity and integration analysis tool

Description

Combines and compares many modularity hypothesis to a covariance matrix. Comparison values are adjusted to the number of 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 individual measurements
n	number of jackknife samples
leave.out	number of individuals to be left out of each jackknife, default is 10%
...	additional arguments to be passed to raply for the jackknife

Value

Dataframe with ranked hypothesis, ordered by the corrected gamma value Jackknife will return the best hypothesis for each sample.

Note

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

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))

lem-usp/EvolQG documentation built on April 14, 2024, 6:21 a.m.