# meanStdGMCMC: Mean standardize the posterior distribution of a G-matrix In evolvability: Calculation of Evolvability Parameters

## Description

Mean standardization of the posterior distribution of a G-matrix

## Usage

 `1` ```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)

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```# 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) ```