View source: R/Bayesian.remigration.R
Bayesian.remigration | R Documentation |
Model of remigration interval
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
)
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. |
Bayesian.remigration fits a remigration interval using Bayesian MCMC
Return a posterior remigration interval.
Marc Girondot marc.girondot@gmail.com
Other Model of Remigration Interval:
LnRI_norm()
,
RI()
,
plot.Remigration()
## 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)
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