meanStdGMCMC: Mean standardize the posterior distribution of a G-matrix

Description Usage Arguments Value Author(s) Examples

Description

Mean standardization of the posterior distribution of a G-matrix

Usage

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meanStdGMCMC(G_mcmc, means_mcmc)

Arguments

G_mcmc

posterior distribution of a variance matrix in the form of a table. Each row in the table must be one iteration of the posterior distribution (or bootstrap distribution). Each iteration of the matrix must be on the form as given by c(x), where x is a matrix. A posterior distribution of a matrix in the slot VCV of a object of class MCMCglmm is by default on this form.

means_mcmc

posterior distribution of a vector of means in the form of a table. Each row in the table must be one iteration of the posterior distribution (or bootstrap distribution). A posterior distribution of a mean vector in the slot Sol of a object of class MCMCglmm is by default on this form.

Value

meanStdGMCMC returns the posterior distribution of a mean standardized variance matrix.

Author(s)

Geir H. Bolstad geir.h.bolstad@ntnu.no

Examples

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# Simulating a posterior distribution 
# (or bootstrap distribution) of a G-matrix:
G = matrix(c(1, 1, 0, 1, 4, 1, 0, 1, 2), ncol = 3)
G_mcmc = sapply(c(G), function(x) rnorm(10, x, 0.01)) 
G_mcmc = t(apply(G_mcmc, 1, function(x){
  G = matrix(x, ncol=sqrt(length(x)))
  G[lower.tri(G)] = t(G)[lower.tri(G)]
  c(G)
}))

# Simulating a posterior distribution 
# (or bootstrap distribution) of trait means:
means = c(1, 1.4, 2.1)
means_mcmc = sapply(means, function(x) rnorm(10, x, 0.01)) 

# Mean standardizing the G-matrix:
meanStdGMCMC(G_mcmc, means_mcmc)


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