Commit f3c246fa authored by matbuoro's avatar matbuoro
Browse files

Mise à jour 2016

parent 72ae463d
......@@ -18,8 +18,8 @@ stade <- "adult"
## WORKING DIRECTORY:
work.dir<-paste("/media/ORE/Abundance",site,stade,sep="/")
setwd(work.dir)
# work.dir<-paste("/media/ORE/Abundance",site,stade,sep="/")
# setwd(work.dir)
##-----------------------------DATA ----------------------------------##
......@@ -32,16 +32,25 @@ source(paste('parameters_',stade,'.R',sep="")) # chargement des paramètres
#------------------------INITS----------------------------------##
source(paste('inits/inits_',stade,'.R',sep="")) # création des inits des données
load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
if(site == "Bresle" && stade == "adult") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
#if(!file.exists(paste('inits/inits_',stade,year,'.Rdata',sep=""))){
if(!file.exists(paste("inits/init-",site,"-",stade,year,".txt",sep=""))){
source(paste('inits/inits_',stade,'.R',sep="")) # création des inits des données
#load(paste('inits/inits_',stade,year,'.Rdata',sep=""))
}
#load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
#if(site == "Bresle" && stade == "adult") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
#if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))
#------------------------MODEL----------------------------------##
model <- paste("model/",stade,"-",site,".R",sep="") # path of the model
model <- paste("model/model_",stade,"-",site,".R",sep="") # path of the model
if(site == "Scorff" && stade == "smolt") {model <- paste("model/",stade,"-",site,"_",year,".R",sep="")} # le modèle Scorrf pour les smolt peut changer tous les ans suivant conditions
model
filename <- file.path(work.dir, model)
#system(paste("cp",model,paste(stade,"-",site,".txt",sep=""),sep=""))
#---------------------------ANALYSIS-----------------------------##
nChains = length(inits) # Number of chains to run.
adaptSteps = 1000 # Number of steps to "tune" the samplers.
......@@ -57,15 +66,25 @@ start.time = Sys.time(); cat("Start of the run\n");
fit <- bugs(
data
,inits
,model.file = model
,model.file = filename
,parameters
,n.chains = nChains, n.iter = nstore + nburnin, n.burnin = nburnin, n.thin = nthin
,DIC=FALSE
,codaPkg = FALSE, clearWD=TRUE
,codaPkg = FALSE, clearWD=FALSE
#,debug=TRUE
,working.directory=work.dir
,working.directory=paste(work.dir,"bugs",sep="/")
)
## cleaning
system("rm bugs/CODA*")
### Save inits ###
# save last values for inits
# inits <- fit$last.values
# if(site == "Nivelle") {
# save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
# }
######### JAGS ##########
## Compile & adapt
......
list(Y=2.30000E+01, eff_R=c(4.00000E+00, 1.40000E+01, 8.00000E+00, 6.00000E+00, 1.00000E+01, 1.20000E+01, 6.00000E+00, 1.30000E+01, 1.20000E+01, 1.20000E+01, 1.30000E+01, 1.10000E+01, 1.70000E+01, 1.10000E+01, 1.10000E+01, 7.00000E+00, 1.00000E+01, 1.00000E+01, 8.00000E+00, 9.00000E+00, 1.30000E+01, 1.20000E+01, 8.00000E+00), Cm_R= structure(.Data= c(4.00000E+00, 0.00000E+00, 3.10000E+01, 1.00000E+00, 4.50000E+01, 3.00000E+00, 1.90000E+01, 0.00000E+00, 5.60000E+01, 1.00000E+00, 1.60000E+01, 5.00000E+00, 5.00000E+00, 0.00000E+00, 3.30000E+01, 0.00000E+00, 3.00000E+01, 1.00000E+00, 2.80000E+01, 5.00000E+00, 1.33000E+02, 5.00000E+00, 6.70000E+01, 8.00000E+00, 6.20000E+01, 6.00000E+00, 3.50000E+01, 4.00000E+00, 