MINT {evolqg}

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))