# SMUFASA Workflow Example

#### Jennifer McNichol

#### 2022-10-18

# Load Packages

    library(QFASA)
    library(dplyr)
    library(compositions)

# Modeling Inputs

Prior to starting make sure that:

- Fatty acid names in all files are the same (contain the exact same numbers/characters and punctuation)
- There are no fatty acids in the prey file that do not appear in the predator file and visa versa

## Fatty Acid Set

- This is the list of FAs to be used in the modelling.
- The simplest alternative is to load a `.csv` file that contains a single column with a header row with the names of thee fatty acids listed below (see example file “FAset.csv”).
- A more complicated alternative is to load a `.csv` file with the full set of FAs and then add code to subset the FAs you wish to use from that set -> this alternative is useful if you are planning to test multiple sets.

    data(FAset)
    fa.set = as.vector(unlist(FAset))

## Matrix of Predator FA Signatures

- The FA signatures in the originating `.csv` file should be proportions summing to 1.
- Each predator signature is a row with the FAs in columns (see example file “predatorFAs.csv”). The FA signatures are subsetted for the chosen FA set (created above) and renormalized during the modelling so there is no need to subset and/or renormalize prior to loading the .csv file or running `p.SMUFASA` BUT make sure that the same FAs appear in the predator and prey files.
- Your predator FA `.csv` file can contain as much tombstone data columns as you like, you must extract the predator FA signatures as separate input in order to run in `p.SMUFASA`. For example: in the code below, the predator `.csv` file (“`predatorFAs.csv`”) has 4 tombstone columns (SampleCode, AnimalCode, SampleGroup, Biopsy). Prior to running `p.SMUFASA`, the tombstone (columns 1-4) and FA data (columns 5 onward) are each extracted from the original data frame. The FA data becomes the `predator.matrix` (which is passed to `p.SMUFASA`) and the tombstone data is retained so that it can be recombined with the model output later on.

    data(predatorFAs)
    tombstone.info = predatorFAs[,1:4]
    predator.matrix = predatorFAs[,5:(ncol(predatorFAs))]
    npredators = nrow(predator.matrix)

## Matrix of Prey FA Signatures

- The FA signatures in the `.csv` file should be proportions summing to 1.
- The prey file should contain all of the individual FA signatures of the prey and their lipid contents (where appropriate).
- If you want to only include a subset of prey species, you must extract it prior to input (see code below).

    data(preyFAs)
    prey.matrix = preyFAs[,-c(1,3)]

    # Selecting 5 prey species to include
    spec.red <-c("capelin", "herring", "mackerel", "pilchard", "sandlance")
    spec.red <- sort(spec.red)
    prey.red <- prey.matrix %>%
      filter(Species %in% spec.red)

## Prey Lipid Content

- Mean lipid content by species group is calculated from the full prey file using the species group as a summary variable (see code below).
- Note: If no lipid content correction is going to be applied, then a vector of 1s of length equal to the number of species groups is used as the vector instead. I.e. `FC - rep(1,nrow(prey.matrix))`.
- If you’ve decided on a subset of species, you must extract them from the mean lipid content vector as well.

    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]

# Running SMUFASA

    smufasa.est <- p.SMUFASA(predator.matrix, prey.red, FC.red, fa.set)

### p.SMUFASA Output

The MUFASA output is a list with 4 components:

- Cal_Estimates
- Diet_Estimates
- nll
- Var_Epsilon

## Calibration Coefficient Estimates

A vector of length equal to the number of FAs used and whose sum is the total number of FAs used. Thos is a set of calibration coefficients common to all predators used.

    CalEst <- smufasa.est$Cal_Estimates

## Diet Estimates

The diet estimate vector returned by `p.SMUFASA` represents an overall common diet for all predators in `predator.matrix` . Note: If you wish to obtain diet estimates for each individual predator see the steps below.

    DietEst <- smufasa.est$Diet_Estimates

## nll

This is a vector of the negative log likelihood values at each iteration of the optimizer.

    nll <- smufasa.est$nll

## Var_Epsilon

This is the optimized diagonal values of the variance-covariance matrix of the errors. See reference in help file for details.

    VarEps <- smufasa.est$Var_Epsilon

# Obtaining Diet Estimates

Once a vector of calibration coefficients is obtained via `p.SMUFASA` you can pass this vector to `p.QFASA` or `p.MUFASA` to obtain individual diet estimates.

## QFASA

    Q = p.QFASA(predator.matrix=predator.matrix, prey.matrix=prey.matrix,
                cal.mat=CalEst, dist.meas=2, gamma=1, FC=FC.red,
                start.val=rep(1,nrow(prey.red)), ext.fa=fa.set)

QFASA Diet Estimates:

    DietEst.Q <- Q$'Diet Estimates'

See `p.QFASA` documnetation for further details.

## MUFASA

    M <- p.SMUFASA(predator.matrix, prey.red, cal.est, FC.red, fa.set)
    DietEst.M <- M$Diet_Estimates

See `p.MUFASA` documnetation for further details.