Calculate mahalanobis distance (null and alternative MSEs) and statistical power for all MPs in an MSE

PRBcalc(
MSE_null,
MSE_alt,
tsd = c("Cat", "Cat", "Cat", "Ind", "ML"),
stat = c("slp", "AAV", "mu", "slp", "slp"),
dnam = c("C_S", "C_V", "C_M", "I_S", "ML_S"),
res = 6,
alpha = 0.05,
plotCC = FALSE,
removedat = FALSE,
removethresh = 0.025
)

## Arguments

MSE_null

An object of class MSE representing the null hypothesis

MSE_alt

An object of class MSE representing the alternative hypothesis

tsd

Character string of data types: Cat = catch, Ind = relative abundance index, ML = mean length in catches

stat

Character string defining the quantity to be calculated for each data type, slp = slope(log(x)), AAV = average annual variability, mu = mean(log(x))

dnam

Character string of names for the quantities calculated

res

Integer, the resolution (time blocking) for the calculation of PPD

alpha

Probability of incorrectly rejecting the null operating model when it is valid

plotCC

Logical, should the PPD cross correlations be plotted?

removedat

Logical, should data not contributing to the mahalanobis distance be removed?

removethresh

Positive fraction: the cumulative percentage of removed data (removedat=TRUE) that contribute to the mahalanobis distance

## Value

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.

## References

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.

T. Carruthers