1d:T7d9,# Return an expected remigration interval. 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. ```r RI(s, t, r = NULL, c = NULL, p) ``` ## Arguments - `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 ## Returns Return a remigration interval. ## Details RI returns an expected remigration interval ## Examples ```r ## 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]) # BP is then RI2BP(proportion=c, RI=c(mean=1:5, sd=rep(0, 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) ``` ## See Also Other Model of Remigration Interval: `Bayesian.remigration()`, `LnRI_norm()`, `RI2BP()`, `plot.Remigration()` ## Author(s) Marc Girondot

RI function