2.20000E+01, 5.00000E+00, 1.30000E+01, 5.00000E+00, 8.20000E+01, 4.00000E+00, 2.80000E+01, 1.30000E+01, 1.40000E+01, 4.00000E+00, 5.20000E+01, 6.00000E+00, 4.70000E+01, 1.40000E+01, 5.00000E+01, 7.00000E+00, 5.50000E+01, 6.00000E+00), .Dim=c(23, 2)), Cum_R= structure(.Data= c(1.40000E+01, 1.00000E+00, 2.80000E+01, 8.00000E+00, 1.40000E+01, 6.00000E+00, 9.00000E+00, 2.00000E+00, 1.30000E+01, 2.00000E+00, 1.10000E+01, 6.00000E+00, 9.00000E+00, 0.00000E+00, 1.10000E+01, 1.00000E+00, 1.20000E+01, 4.00000E+00, 2.40000E+01, 4.00000E+00, 6.50000E+01, 2.00000E+00, 2.00000E+01, 7.00000E+00, 2.00000E+01, 4.00000E+00, 1.60000E+01, 5.00000E+00, 1.20000E+01, 9.00000E+00, 7.00000E+00, 8.00000E+00, 3.10000E+01, 3.00000E+00, 4.00000E+00, 7.00000E+00, 9.00000E+00, 6.00000E+00, 2.60000E+01, 9.00000E+00, 3.30000E+01, 7.00000E+00, 2.20000E+01, 1.30000E+01, 3.30000E+01, 6.00000E+00), .Dim=c(23, 2)), Q= structure(.Data= c(2.44194E+00, 7.82823E+00, 1.21284E+00, 4.12274E+00, 1.22348E+00, 3.92435E+00, 1.18398E+00, 3.23790E+00, 2.01719E+00, 6.63452E+00, 1.95419E+00, 8.14371E+00, 2.53710E+00, 7.47919E+00, 1.83855E+00, 8.89161E+00, 1.90081E+00, 3.72242E+00, 8.48097E-01, 2.45258E+00, 1.51176E+00, 3.76855E+00, 9.04774E-01, 2.89468E+00, 9.76258E-01, 6.48226E+00, 4.07758E+00, 4.08694E+00, 2.14677E+00, 4.82774E+00, 1.46758E+00, 3.80710E+00, 9.46452E-01, 5.22887E+00, 8.54177E-01, 2.32661E+00, 3.44016E+00, 7.19710E+00, 1.66977E+00, 6.92855E+00, 1.69889E+00, 4.91790E+00, 1.34410E+00, 4.39033E+00, 1.41926E+00, 5.00887E+00), .Dim=c(23, 2)), Q_dec=c(6.84667E+00, 4.88567E+00, 3.77267E+00, 6.22967E+00, 4.98133E+00, 6.65867E+00, 3.08900E+01, 2.34100E+00, 9.71167E+00, 2.56800E+00, 3.50900E+00, 5.68333E+00, 1.42613E+01, 3.88000E+00, 4.77333E+00, 1.18903E+01, 6.35300E+00, 8.33727E+00, 1.41787E+01, 6.44333E+00, 6.45667E+00, 4.00000E+00, 9.90968E+08, 1.00000E+00, 4.53613E+05), Cm_D= structure(.Data= c(0.00000E+00, 0.00000E+00, 7.00000E+00, 1.10000E+01, 2.00000E+00, 1.00000E+00, 2.00000E+00, 6.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 4.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 4.00000E+00, 0.00000E+00, 1.40000E+01, 2.00000E+00), .Dim=c(23, 2)), Cum_D= structure(.Data= c(0.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 2.00000E+00, 2.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 6.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 6.00000E+00, 0.00000E+00), .Dim=c(23, 2)), C_MP= structure(.Data= c(1.57000E+02, 7.00000E+00, 5.08000E+02, 4.30000E+01, 5.07000E+02, 3.10000E+01, 3.22000E+02, 3.80000E+01, 4.42000E+02, 9.00000E+00, 1.67000E+02, 4.10000E+01, 1.51000E+02, 1.10000E+01, 2.28000E+02, 1.70000E+01, 4.20000E+02, 7.00000E+00, 1.30000E+02, 2.60000E+01, 7.61000E+02, 3.30000E+01, 3.35000E+02, 7.30000E+01, 6.62000E+02, 3.90000E+01, 3.18000E+02, 4.70000E+01, 1.90000E+02, 2.90000E+01, 1.87000E+02, 5.40000E+01, 5.33000E+02, 3.40000E+01, 3.07000E+02, 1.40000E+02, 2.27000E+02, 5.60000E+01, 3.94000E+02, 4.30000E+01, 4.33000E+02, 7.60000E+01, 3.91000E+02, 7.20000E+01, 3.86000E+02, 3.90000E+01), .Dim=c(23, 2)), Cm_MP= structure(.Data= c(1.56000E+02, 7.00000E+00, 5.00000E+02, 4.20000E+01, 5.02000E+02, 3.00000E+01, 3.20000E+02, 3.80000E+01, 4.42000E+02, 9.00000E+00, 1.66000E+02, 4.10000E+01, 1.51000E+02, 1.10000E+01, 2.26000E+02, 1.70000E+01, 4.20000E+02, 7.00000E+00, 1.30000E+02, 2.50000E+01, 7.61000E+02, 3.30000E+01, 