data(dippers)
DH <- dippers[1:7] # Extract the detection histories
survCJS(DH) # the phi(.) p(.) model
survCJS(DH, phi ~ .time) # the phi(t) p(.) model
# Floods affected the 2nd and 3rd intervals
df <- data.frame(flood = c(FALSE, TRUE, TRUE, FALSE, FALSE, FALSE))
survCJS(DH, phi ~ flood, data=df)
# Including a grouping factor:
survCJS(DH, phi ~ flood * group, data=df, group=dippers$sex)
# Bayesian estimation:
# \donttest{
if(requireNamespace("rjags")) {
Bdip <- BsurvCJS(DH, parallel=FALSE)
plot(Bdip)
BdipFlood <- BsurvCJS(DH, list(phi ~ flood, p ~ .time), data=df, parallel=FALSE)
BdipFlood
op <- par(mfrow=2:1)
plot(BdipFlood, "phi[1]", xlim=c(0.3, 0.75), main="No flood")
plot(BdipFlood, "phi[2]", xlim=c(0.3, 0.75), main="Flood")
par(op)
ratio <- BdipFlood['phi[2]'] / BdipFlood['phi[1]']
postPlot(ratio, compVal=1)
}
# }
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