QFASA (version 1.2.1)

p.MUFASA: Returns MUFASA diet estimates corresponding to a sample of predators.

Description

Computes the diet estimate for each predator in pred.mat using MLE method.

Usage

p.MUFASA(pred.mat, prey.mat, cal.mat, FC, ext.fa)

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.

nll

Negative log likelihood values. As per solnp documentation, nll is "Vector of function values during optimization with last one the value at the optimal".

Var_Epsilon

Optimized values of error variance. See reference.

Arguments

pred.mat

matrix containing the FA signatures of the predators.

prey.mat

matrix containing FA signatures from each prey group The first column must index the prey group. prey.mat is the prey database.

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. cal.mat must contain names of FAs.

FC

vector of fat content of length equal to the number of prey groups or species.

ext.fa

subset of fatty acids to be used to obtain QFASA diet estimates.

References

Steeves, Holly (2020) Maximum likelihood approach to diet estimation and inference based on fatty acid signatures. PhD thesis available at https://dalspace.library.dal.ca/handle/10222/80034.

See Also

p.MLE() for a simplifed version of p.MUFASA() that is faster to run.

Examples

 ## This example takes some time to run.
 ## Please uncomment code below to run.

#library(dplyr)
#library(compositions)
 ## 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
## Extracting a small number of species to speed up calculations for the example.
#data(preyFAs)
#prey.matrix = preyFAs[,-c(1,3)]
#spec.red <-c("capelin", "herring", "mackerel", "pilchard", "sandlance")
#spec.red <- sort(spec.red)
#prey.red <- prey.matrix %>% filter(Species %in% spec.red)

## Fat content
#FC = preyFAs[,c(2,3)]
#FC = FC %>%  arrange(Species)
#FC.vec = tapply(FC$lipid,FC$Species,mean,na.rm=TRUE)
#FC.red <- FC.vec[spec.red]

## Calibration Coefficients
#data(CC)
#cal.vec = CC[,2]
#cal.m = replicate(npredators, cal.vec)
#rownames(cal.m) <- CC$FA

#M <- p.MUFASA(predator.matrix, prey.red, cal.m, FC.red, fa.set)

## Diet EStimates

#M$Diet_Estimates