ecocbo (version 1.0.0)

sim_cbo: Cost-Benefit Optimization for Sampling Effort

Description

Given a table of statistical power estimates produced by sim_beta, sim_cbo finds the sampling design (number of replicates/site and sites) that minimizes total cost while achieving a user‐specified power threshold.

Usage

sim_cbo(data, cn, cm = NULL, perm = 100)

Value

A data frame with one row per candidate design. In the single factor case, the results include the available n values, their statistical power and cost. For the nested symmetric experiments, the results include all the available values for m, the optimal n, according to the power, and the associated cost. The results also mark a suggested sampling effort, based on the cost and power range as selected by the user.

Arguments

data

Object of class "ecocbo_beta", as returned by sim_beta.

cn

Numeric. Cost per sampling unit.

cm

Numeric. Fixed cost per replicate.

perm

Integer. Minimum number of permutations needed to reject the null hypothesis. Defaults to 100, as it would allow for rejecting with alpha = 0.05, the user can change this value to make the testing more strict (e.g. 200 for testing alpha = 0.01 or 5000 for testing alpha = 0.001).

Author

Edlin Guerra-Castro (), 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() Underwood_cbo()

Examples

# Optimization of single factor experiment
sim_cbo(data = epiBetaR, cn = 80)

# Optimization of a nested factor experiment
sim_cbo(data = betaNested, cn = 80, cm = 180)