Estimates the statistical power of a study by comparing variation under null and alternative hypotheses. For instance, if the beta error is 0.25, there is a 25% chance of failing to detect a real difference, and the power of the study is \(1 - \beta\), meaning 0.75 in this case.
sim_beta(data, alpha = 0.05)A list of class "ecocbo_beta", containing:
$Power: a data frame with power and beta estimates across different
sampling efforts (m sites and n samples).
$Results: a data frame with pseudo-F estimates for simH0 and simHa.
$alpha: significance level for Type I error.
An object of class "ecocbo_data" that results from applying
prep_data() to a community dataset.
Numeric. Significance level for Type I error. Defaults to 0.05.
Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras
The function displays a summary matrix with estimated power values for various sampling efforts.
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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