sim_cbo: Simulated cost-benefit optimization

In ecocbo: Calculating Optimum Sampling Effort in Community Ecology

Simulated cost-benefit optimization

Description

sim_cbo() can be used to apply a cost-benefit optimization model that depends either on a desired level of precision or on a budgeted total cost, as proposed by Underwood (1997).

Usage

sim_cbo(comp.var, multSE = NULL, ct = NULL, ck, cj = NULL)

Arguments

comp.var	Data frame as obtained from scompvar().
multSE	Optional. Required multivariate standard error for the sampling experiment.
ct	Optional. Total cost for the sampling experiment.
ck	Cost per replicate.
cj	Cost per unit.

Value

A data frame containing the optimized values for m number of sites and n number of samples to consider.

Author(s)

Edlin Guerra-Castro ([e-mail]), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

See Also

sim_beta() plot_power() scompvar()

Examples

compVar <- scompvar(data = simResults)

sim_cbo(comp.var = compVar, multSE = NULL, ct = 20000, ck = 100, cj = 2500)
sim_cbo(comp.var = compVar, multSE = 0.15, ct = NULL, ck = 100, cj = 2500)

ecocbo documentation built on Sept. 11, 2024, 8:09 p.m.