
G
needs to be symmetric and positive definite.
evolvabilityBeta(G, Beta, means = 1)
A variance matrix.
Either a vector or a matrix of unit length selection gradients stacked column wise.
An optional vector of trait means (for internal mean standardization).
An object of class
'evolvabilityBeta'
, which is a list
with the following components:
Beta |
The matrix of selection gradients. |
e |
|||
The evolvability of each selection gradient. |
r |
||||
The respondability of each selection gradient. |
c |
||||
The conditional evolvability of each selection gradient. |
a |
||||
The autonomy of each selection gradient. |
Beta |
The matrix of selection gradients. |
evolvabilityBeta
calculates (unconditional) evolvability (e),
respondability (r), conditional evolvability (c), autonomy (a) and
integration (i) along selection gradients given an additive-genetic variance
matrix as described in Hansen and Houle (2008).
Hansen, T. F. & Houle, D. (2008) Measuring and comparing evolvability and constraint in multivariate characters. J. Evol. Biol. 21:1201-1219.
# NOT RUN {
G <- matrix(c(1, 1, 0, 1, 2, 2, 0, 2, 3), ncol = 3) / 10
Beta <- randomBeta(5, 3)
X <- evolvabilityBeta(G, Beta)
summary(X)
# }
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