View source: R/evolvabilityBetaMCMC.R
evolvabilityBetaMCMC | R Documentation |
evolvabilityBetaMCMC
calculates (unconditional) evolvability (e),
respondability (r), conditional evolvability (c), autonomy (a) and
integration (i) from selection gradients given the posterior distribution of
an additive-genetic variance matrix. These measures and their meanings are
described in Hansen and Houle (2008).
evolvabilityBetaMCMC(G_mcmc, Beta, post.dist = FALSE)
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 |
Beta |
either a vector or a matrix of unit length selection gradients stacked column wise. |
post.dist |
logical: should the posterior distribution of the evolvability parameters be saved. |
An object of class
'evolvabilityBetaMCMC'
, which is a
list with the following components:
eB | The posterior median and highest posterior density interval of evolvability for each selection gradient. | |||
rB | The posterior median and highest posterior density interval of respondability for each selection gradient. | |||
cB | The posterior median and highest posterior density interval of conditional evolvability for each selection gradient. | |||
aB | The posterior median and highest posterior density interval of autonomy for each selection gradient. | |||
iB | The posterior median and highest posterior density interval of integration for each selection gradient. | |||
Beta | The matrix of selection gradients. | |||
summary | The means of evolvability parameters across all selection gradients. | |||
post.dist | The full posterior distribution. |
Geir H. Bolstad
Hansen, T. F. & Houle, D. (2008) Measuring and comparing evolvability and constraint in multivariate characters. J. Evol. Biol. 21:1201-1219.
# 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: G_mcmc <- meanStdGMCMC(G_mcmc, means_mcmc) # Generating selection gradients in five random directions: Beta <- randomBeta(5, 3) # Calculating evolvability parameters: x <- evolvabilityBetaMCMC(G_mcmc, Beta, post.dist = TRUE) summary(x)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.