3.35000E+02, 7.30000E+01, 6.61000E+02, 3.90000E+01, 3.18000E+02, 4.70000E+01, 1.90000E+02, 2.90000E+01, 1.87000E+02, 5.40000E+01, 5.33000E+02, 3.40000E+01, 3.07000E+02, 1.40000E+02, 2.27000E+02, 5.60000E+01, 3.93000E+02, 4.30000E+01, 4.33000E+02, 7.60000E+01, 3.91000E+02, 7.20000E+01, 3.85000E+02, 3.90000E+01), .Dim=c(23, 2)), Cum_MP= structure(.Data= c(0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00), .Dim=c(23, 2)), C_F= structure(.Data= c(4.10000E+01, 2.00000E+01, 7.50000E+01, 1.10000E+01, 9.20000E+01, 1.10000E+01, 3.50000E+01, 8.00000E+00, 7.00000E+01, 5.00000E+00, 2.50000E+01, 7.00000E+00, 4.10000E+01, 1.00000E+01, 3.10000E+01, 6.00000E+00, 2.40000E+01, 1.00000E+00), .Dim=c(9, 2)), Cm_F= structure(.Data= c(3.00000E+00, 0.00000E+00, 3.90000E+01, 3.00000E+00, 2.50000E+01, 0.00000E+00, 1.70000E+01, 2.00000E+00, 5.00000E+01, 2.00000E+00, 1.60000E+01, 0.00000E+00, 1.80000E+01, 1.00000E+00, 1.40000E+01, 1.00000E+00, 7.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 6.00000E+01, 1.00000E+00, 1.70000E+01, 1.60000E+01, 4.50000E+01, 5.00000E+00, 3.00000E+01, 5.00000E+00, 8.00000E+00, 2.00000E+00, 5.00000E+00, 5.00000E+00, 3.20000E+01, 0.00000E+00, 2.10000E+01, 5.00000E+00, 1.80000E+01, 5.00000E+00, 1.30000E+01, 2.00000E+00, 3.80000E+01, 4.00000E+00, 1.80000E+01, 1.00000E+01, 9.00000E+00, 2.00000E+00), .Dim=c(23, 2)), Cum_F= structure(.Data= c(1.40000E+01, 2.00000E+01, 1.00000E+01, 2.00000E+00, 8.00000E+00, 6.00000E+00, 7.00000E+00, 4.00000E+00, 5.00000E+00, 3.00000E+00, 4.00000E+00, 3.00000E+00, 1.90000E+01, 4.00000E+00, 4.00000E+00, 3.00000E+00, 5.00000E+00, 1.00000E+00, 0.00000E+00, 6.00000E+00, 2.60000E+01, 9.00000E+00, 1.00000E+01, 1.00000E+01, 2.50000E+01, 2.50000E+01, 2.00000E+01, 7.00000E+00, 4.00000E+00, 1.20000E+01, 0.00000E+00, 8.00000E+00, 2.60000E+01, 3.00000E+00, 7.00000E+00, 7.00000E+00, 1.60000E+01, 2.10000E+01, 1.60000E+01, 5.00000E+00, 1.80000E+01, 8.00000E+00, 3.40000E+01, 2.00000E+01, 2.40000E+01, 8.00000E+00), .Dim=c(23, 2)))
list(lambda_tot0=4.15700E+02, logit_effort_R=c(1.89700E-01, 1.62100E-01), logit_flow_MP=c(-6.31200E-02, -3.23200E-01), logit_flow_R=c(-3.84400E-01, -1.42000E-01), logit_int_MP=c(7.41500E-01, -4.10300E-01), logit_int_R=c(-2.18400E+00, -2.03600E+00), logit_pi_oF=c(2.22700E-01, 7.49300E-01, -4.88300E-01, 9.54100E-01, 1.07400E+00, 7.17700E-01, 1.44500E+00, 4.20900E-01, 7.48700E-01), mupi_oF=5.93300E-01, pi_MP94=c(4.43200E-01, 3.22000E-01), pi_oD=1.51100E-01, rate_lambda=1.21700E-02, rho_D=c(-5.85500E-01, 9.67400E-01), rho_F=c(-7.12300E-01, -1.43000E-02), s=c(2.22300E+01, 5.28300E+00), shape_lambda=7.15900E+00, sigmapi_D=c(1.65100E+00, 2.42800E-01), sigmapi_F=c(7.19000E-01, 1.11800E+00), sigmapi_MP=2.78000E-01, sigmapi_R=1.69300E-01, sigmapi_oF=5.86500E-01, m_F= structure(.Data= c(5.00000E+00, 2.00000E+00, 0.00000E+00, 2.10000E+01, 5.30000E+01, 2.00000E+00, 4.00000E+00, 2.20000E+01, 6.80000E+01, 2.00000E+00, 3.00000E+00, 2.40000E+01, 1.90000E+01, 1.00000E+00, 4.00000E+00, 1.30000E+01, 6.40000E+01, 0.00000E+00), .Dim=c(9, 2)), um_F= structure(.Data= c(3.60000E+01, 3.00000E+00, 2.00000E+01, 4.00000E+00, 2.20000E+01, 5.00000E+00, 7.00000E+00, 1.90000E+01, 2.40000E+01, 8.00000E+00, 8.00000E+00, 7.00000E+00, 1.60000E+01, 5.00000E+00, 4.00000E+00, 1.10000E+01, 6.00000E+00, 1.00000E+00), .Dim=c(9, 2)), mupi_D= structure(.Data= c(7.18500E-03, 3.95300E-01, 9.91700E-03, 