View source: R/evolvabilityBetaMCMC2.R
evolvabilityBetaMCMC2 | R Documentation |
evolvabilityBetaMCMC2
calculates (unconditional) evolvability (e),
respondability (r), conditional evolvability (c), autonomy (a) and
integration (i) along a selection gradient estimate with uncertainty.
evolvabilityBetaMCMC2(G_mcmc, Beta_mcmc, post.dist = FALSE)
G_mcmc |
A 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_mcmc |
A posterior distribution of a unit length selection gradient where iterations are given row wise. | |||||||||||||||
post.dist |
logical: should the posterior distribution of the evolvability parameters be saved.
|
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) # Simulating a posterior distribution (or bootstrap distribution) # of a unit length selection gradient: Beta <- randomBeta(1, 3) Beta.mcmc <- sapply(c(Beta), function(x) rnorm(10, x, 0.01)) Beta.mcmc <- t(apply(Beta.mcmc, 1, function(x) x / sqrt(sum(x^2)))) # Running the model: evolvabilityBetaMCMC2(G_mcmc, Beta_mcmc = Beta.mcmc, post.dist = TRUE) }
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