RI | R Documentation |

Model of remigration interval

Note that r, s and t are conditional probabilities. If c is null, then return probabilities are
estimated from r. r can be named vector. For example:

r <- c(r1=0.5, r2=0.60, r3=1) is equivalent to c <- c(c1=0.5, c2=0.3, c3=0.2)

The vector of r described the probability that a female returned after
1, 2, 3 years among those who have not nested before.
The vector of r is the same but defining the return probability for an initial female.

```
RI(s, t, r = NULL, c = NULL, p)
```

`s` |
Time-conditional probability of survival |

`t` |
Time-conditional probability of tag retention |

`r` |
Time-conditional probability of return |

`c` |
Probability of return |

`p` |
Annual probability of observation |

RI returns an expected remigration interval

Return a remigration interval.

Marc Girondot

Other Model of Remigration Interval:
`Bayesian.remigration()`

,
`LnRI_norm()`

,
`plot.Remigration()`

```
## Not run:
library(phenology)
# Example
s <- c(s1=1, s2=1, s3=1, s4=1, s5=1)
t <- c(t1=0.95, t2=1, t3=1, t4=1, t5=1)
r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1)
p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6)
# r is equivalent to
c <- c(c1=0.1, c2=0.72, c3=0.126, c4=0.0378, c5=0.0162)
# Then the true remigration interval is:
ri_true <- sum(1:5*c[1:5])
s_ri <- NULL
for (sx in seq(from=0.01, to=1, by=0.01)) {
s[] <- sx
ri1 <- RI(s=s, t=t, r=r, p=p)
s_ri <- c(s_ri,sum(1:5*ri1)/sum(ri1))
}
par(mar=c(4, 4, 1, 1)+0.4)
plot(x=seq(from=0.01, to=1, by=0.01), y=s_ri, type="l",
las=1, bty="n", ylim=c(0, 4),
xlab="Annuual survival probabilities", ylab="Naive Remigration Interval",
main="")
segments(x0=0.01, x1=1, y0=ri_true, y1=ri_true, lty=2, col="red")
legend("topright", legend="True remigration interval", lty=2, col="red")
## End(Not run)
```

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