crawl (version 1.3-1)

northernFurSeal: Northern fur seal pup relocation data set used in Johnson et al. (2008)

Description

Northern fur seal pup relocation data set used in Johnson et al. (2008)

source

Alska Ecosystems Program National Marine Mammal Laboratory Alaska Fisheries Science Center National Marine Fisheries Service, NOAA 7600 Sand Point Way NE Seattle, WA 98115

References

Johnson, D., J. London, M. -A. Lea, and J. Durban (2008) Continuous-time random walk model for animal telemetry data. Ecology 89:1208-1215.

Examples

data(northernFurSeal)

argosClasses <- c("3", "2", "1", "0", "A", "B")
ArgosMultFactors <- data.frame(Argos_loc_class=argosClasses,
errX=log(c(1, 1.5, 4, 14, 5.21, 20.78)),
errY=log(c(1, 1.5, 4, 14, 11.08, 31.03)))
nfsNew <- merge(northernFurSeal, ArgosMultFactors,
by=c("Argos_loc_class"), all.x=TRUE)
nfsNew <- nfsNew[order(nfsNew$Time), ]

# State starting values
initial.drift <- list(a1.x=c(189.686, 0, 0), a1.y=c(57.145, 0, 0),
P1.x=diag(c(0, 0.001, 0.001)),
P1.y=diag(c(0, 0.001, 0.001)))

##Fit random drift model
fit <- crwMLE(mov.model=~1, err.model=list(x=~errX, y=~errY), drift.model=TRUE,
data=nfsNew, coord=c("longitude", "latitude"), polar.coord=TRUE,
Time.name="Time", initial.state=initial.drift,
fixPar=c(NA, 1, NA, 1, NA, NA, NA, NA),
control=list(maxit=2000,trace=1, REPORT=10),
initialSANN=list(maxit=300, trace=1, REPORT=1)
)

##Make hourly location predictions
predTime <- seq(ceiling(min(nfsNew$Time)), floor(max(nfsNew$Time)), 1)
predObj <- crwPredict(object.crwFit=fit, predTime, speedEst=TRUE, flat=TRUE)
head(predObj)
crwPredictPlot(predObj)

##Create simulation object with 100 parameter draws
simObj <- crwSimulator(fit, predTime, parIS=100, df=20, scale=18/20)

## Examine IS weight distribution
w <- simObj$thetaSampList[[1]][,1]
dev.new()
hist(w*100, main='Importance Sampling Weights', sub='More weights near 1 is desirable')

##Approximate number of independent samples
round(100/(1+(sd(w)/mean(w))^2))

dev.new(bg=gray(0.75))
jet.colors <-
colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan",
"#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
crwPredictPlot(predObj, 'map')

## Sample 20 tracks from posterior predictive distribution
iter <- 20
cols <- jet.colors(iter)
for(i in 1:iter){
samp <- crwPostIS(simObj)
lines(samp$alpha.sim.x[,'mu'], samp$alpha.sim.y[,'mu'],col=cols[i])
}