Mean standardization of the posterior distribution of a G-matrix

1 | ```
meanStdGMCMC(G_mcmc, means_mcmc)
``` |

`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 |

`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 |

`meanStdGMCMC`

returns the posterior distribution of a mean standardized variance matrix.

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

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)
``` |

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