Bayesian.remigration: Return a posterior remigration interval.

In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

Return a posterior remigration interval.

Description

Model of remigration interval

Usage

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.

Details

Bayesian.remigration fits a remigration interval using Bayesian MCMC

Value

Return a posterior remigration interval.

Author(s)

Marc Girondot [e-mail]

See Also

Other Model of Remigration Interval: LnRI_norm(), RI(), plot.Remigration()

Examples

## 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)

phenology documentation built on Oct. 16, 2023, 9:06 a.m.