p.QFASA {QFASA} | R Documentation |
Returns QFASA diet estimates corresponding to a sample of predators.
Description
Computes the diet estimate for each predator in predator.mat using either the Kullback-Leibler Distance (KL), the Aitchison Distance (AIT) or the Chi-Square Distance (CS).
Usage
p.QFASA(
predator.mat,
prey.mat,
cal.mat,
dist.meas,
gamma = 1,
FC = rep(1, nrow(prey.mat)),
start.val = rep(0.99999, nrow(prey.mat)),
ext.fa
)
Arguments
predator.mat |
matrix containing the FA signatures of the predators. |
prey.mat |
matrix containing a representative FA signature from each prey group (usually the mean). The first column must index the prey group. Note can use function MEANmeth to calculate the means. |
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. |
dist.meas |
distance measure to use for estimation: 1=KL, 2=AIT or 3=CS |
gamma |
parameter required for calculations using CS distance (passed to CS.obj). Currently being set to 1. |
FC |
vector of fat content of length equal to the number of prey groups or species. |
start.val |
initial vector of parameters to be optimized |
ext.fa |
subset of fatty acids to be used to obtain QFASA diet estimates. |
Details
Before carrying out an analysis using QFASA, rows of prey database must be normalized to sum to one. See Example for code that extracts a subset of FAs and then normalizes the prey database signatures.
Value
A list with components:
Diet Estimates |
A matrix of the diet estimates for each predator where each row corresponds to a predator and the columns to prey species. The estimates are expressed as proportions summing to one. |
Additional Measures |
For each predator for which a diet estimate was obtained: |
ModFAS |
the value of the modelled fatty acid. These are expressed as proportions summing to one. |
DistCont |
The contribution of each fatty acid to the final minimized distance. |
PropDistCont |
The contribution of each fatty acid to the final minimized distance as a proportion of the total. |
MinDist |
The final minimized distance. |
References
Iverson, Sara J., Field, Chris, Bowen, W. Don and Blanchard, Wade (2004) Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets. Ecological Monographs, 74(2), 211-235
Examples
## Fatty Acids
data(FAset)
fa.set = as.vector(unlist(FAset))
## Predators
data(predatorFAs)
tombstone.info = predatorFAs[,1:4]
predator.matrix = predatorFAs[,5:(ncol(predatorFAs))]
npredators = nrow(predator.matrix)
## Prey
data(preyFAs)
prey.sub=(preyFAs[,4:(ncol(preyFAs))])[fa.set]
prey.sub=prey.sub/apply(prey.sub,1,sum)
group=as.vector(preyFAs$Species)
prey.matrix=cbind(group,prey.sub)
prey.matrix=MEANmeth(prey.matrix)
## Fat Content
FC = preyFAs[,c(2,3)]
FC = as.vector(tapply(FC$lipid,FC$Species,mean,na.rm=TRUE))
## Calibration Coefficients
data(CC)
cal.vec = CC[,2]
cal.mat = replicate(npredators, cal.vec)
## Run QFASA
Q = p.QFASA(predator.matrix,
prey.matrix,
cal.mat,
dist.meas = 1,
gamma=1,
FC,
start.val = rep(1,nrow(prey.matrix)),
fa.set)
## Diet Estimates
DietEst = Q$'Diet Estimates'