1.23100E-01), .Dim=c(2, 2)), mupi_F= structure(.Data= c(6.53800E-02, 1.09600E-01, 1.01200E-01, 1.17900E-01), .Dim=c(2, 2)), lambda_tot=c(6.72000E+02, 1.22900E+03, 8.19000E+02, 6.26000E+02, 6.48000E+02, 4.40000E+02, 4.52000E+02, 4.12000E+02, 6.86000E+02, 3.72000E+02, 1.26200E+03, 6.49000E+02, 1.01600E+03, 6.34000E+02, 4.47000E+02, 4.84000E+02, 8.72000E+02, 6.38000E+02, 5.60000E+02, 7.71000E+02, 9.27000E+02, 8.32000E+02, 7.72000E+02), logit_piD_1SW= structure(.Data= c(-6.42000E+00, -6.42000E+00, -4.80600E+00, -6.89300E+00, -5.44800E+00, -5.44800E+00, -5.11000E+00, -6.21300E+00, -6.37000E+00, -6.37000E+00, -5.74900E+00, -5.74900E+00, -5.99400E+00, -5.99400E+00, -5.84100E+00, -5.84100E+00, -6.42600E+00, -6.42600E+00, -5.64200E+00, -5.64200E+00, -6.37300E+00, -6.37300E+00, -5.06500E+00, -6.16800E+00, -6.81300E+00, -6.81300E+00, -6.21300E+00, -6.21300E+00, -5.79600E+00, -5.79600E+00, -5.75600E+00, -5.75600E+00, -5.95700E+00, -5.95700E+00, -5.26300E+00, -5.95800E+00, -5.98400E+00, -5.98400E+00, -6.44300E+00, -6.44300E+00, -6.64600E+00, -6.64600E+00, -4.78200E+00, -5.70400E+00, -3.75500E+00, -4.53000E+00), .Dim=c(23, 2)), logit_piD_MSW= structure(.Data= c(-4.02500E+00, -3.31400E+00, -2.95800E+00, -4.38200E+00, -4.06900E+00, -3.65500E+00, -2.83300E+00, -4.12700E+00, -4.12700E+00, -4.12700E+00, -3.17800E+00, -3.70500E+00, -2.75200E+00, -1.81500E+00, -4.19000E+00, -4.19000E+00, -4.19000E+00, -4.19000E+00, -3.77300E+00, -3.35600E+00, -4.46600E+00, -4.46600E+00, -4.43700E+00, -5.13600E+00, -4.64400E+00, -4.64400E+00, -4.89000E+00, -4.89000E+00, -4.75400E+00, -4.75400E+00, -5.11200E+00, -5.11200E+00, -4.56400E+00, -4.56400E+00, -4.82000E+00, -4.82000E+00, -4.38200E+00, -5.08100E+00, -4.94900E+00, -4.94900E+00, -5.04300E+00, -5.04300E+00, -5.43400E+00, -4.73600E+00, -3.62900E+00, -4.74500E+00), .Dim=c(23, 2)), logit_piF_1SW= structure(.Data= c(-4.62000E+00, -2.74900E+00, -6.89300E+00, -3.82800E+00, -2.48200E+00, -3.38200E+00, -4.59500E+00, -4.11900E+00, -2.01200E+00, -3.10900E+00, -4.35300E+00, -3.52600E+00, -2.95000E+00, -3.12000E+00, -4.22000E+00, -4.22000E+00, -2.14300E+00, -4.47100E+00, NA, NA, -2.90400E+00, -3.74900E+00, -3.24100E+00, -3.74800E+00, -2.93400E+00, -3.52700E+00, -2.71700E+00, -3.12700E+00, -3.57400E+00, -4.17400E+00, -3.94800E+00, -5.75600E+00, -3.11300E+00, -3.32200E+00, -2.81200E+00, -3.86100E+00, -2.99300E+00, -3.11000E+00, -3.78300E+00, -3.58400E+00, -2.93200E+00, -3.67800E+00, -3.42400E+00, -2.78500E+00, -4.16800E+00, -3.22800E+00), .Dim=c(23, 2)), logit_piF_MSW= structure(.Data= c(-2.89000E+00, -2.58400E+00, -2.30700E+00, -3.86300E+00, -3.65500E+00, -2.93600E+00, -1.49900E+00, -1.66800E+00, -2.99600E+00, -1.79200E+00, -1.38600E+00, -2.68300E+00, -3.17800E+00, -1.99200E+00, -1.33100E+00, -1.52200E+00, -4.19000E+00, -3.48100E+00, -3.77300E+00, -2.46100E+00, -3.76100E+00, -2.05400E+00, -2.20400E+00, -2.67700E+00, -2.80300E+00, -1.11100E+00, -3.06000E+00, -2.75700E+00, -3.63800E+00, -2.07900E+00, -3.29000E+00, -2.86500E+00, -4.56400E+00, -3.14600E+00, -3.70500E+00, -3.40900E+00, -3.25800E+00, -1.85100E+00, -3.83600E+00, -3.12100E+00, -3.40800E+00, -2.79300E+00, -2.99100E+00, -2.29800E+00, -3.62900E+00, -2.47600E+00), .Dim=c(23, 2)), logit_pi_MP= structure(.Data= c(-1.07900E+00, -1.96600E+00, 2.84000E-02, -1.56600E+00, 9.30300E-01, -1.08700E+00, 5.75400E-01, -8.39800E-01, 1.12800E+00, -1.79200E+00, 