northernFurSeal {crawl} | R Documentation |
Northern fur seal pup relocation data set used in Johnson et al. (2008)
data(northernFurSeal)
A data frame with 795 observations on the following 4 variables:
Time
Argos_loc_class
0
1
2
3
A
.latitude
longitude
Alska Ecosystems Program National Marine Mammal Laboratory Alaska Fisheries Science Center National Marine Fisheries Service, NOAA 7600 Sand Point Way NE Seattle, WA 98115
Johnson, D., J. London, M. -A. Lea, and J. Durban (2008) Continuous-time random walk model for animal telemetry data. Ecology 89:1208-1215.
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$Time), ] # State starting values initial.drift <- list(a1.x=c(189.686, 0, 0), a1.y=c(57.145, 0, 0), P1.x=diag(c(0, 0.001, 0.001)), P1.y=diag(c(0, 0.001, 0.001))) ##Fit random drift model fit <- crwMLE(mov.model=~1, err.model=list(x=~errX, y=~errY), drift.model=TRUE, data=nfsNew, coord=c("longitude", "latitude"), Time.name="Time", initial.state=initial.drift, polar.coord=TRUE, fixPar=c(NA, 1, NA, 1, NA, NA, NA,NA), control=list(maxit=2000,trace=1, REPORT=10)) ##Make hourly location predictions predTime <- seq(ceiling(min(nfsNew$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 Samplig Weights', sub='More weights near 1 is desirable') ##Approximate number of independent samples 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]) }