Calculate mahalanobis distance (null and alternative MSEs) and statistical power for all MPs in an MSE
An object of class MSE representing the null hypothesis
An object of class MSE representing the alternative hypothesis
Character string of data types: Cat = catch, Ind = relative abundance index, ML = mean length in catches
Character string defining the quantity to be calculated for each data type, slp = slope(log(x)), AAV = average annual variability, mu = mean(log(x))
Character string of names for the quantities calculated
Integer, the resolution (time blocking) for the calculation of PPD
Probability of incorrectly rejecting the null operating model when it is valid
Logical, should the PPD cross correlations be plotted?
Logical, should data not contributing to the mahalanobis distance be removed?
Positive fraction: the cumulative percentage of removed data (removedat=TRUE) that contribute to the mahalanobis distance
A list object with two hierarchies of indexing, first by MP, second has two positions as described in Probs: (1) mahalanobis distance, (2) a matrix of type 1 error (first row) and statistical power (second row), by time block.
Carruthers, T.R, and Hordyk, A.R. In press. Using management strategy evaluation to establish indicators of changing fisheries. Canadian Journal of Fisheries and Aquatic Science.