1.08000E-01, -7.17200E-01, -5.08200E-01, -1.26600E+00, 6.41400E-01, -1.07900E+00, 7.46900E-01, -2.14800E+00, -1.62900E-01, -9.40000E-01, 6.11200E-01, -5.10800E-01, 8.51300E-01, -2.94500E-01, 9.72300E-01, -5.26100E-01, 5.58000E-01, -6.15800E-01, 3.05400E-01, -1.11000E+00, 3.63600E-01, -7.38400E-01, 7.89600E-01, -6.16800E-01, 1.33200E+00, 2.41200E-01, 2.83300E-01, -6.38100E-01, 5.10000E-01, -8.33900E-01, 2.47700E-01, -5.12900E-02, 6.16800E-01, -7.85900E-01, 3.51100E-01, -6.80200E-01), .Dim=c(23, 2)), logit_pi_R= structure(.Data= c(-3.61800E+00, -1.50000E+00, -2.61300E+00, -3.29600E+00, -2.25700E+00, -2.15900E+00, -2.69700E+00, -1.50000E+00, -1.79200E+00, -1.79200E+00, -2.12500E+00, -1.85600E+00, -3.24300E+00, -1.50000E+00, -1.69100E+00, -1.50000E+00, -2.54700E+00, -1.79200E+00, -1.29300E+00, -1.28100E+00, -1.45000E+00, -1.68600E+00, -1.31300E+00, -1.79200E+00, -2.19000E+00, -1.54000E+00, -1.97800E+00, -2.25100E+00, -1.98400E+00, -1.48200E+00, -2.56500E+00, -2.17500E+00, -1.62900E+00, -2.01500E+00, -2.21700E+00, -2.23100E+00, -2.63400E+00, -2.44200E+00, -1.84200E+00, -1.76400E+00, -2.00200E+00, -1.42100E+00, -1.85300E+00, -2.06100E+00, -1.72000E+00, -1.57600E+00), .Dim=c(23, 2)), m_D= structure(.Data= c(0.00000E+00, 0.00000E+00, 7.00000E+00, 1.10000E+01, 2.00000E+00, 1.00000E+00, 2.00000E+00, 6.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 4.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 4.00000E+00, 0.00000E+00, 1.40000E+01, 2.00000E+00), .Dim=c(23, 2)), n= structure(.Data= c(6.15000E+02, 5.70000E+01, 9.86000E+02, 2.43000E+02, 7.00000E+02, 1.19000E+02, 5.00000E+02, 1.26000E+02, 5.85000E+02, 6.30000E+01, 3.15000E+02, 1.25000E+02, 4.02000E+02, 5.00000E+01, 3.45000E+02, 6.70000E+01, 6.19000E+02, 6.70000E+01, 2.83000E+02, 8.90000E+01, 1.17400E+03, 8.80000E+01, 4.78000E+02, 1.71000E+02, 9.11000E+02, 1.05000E+02, 5.00000E+02, 1.34000E+02, 3.30000E+02, 1.17000E+02, 3.17000E+02, 1.67000E+02, 7.75000E+02, 9.70000E+01, 3.88000E+02, 2.50000E+02, 3.98000E+02, 1.62000E+02, 6.29000E+02, 1.42000E+02, 7.71000E+02, 1.56000E+02, 6.02000E+02, 2.30000E+02, 6.56000E+02, 1.16000E+02), .Dim=c(23, 2)), um_D= structure(.Data= c(0.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 2.00000E+00, 2.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 6.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 6.00000E+00, 0.00000E+00), .Dim=c(23, 2)))
This diff is collapsed.
modelCheck('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Scorff/adult/bugs/model_adult-Scorff.R.txt')
modelData('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Scorff/adult/bugs/data.txt')
modelCompile(1)
modelSetRN(1)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Scorff/adult/bugs/inits1.txt',1)
modelGenInits()
modelUpdate(1000,1,1000)
samplesSet(logit_int_MP)
samplesSet(logit_flow_MP)
samplesSet(sigmapi_MP)
samplesSet(mupi_F)
samplesSet(sigmapi_F)
samplesSet(rho_F)
samplesSet(logit_int_R)
samplesSet(logit_effort_R)
samplesSet(logit_flow_R)
samplesSet(sigmapi_R)
samplesSet(mupi_D)
samplesSet(sigmapi_D)
samplesSet(rho_D)
samplesSet(mupi_oF)
samplesSet(sigmapi_oF)
samplesSet(test)
samplesSet(diffF_1SW)
samplesSet(diffF_MSW)
samplesSet(diff1SW)
samplesSet(diffMSW)
samplesSet(pi_MP)
samplesSet(pi_MP94)
samplesSet(p_MP94_tot)
samplesSet(epsilon_MP)
samplesSet(pi_oF)
samplesSet(piF_1SW)
samplesSet(piF_MSW)
samplesSet(pi_oD)
