# Return a posterior remigration interval. Model of remigration interval ```r Bayesian.remigration( parameters = stop("Priors must be supplied"), data = stop("data must be supplied"), kl = NULL, n.iter = 1e+05, n.chains = 1, n.adapt = 10000, thin = 1, trace = 10, adaptive = TRUE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL ) ``` ## Arguments - `parameters`: Priors for Bayesian MCMC - `data`: Data to be fitted - `kl`: Maximum number of years for remigration intervals. - `n.iter`: Number of iterations for MCMC - `n.chains`: Number of replicates - `n.adapt`: Number of iterations before to store outputs - `thin`: Number of iterations between each stored output - `trace`: Or FALSE or period to show progress - `adaptive`: Should an adaptive process for SDProp be used - `adaptive.lag`: Lag to analyze the SDProp value in an adaptive context - `adaptive.fun`: Function used to change the SDProp - `intermediate`: Period for saving intermediate result, NULL for no save - `filename`: If intermediate is not NULL, save intermediate result in this file - `previous`: Previous result to be continued. Can be the filename in which intermediate results are saved. ## Returns Return a posterior remigration interval. ## Details Bayesian.remigration fits a remigration interval using Bayesian MCMC ## 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: `LnRI_norm()`, `RI()`, `RI2BP()`, `plot.Remigration()` ## Author(s) Marc Girondot marc.girondot@gmail.com

Bayesian.remigration function