# R/siber.hull.metrics.R In siar: Stable Isotope Analysis in R

#### Documented in siber.hull.metrics

```siber.hull.metrics <- function(X,Y,G,R=10^4){

reps <- 10^4

## now loop through the data and calculate the ellipses
M <- length(unique(G))

# split the isotope data based on group
spx <- split(X,G)
spy <- split(Y,G)

# some matrices and vectors in which to store the results
sim.mu.X <- matrix(data=0,nrow=reps,ncol=M)
sim.mu.Y <- matrix(data=0,nrow=reps,ncol=M)

for (i in 1:M){

#-----------------------------------------------------------------------------

mymodel <- bayesMVN(spx[[i]],spy[[i]])
#mymodel <- bayestwoNorm(x,y)

#-----------------------------------------------------------------------------

if (i == 1){
simC <- mymodel\$b[,1]
simN <- mymodel\$b[,2]
}
else{
simC <- cbind(simC,mymodel\$b[,1])
simN <- cbind(simN,mymodel\$b[,2])
}
rm(mymodel)
}

# now loop through all the simulated group means
# and calculate the layman metrics
nr <- nrow(simC) # the number of reps.. same as reps defined above

dNr <- numeric(nr)
dCr <- numeric(nr)
TA <- numeric(nr)
CD <- numeric(nr)
MNND <- numeric(nr)
SDNND <- numeric(nr)

for (i in 1:nr) {

layman <- laymanmetrics( simC[i,], simN[i,] )

# extract the metrics into their named vectors
dNr[i] <- layman\$dN_range
dCr[i] <- layman\$dC_range
TA[i] <- layman\$hull\$TA
CD[i] <- layman\$CD
MNND[i] <- layman\$MNND
SDNND[i] <- layman\$SDNND

}

metrics <- cbind( dNr, dCr, TA, CD, MNND, SDNND)

return(metrics)

}
```

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siar documentation built on May 30, 2017, 2:17 a.m.