samplesSet(piD_1SW)
samplesSet(piD_MSW)
samplesSet(pi_R)
samplesSet(epsilon_R)
samplesSet(n_tot)
samplesSet(n_1SW)
samplesSet(n_MSW)
samplesSet(shape_lambda)
samplesSet(rate_lambda)
samplesSet(lambda_tot0)
samplesSet(Plambda0)
samplesSet(lambda_tot)
samplesSet(Plambda)
samplesSet(s)
samplesSet(e_tot)
samplesSet(e_1SW)
samplesSet(e_MSW)
summarySet(logit_int_MP)
summarySet(logit_flow_MP)
summarySet(sigmapi_MP)
summarySet(mupi_F)
summarySet(sigmapi_F)
summarySet(rho_F)
summarySet(logit_int_R)
summarySet(logit_effort_R)
summarySet(logit_flow_R)
summarySet(sigmapi_R)
summarySet(mupi_D)
summarySet(sigmapi_D)
summarySet(rho_D)
summarySet(mupi_oF)
summarySet(sigmapi_oF)
summarySet(test)
summarySet(diffF_1SW)
summarySet(diffF_MSW)
summarySet(diff1SW)
summarySet(diffMSW)
summarySet(pi_MP)
summarySet(pi_MP94)
summarySet(p_MP94_tot)
summarySet(epsilon_MP)
summarySet(pi_oF)
summarySet(piF_1SW)
summarySet(piF_MSW)
summarySet(pi_oD)
summarySet(piD_1SW)
summarySet(piD_MSW)
summarySet(pi_R)
summarySet(epsilon_R)
summarySet(n_tot)
summarySet(n_1SW)
summarySet(n_MSW)
summarySet(shape_lambda)
summarySet(rate_lambda)
summarySet(lambda_tot0)
summarySet(Plambda0)
summarySet(lambda_tot)
summarySet(Plambda)
summarySet(s)
summarySet(e_tot)
summarySet(e_1SW)
summarySet(e_MSW)
modelUpdate(5000,1,5000)
samplesCoda('*', '/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Scorff/adult/bugs//')
summaryStats('*')
modelQuit('y')
list(lambda_tot0=4.157E+02, logit_effort_R=c(1.897E-01, 1.621E-01), logit_flow_MP=c(-6.312E-02, -3.232E-01), logit_flow_R=c(-3.844E-01, -1.420E-01), logit_int_MP=c(7.415E-01, -4.103E-01), logit_int_R=c(-2.184E+00, -2.036E+00), logit_pi_oF=c(2.227E-01, 7.493E-01, -4.883E-01, 9.541E-01, 1.074E+00, 7.177E-01, 1.445E+00, 4.209E-01, 7.487E-01), mupi_oF=5.933E-01, pi_MP94=c(4.432E-01, 3.220E-01), pi_oD=1.511E-01, rate_lambda=1.217E-02, rho_D=c(-5.855E-01, 9.674E-01), rho_F=c(-7.123E-01, -1.430E-02), s=c(2.223E+01, 5.283E+00), shape_lambda=7.159E+00, sigmapi_D=c(1.651E+00, 2.428E-01), sigmapi_F=c(7.190E-01, 1.118E+00), sigmapi_MP=2.780E-01, sigmapi_R=1.693E-01, sigmapi_oF=5.865E-01, m_F= structure(.Data= c(5.000E+00, 2.000E+00, 0.000E+00, 2.100E+01, 5.300E+01, 2.000E+00, 4.000E+00, 2.200E+01, 6.800E+01, 2.000E+00, 3.000E+00, 2.400E+01, 1.900E+01, 1.000E+00, 4.000E+00, 1.300E+01, 6.400E+01, 0.000E+00), .Dim=c(9, 2)), um_F= structure(.Data= c(3.600E+01, 3.000E+00, 2.000E+01, 4.000E+00, 2.200E+01, 5.000E+00, 7.000E+00, 1.900E+01, 2.400E+01, 8.000E+00, 8.000E+00, 7.000E+00, 1.600E+01, 5.000E+00, 4.000E+00, 1.100E+01, 6.000E+00, 1.000E+00), .Dim=c(9, 2)), mupi_D= structure(.Data= c(7.185E-03, 3.953E-01, 9.917E-03, 1.231E-01), .Dim=c(2, 2)), mupi_F= structure(.Data= c(6.538E-02, 1.096E-01, 1.012E-01, 1.179E-01), .Dim=c(2, 2)), lambda_tot=c(6.720E+02, 1.229E+03, 8.190E+02, 6.260E+02, 6.480E+02, 4.400E+02, 4.520E+02, 4.120E+02, 6.860E+02, 3.720E+02, 1.262E+03, 6.490E+02, 1.016E+03, 6.340E+02, 4.470E+02, 4.840E+02, 8.720E+02, 6.380E+02, 5.600E+02, 7.710E+02, 9.270E+02, 8.320E+02, 7.720E+02), logit_piD_1SW= structure(.Data= c(-6.420E+00, -6.420E+00, -4.806E+00, -6.893E+00, -5.448E+00, -5.448E+00, -5.110E+00, -6.213E+00, -6.370E+00, -6.370E+00, -5.749E+00, -5.749E+00, -5.994E+00, -5.994E+00, -5.841E+00, -5.841E+00, -6.426E+00, -6.426E+00, -5.642E+00, -5.642E+00, -6.373E+00, -6.373E+00, -5.065E+00, -6.168E+00, -6.813E+00, -6.813E+00, -6.213E+00, -6.213E+00, -5.796E+00, -5.796E+00, -5.756E+00, -5.756E+00, -5.957E+00, -5.957E+00, -5.263E+00, -5.958E+00, -5.984E+00, -5.984E+00, -6.443E+00, -6.443E+00, -6.646E+00, -6.646E+00, -4.782E+00, -5.704E+00, -3.755E+00, -4.530E+00), .Dim=c(23, 