Derive a hypothetical set of population vectors corresponding to a time-series of matrix population models
Derive a hypothetical set of population vectors (i.e. population size distributions across stages) given a time-series of matrix population models (MPMs), by taking the stable stage distribution of the mean matrix as the starting vector (or optionally, a uniform or random starting vector), and deriving subsequent vectors through recursive population projection.
pop_vectors(A, start = "stable.stage")
A |
A list of MPMs (i.e. square population projection matrices). |
start |
Method to derive the first population vector in the series. Either 'stable.stage' to use the stable stage distribution of the mean matrix as the starting vector, 'uniform' to use a uniform starting vector (all elements equal), or 'random' to use a randomly-generated starting vector. Defaults to 'stable.stage'. |
This function is useful for providing population vectors as input to the
perturb_stochastic
function which calculates stochastic
elasticities given a time-series of matrix population models and
corresponding population vectors, using the method described in Haridas et
al. (2009).
A list of population vectors
Patrick Barks <patrick.barks@gmail.com>
Haridas, C. V., Tuljapurkar, S., & Coulson, T. 2009. Estimating stochastic elasticities directly from longitudinal data. Ecology Letters, 12, 806-812. <doi:10.1111/j.1461-0248.2009.01330.x>
Other perturbation analysis:
perturb_matrix()
,
perturb_stochastic()
,
perturb_trans()
,
perturb_vr()
# generate list of matrices matA_l <- replicate(5, matrix(runif(9), 3, 3), simplify = FALSE) # calculate corresponding population vectors pop_vectors(matA_l) pop_vectors(matA_l, start = "uniform") pop_vectors(matA_l, start = "random")
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