sim_beta() can be used to assess the power of a study by comparing the
variation when one can assume whether an ecological community does not have
composition differences (H0 true) or it does (H0 false). For example, if the
beta error is 0.25, then there is a 25% chance of failing to detect a
difference even if the difference is real. The power of the study is
\(1 - \beta\), so in this example, the power of the study is 0.75.
sim_beta(data, alpha = 0.05)sim_data() returns an object of class "ecocbo_beta".
The function print() is used to present a matrix that summarizes the
results by showing the estimate power according to different sampling efforts.
An object of class "ecocbo_beta" is a list containing the following components:
$Power a data frame containing the estimation of power and beta for
several combination of sampling efforts (m sites and n samples).
$Results a data frame containing the estimates of pseudoF for simH0
and simHa.
$alpha level of significance for Type I error.
An object of class "ecocbo_data" that results from applying
prep_data() to a community data frame.
Level of significance for Type I error. Defaults to 0.05.
Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras
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.
Anderson, M. J. (2014). Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1-15.
Guerra‐Castro, E. J., Cajas, J. C., Simões, N., Cruz‐Motta, J. J., & Mascaró, M. (2021). SSP: an R package to estimate sampling effort in studies of ecological communities. Ecography, 44(4), 561-573.
plot_power()
scompvar()
sim_cbo()
prep_data()
SSP::assempar()
SSP::simdata()
sim_beta(data = simResults, alpha = 0.05)
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