2)), logit_piD_MSW= structure(.Data= c(-4.025E+00, -3.314E+00, -2.958E+00, -4.382E+00, -4.069E+00, -3.655E+00, -2.833E+00, -4.127E+00, -4.127E+00, -4.127E+00, -3.178E+00, -3.705E+00, -2.752E+00, -1.815E+00, -4.190E+00, -4.190E+00, -4.190E+00, -4.190E+00, -3.773E+00, -3.356E+00, -4.466E+00, -4.466E+00, -4.437E+00, -5.136E+00, -4.644E+00, -4.644E+00, -4.890E+00, -4.890E+00, -4.754E+00, -4.754E+00, -5.112E+00, -5.112E+00, -4.564E+00, -4.564E+00, -4.820E+00, -4.820E+00, -4.382E+00, -5.081E+00, -4.949E+00, -4.949E+00, -5.043E+00, -5.043E+00, -5.434E+00, -4.736E+00, -3.629E+00, -4.745E+00), .Dim=c(23, 2)), logit_piF_1SW= structure(.Data= c(-4.620E+00, -2.749E+00, -6.893E+00, -3.828E+00, -2.482E+00, -3.382E+00, -4.595E+00, -4.119E+00, -2.012E+00, -3.109E+00, -4.353E+00, -3.526E+00, -2.950E+00, -3.120E+00, -4.220E+00, -4.220E+00, -2.143E+00, -4.471E+00, NA, NA, -2.904E+00, -3.749E+00, -3.241E+00, -3.748E+00, -2.934E+00, -3.527E+00, -2.717E+00, -3.127E+00, -3.574E+00, -4.174E+00, -3.948E+00, -5.756E+00, -3.113E+00, -3.322E+00, -2.812E+00, -3.861E+00, -2.993E+00, -3.110E+00, -3.783E+00, -3.584E+00, -2.932E+00, -3.678E+00, -3.424E+00, -2.785E+00, -4.168E+00, -3.228E+00), .Dim=c(23, 2)), logit_piF_MSW= structure(.Data= c(-2.890E+00, -2.584E+00, -2.307E+00, -3.863E+00, -3.655E+00, -2.936E+00, -1.499E+00, -1.668E+00, -2.996E+00, -1.792E+00, -1.386E+00, -2.683E+00, -3.178E+00, -1.992E+00, -1.331E+00, -1.522E+00, -4.190E+00, -3.481E+00, -3.773E+00, -2.461E+00, -3.761E+00, -2.054E+00, -2.204E+00, -2.677E+00, -2.803E+00, -1.111E+00, -3.060E+00, -2.757E+00, -3.638E+00, -2.079E+00, -3.290E+00, -2.865E+00, -4.564E+00, -3.146E+00, -3.705E+00, -3.409E+00, -3.258E+00, -1.851E+00, -3.836E+00, -3.121E+00, -3.408E+00, -2.793E+00, -2.991E+00, -2.298E+00, -3.629E+00, -2.476E+00), .Dim=c(23, 2)), logit_pi_MP= structure(.Data= c(-1.079E+00, -1.966E+00, 2.840E-02, -1.566E+00, 9.303E-01, -1.087E+00, 5.754E-01, -8.398E-01, 1.128E+00, -1.792E+00, 1.080E-01, -7.172E-01, -5.082E-01, -1.266E+00, 6.414E-01, -1.079E+00, 7.469E-01, -2.148E+00, -1.629E-01, -9.400E-01, 6.112E-01, -5.108E-01, 8.513E-01, -2.945E-01, 9.723E-01, -5.261E-01, 5.580E-01, -6.158E-01, 3.054E-01, -1.110E+00, 3.636E-01, -7.384E-01, 7.896E-01, -6.168E-01, 1.332E+00, 2.412E-01, 2.833E-01, -6.381E-01, 5.100E-01, -8.339E-01, 2.477E-01, -5.129E-02, 6.168E-01, -7.859E-01, 3.511E-01, -6.802E-01), .Dim=c(23, 2)), logit_pi_R= structure(.Data= c(-3.618E+00, -1.500E+00, -2.613E+00, -3.296E+00, -2.257E+00, -2.159E+00, -2.697E+00, -1.500E+00, -1.792E+00, -1.792E+00, -2.125E+00, -1.856E+00, -3.243E+00, -1.500E+00, -1.691E+00, -1.500E+00, -2.547E+00, -1.792E+00, -1.293E+00, -1.281E+00, -1.450E+00, -1.686E+00, -1.313E+00, -1.792E+00, -2.190E+00, -1.540E+00, -1.978E+00, -2.251E+00, -1.984E+00, -1.482E+00, -2.565E+00, -2.175E+00, -1.629E+00, -2.015E+00, -2.217E+00, -2.231E+00, -2.634E+00, -2.442E+00, -1.842E+00, -1.764E+00, -2.002E+00, -1.421E+00, -1.853E+00, -2.061E+00, -1.720E+00, -1.576E+00), .Dim=c(23, 2)), m_D= structure(.Data= c(0.000E+00, 0.000E+00, 7.000E+00, 1.100E+01, 2.000E+00, 1.000E+00, 2.000E+00, 6.