bias.all | R Documentation |
beta.meths.CI
.Calculate bias correction for confidence intervals from beta.meths.CI
.
bias.all(
p.mat,
prey.mat,
cal.mat = rep(1, length(ext.fa)),
fat.cont = rep(1, nrow(prey.mat)),
R.bias,
noise,
nprey,
specify.noise,
dist.meas,
ext.fa
)
p.mat |
matrix containing the fatty acid signatures of the predators. |
prey.mat |
matrix containing a representative fatty acid signature |
cal.mat |
matrix of calibration factors where the i th column is to be used with the i th predator. If modelling is to be done without calibration coefficients, simply pass a vector or matrix of ones. |
fat.cont |
prey fat content |
R.bias |
bootstrap replicates |
noise |
noise |
nprey |
number of prey |
specify.noise |
noise |
dist.meas |
distance measure |
ext.fa |
subset of FA's to use. |
Row 1 is Lambda CI, row 2 is Lambda skew, and row 3 is Beta CI
## Fatty Acids
data(FAset)
fa.set = as.vector(unlist(FAset))
## Predators
data(predatorFAs)
tombstone.info = predatorFAs[,1:4]
predator.matrix = predatorFAs[, fa.set]
npredators = nrow(predator.matrix)
## Prey
prey.sub = preyFAs[, fa.set]
prey.sub = prey.sub / apply(prey.sub, 1, sum)
group = as.vector(preyFAs$Species)
prey.matrix.full = cbind(group,prey.sub)
prey.matrix = MEANmeth(prey.matrix.full)
## Calibration Coefficients
data(CC)
cal.vec = CC[CC$FA %in% fa.set, 2]
cal.mat = replicate(npredators, cal.vec)
# Note: uncomment examples to run. CRAN tests fail because execution time > 5 seconds
# diet.est <- p.QFASA(predator.mat = predator.matrix,
# prey.mat = prey.matrix,
# cal.mat = cal.mat,
# dist.meas = 2,
# start.val = rep(1,nrow(prey.matrix)),
# ext.fa = fa.set)[['Diet Estimates']]
#
# bias <- bias.all(p.mat = diet.est,
# prey.mat = prey.matrix.full,
# cal.mat = cal.mat,
# R.bias = 10,
# noise = 0,
# nprey = 50,
# dist.meas = 2,
# ext.fa = fa.set)
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