# plot.Remigration: Plot the remigration intervals. In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

## Description

Plot the remigration intervals.

## Usage

 ```1 2``` ```## 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

## Details

plot.Remigration plots the remigration intervals.

## Value

An invisible dataframe with values used for plotting.

## Author(s)

Marc Girondot

Other Model of Remigration Interval: `Bayesian.remigration()`, `LnRI_norm()`, `RI()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43``` ```## 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) ```