Nothing

```
overlapEst <-
function( A, B, kmax=3, adjust=c(0.8, 1, 4), n.grid=128,
type=c("all", "Dhat1", "Dhat4", "Dhat5")) {
## Calculates 3 estimates of overlap: numbers 1,4 and 5 in R&L 2009:325
# Args
# A, B : the observations of species A and species B, in RADIANS.
# kmax : maximum value of k for kappa estimation
# adjust : smoother adjustment; either a single value used for all 3 Deltas,
# or a vector of 3 different values; a NA value in adjust means that
# the corresponding Dhat will not be estimated. (adjust = 1/c in old code)
# n.grid : number of points at which to estimate density; smaller values
# give lower precision but run faster in simulations and bootstraps.
# Returns:
# A named vector of 3 overlap estimates
if(length(adjust) == 1)
adjust <- rep(adjust, 3)
adj <- adjust
type <- match.arg(type)
index <- switch(type,
all = 0,
Dhat1 = {is.na(adj) <- 2:3 ; 1},
Dhat4 = {is.na(adj) <- c(1,3) ; 2},
Dhat5 = {is.na(adj) <- 1:2 ; 3} )
if(index && is.na(adj[index]))
stop("You specified adjust = NA for the selected type: ", type)
grid <- seq(0, 2*pi, length=n.grid)
dA.current <- NA
out <- rep(NA, 3)
names(out) <- c("Dhat1", "Dhat4", "Dhat5")
# Get bandwidth
bw0.A <- getBandWidth(A, kmax=kmax)
bw0.B <- getBandWidth(B, kmax=kmax)
if(!is.na(bw0.A) && !is.na(bw0.B)) { # if no Uniroot Error
if(!is.na(adj[1])) { # Do Dhat1
dA.current <- 1
dA <- densityFit(A, grid, bw0.A / adj[1])
dB <- densityFit(B, grid, bw0.B / adj[1])
# Prune first value and scale to sum to 1
dA1 <- dA[-1] / sum(dA[-1])
dB1 <- dB[-1] / sum(dB[-1])
out[1] <- sum(pmin(dA1, dB1)) # Dhat1
}
# Get densities for Dhat4 and Dhat5
n1 <- length(A)
allObs <- c(A, B)
n <- length(allObs)
if(!is.na(adj[2])) { # Get kernel densities for Dhat4
if(n > (n.grid*1.1)) { # Use interpolation
if(is.na(adj[1]) | !identical(all.equal(adj[1], adj[2]), TRUE)) {
dA.current <- 2
dA <- densityFit(A, grid, bw0.A / adj[2])
dB <- densityFit(B, grid, bw0.B / adj[2])
}
dAx <- approx(grid, dA, allObs)$y
dBx <- approx(grid, dB, allObs)$y
} else {
dAx <- densityFit(A, allObs, bw0.A / adj[2])
dBx <- densityFit(B, allObs, bw0.B / adj[2])
}
out[2] <- (mean(pmin(1, dBx[1:n1] / dAx[1:n1])) + # Dhat4
mean(pmin(1, dAx[(n1+1):n] / dBx[(n1+1):n]))) / 2
}
if(!is.na(adj[3])) {
if(is.na(adj[2]) | !identical(all.equal(adj[2], adj[3]), TRUE)) {
# Do new kernel densities for Dhat5
if(n > (n.grid*1.1)) { # Use interpolation
if(is.na(dA.current) | !identical(all.equal(adj[dA.current], adj[3]), TRUE)) {
dA <- densityFit(A, grid, bw0.A / adj[3])
dB <- densityFit(B, grid, bw0.B / adj[3])
}
dAx <- approx(grid, dA, allObs)$y
dBx <- approx(grid, dB, allObs)$y
} else {
dAx <- densityFit(A, allObs, bw0.A / adj[3])
dBx <- densityFit(B, allObs, bw0.B / adj[3])
}
}
out[3] <- mean(dBx[1:n1] > dAx[1:n1]) + # Dhat5
mean(dAx[(n1+1):n] >= dBx[(n1+1):n])
}
} # end no uniroot error
if(type != "all" && index)
out <- out[index]
return(out)
}
```

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