Description Usage Arguments Details Value Author(s) References Examples
View source: R/evolvabilityMeans.R
evolvabilityMeans
calculates the average (unconditional) evolvability
(e), respondability (r), conditional evolvability (c), autonomy (a) and
integration (i) of a additive-genetic variance matrix using the approximation
formulas described in Hansen and Houle (2008, 2009).
1 | evolvabilityMeans(G, means = 1)
|
G |
A variance matrix (must be symmetric and positive definite). |
means |
An optional vector of trait means, for mean standardization. |
The equations for calculating the evolvability parameters are
approximations, except for the minimum, maximum and unconditional
evolvability which are exact. The bias of the approximations depends on the
dimensionality of the G-matrix, with higher bias for few dimensions (see
Hansen and Houle 2008). For low dimensional G-matrices, we recommend
estimating the averages of the evolvability parameters using
evolavbilityBetaMCMC
over many random selection gradients (
randomBeta
). The maximum and minimum evolvability, which
are also the maximum and minimum respondability and conditional
evolvability, equals the largest and smallest eigenvalue of the G-matrix,
respectively.
A vector with the following components:
e_mean | The average (unconditional) evolvability. | |||
e_min | The minimum evolvability. | |||
e_max | The maximum evolvability. | |||
r_mean | The average respondability. | |||
c_mean | The average conditional evolvability. | |||
a_mean | The average autonomy. | |||
i_mean | The average integration. |
Geir H. Bolstad
Hansen, T. F. & Houle, D. (2008) Measuring and comparing evolvability and
constraint in multivariate characters. J. Evol. Biol. 21:1201-1219.
Hansen, T. F. & Houle, D. (2009) Corrigendum. J. Evol. Biol. 22:913-915.
1 2 | G <- matrix(c(1, 1, 0, 1, 2, 1, 0, 1, 2), ncol = 3)
evolvabilityMeans(G)
|
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