plot_power {ecocbo} | R Documentation |
Power curves for different sampling efforts
Description
plot_power()
can be used to visualize the power of a study as a
function of the sampling effort. The power curve plot shows that the
power of the study increases as the sample size increases, and the density
plot shows the overlapping areas where \alpha
and \beta
are
significant.
Usage
plot_power(data, n = NULL, m, method = "both")
Arguments
data |
Object of class "ecocbo_beta" that results from |
n |
Defaults to NULL, and then the function computes the number of samples (n) that results in a sampling effort close to 95% in power. If provided, said number of samples will be used. |
m |
Site label to be used as basis for the plot. |
method |
The desired plot. Options are "power", "density" or "both". "power" plots the power curve, "density" plots the density distribution of pseudoF, and "both" draws both plots one next to the other. |
Value
If the method is "power", then a power curve in which the selected,
or computed, "n" is marked in red. If the method is "density", then a
density plot for the observed pseudoF values and a line marking the value of
pseudoF that marks the significance level indicated in sim_beta()
.
If the method is "both", then a composite with a power curve and a
density plot side by side.
The value of the selected "m", "n" and the corresponding component of variation are presented in all methods.
Author(s)
Edlin Guerra-Castro (edlinguerra@gmail.com), 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()
scompvar()
sim_cbo()
Examples
plot_power(data = epiBetaR, n = 4, m = 2, method = "both")
plot_power(data = epiBetaR, n = NULL, m = 3, method = "power")
plot_power(data = epiBetaR, n = NULL, m = 3, method = "density")