1d:T795,# Plot the remigration intervals. ```r ## S3 method for class 'Remigration' plot(x, legend = TRUE, ...) ``` ## Arguments - `x`: Object obtained from Bayesian.remigration() - `legend`: TRUE or FALSE or c(x, y) - `...`: Parameters transmitted to plot ## Returns An invisible dataframe with values used for plotting. ## Details plot.Remigration plots the remigration intervals. ## Examples ```r ## Not run: library(phenology) # Example # Each year a fraction of 0.9 is surviving s <- c(s=0.9) # Probability of tag retention; 0.8 t <- c(t=0.8) # Time-conditional return probability - This is the true remigration rate r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) # Capture probability p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # Number of observations for 400 tagged females after 1, 2, 3, 4, and 5 years OBS <- c(400, 10, 120, 40, 20, 10) kl_s <- length(s) kl_t <- length(t) kl_r <- length(r) kl_p <- length(p) pMCMC <- data.frame(Density=c("newdbeta", "newdbeta", rep("dunif", kl_r), rep("newdbeta", kl_p), "dunif"), Prior1=c(s, t, rep(0, kl_r), rep(0.2, kl_p), 0), Prior2=c(0.02, 0.02, rep(1, kl_r), rep(0.08, kl_p), 10), SDProp=c(0.05, 0.05, rep(0.05, kl_r), rep(0.05, kl_p), 0.05), Min=c(0, 0, rep(0, kl_r), rep(0, kl_p), 0), Max=c(1, 1, rep(1, kl_r), rep(1, kl_p), 10), Init=c(s, t, r, p, 1), stringsAsFactors = FALSE, row.names=c("s", "t", names(r), names(p), "sd") ) rMCMC <- Bayesian.remigration(parameters = pMCMC, n.iter = 1000000, n.adapt = 300000, trace=10000, data=OBS) plot(rMCMC) ## End(Not run) ``` ## See Also Other Model of Remigration Interval: `Bayesian.remigration()`, `LnRI_norm()`, `RI()`, `RI2BP()` ## Author(s) Marc Girondot

plot.Remigration function