Compute the standardized left and right eigenvectors via iteration
Compute the standardized left and right eigenvectors via iteration
right_ev(ipm, iterations, tolerance) ## S3 method for class 'simple_di_det_ipm' right_ev(ipm, iterations = 100, tolerance = 1e-10) ## S3 method for class 'general_di_det_ipm' right_ev(ipm, iterations = 100, tolerance = 1e-10) left_ev(ipm, iterations, tolerance) ## S3 method for class 'simple_di_det_ipm' left_ev(ipm, iterations = 100, tolerance = 1e-10) ## S3 method for class 'general_di_det_ipm' left_ev(ipm, iterations = 100, tolerance = 1e-10)
ipm |
Output from |
iterations |
The number of times to iterate the model to reach convergence. Default is 100. |
tolerance |
Tolerance to evaluate convergence to asymptotic dynamics. |
For right_ev, if the model has already been iterated and has
converged to asymptotic dynamics, then it will just extract the final
population state and return that in a named list. Each element of the list
is a vector with length >= 1 and corresponds each state variable's
portion of the eigenvector.
If the model has been iterated, but has not yet converged to asymptotic dynamics,
right_ev will try to iterate it further using the final population state
as the starting point. The default number of iterations is 100, and can be
adjusted using the iterations argument.
If the model hasn't been iterated, then right_ev will try iterating it
for iterations number of time steps and check for convergence. In the
latter two cases, if the model still has not converged to asymptotic dynamics,
it will return NA with a warning.
For left_ev, the transpose iteration (sensu Ellner & Rees 2006,
Appendix A) is worked out based on the state_start and state_end
in the model's proto_ipm object. The model is then iterated for
iterations times to produce a standardized left eigenvector.
A list of named numeric vector(s) corresponding to the stable trait distribution
function (right_ev) or the reproductive values for each trait (left_ev).
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