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 4.000E+00, 0.000E+00, 2.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 1.000E+00, 1.000E+00, 0.000E+00, 2.000E+00, 1.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 1.000E+00, 0.000E+00, 1.000E+00, 1.000E+00, 0.000E+00, 1.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 4.000E+00, 0.000E+00, 1.400E+01, 2.000E+00), .Dim=c(23, 2)), n= structure(.Data= c(6.150E+02, 5.700E+01, 9.860E+02, 2.430E+02, 7.000E+02, 1.190E+02, 5.000E+02, 1.260E+02, 5.850E+02, 6.300E+01, 3.150E+02, 1.250E+02, 4.020E+02, 5.000E+01, 3.450E+02, 6.700E+01, 6.190E+02, 6.700E+01, 2.830E+02, 8.900E+01, 1.174E+03, 8.800E+01, 4.780E+02, 1.710E+02, 9.110E+02, 1.050E+02, 5.000E+02, 1.340E+02, 3.300E+02, 1.170E+02, 3.170E+02, 1.670E+02, 7.750E+02, 9.700E+01, 3.880E+02, 2.500E+02, 3.980E+02, 1.620E+02, 6.290E+02, 1.420E+02, 7.710E+02, 1.560E+02, 6.020E+02, 2.300E+02, 6.560E+02, 1.160E+02), .Dim=c(23, 2)), um_D= structure(.Data= c(0.000E+00, 1.000E+00, 0.000E+00, 2.000E+00, 2.000E+00, 2.000E+00, 0.000E+00, 1.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 2.000E+00, 0.000E+00, 6.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 2.000E+00, 1.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 1.000E+00, 0.000E+00, 0.000E+00, 1.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 0.000E+00, 1.000E+00, 1.000E+00, 6.000E+00, 0.000E+00), .Dim=c(23, 2)))
......@@ -282,5 +282,7 @@ inits_updated <- list(
inits <- list(c( inits_fix,inits_updated))
save(inits,file=paste(paste('inits/inits_',stade,'.Rdata',sep="")))
#save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
bugs.inits(inits, n.chains=1,digits=3, inits.files = paste('inits/init-',site,'-',stade,year,'.txt',sep=""))
......@@ -12,33 +12,33 @@ heidel.diag also implements a convergence diagnostic, and removes up to half the
Stationarity start p-value
test iteration
sigmapi_MP passed 1 0.29851
sigmapi_R failed NA 0.00127
mupi_oF passed 1 0.14574
sigmapi_oF passed 1 0.28920
diffF_1SW passed 1 0.11100
diffF_MSW passed 1 0.11554
diff1SW passed 501 0.05261
diffMSW passed 1 0.40440
pi_oD passed 1 0.13998
shape_lambda passed 1 0.89298
rate_lambda passed 1 0.89803
lambda_tot0 passed 1 0.31651
Halfwidth Mean Halfwidth
test
sigmapi_MP passed 0.3491 2.25e-02
sigmapi_R <NA> NA NA
mupi_oF passed 0.7872 3.75e-02
sigmapi_oF failed 1.1734 5.64e-01
diffF_1SW failed -0.1038 1.53e-02
diffF_MSW passed -0.8611 2.92e-02
diff1SW failed 0.5074 1.06e-01
diffMSW failed 0.9013 1.11e-01
pi_oD passed 0.2146 1.72e-02
shape_lambda passed 6.4713 3.11e-01
rate_lambda passed 0.0104 5.17e-04
lambda_tot0 passed 616.8054 2.77e+01
sigmapi_MP passed 1 0.0631
sigmapi_R passed 1 0.3471
mupi_oF passed 2001 0.1701
sigmapi_oF passed 1 0.0617
diffF_1SW passed 1 0.2121
diffF_MSW passed 1 0.3396
diff1SW passed 1 0.2906
diffMSW passed 2001 0.1825
pi_oD passed 1 0.0798
shape_lambda passed 1 0.1801
rate_lambda passed 1 0.1355
lambda_tot0 passed 1 0.6136
Halfwidth Mean Halfwidth
test
sigmapi_MP passed 0.35274 1.73e-02
sigmapi_R passed 0.37165 6.28e-03
mupi_oF passed 0.83583 3.36e-02
sigmapi_oF failed 2.01282 3.99e-01
diffF_1SW passed -0.22315 1.58e-02
diffF_MSW passed -0.93534 4.52e-02
diff1SW failed 0.47186 8.08e-02
diffMSW passed 0.90697 9.01e-02
pi_oD passed 0.19594 1.80e-02
shape_lambda passed 6.21314 2.65e-01
rate_lambda passed 0.00994 4.42e-04
lambda_tot0 passed 634.08820 2.85e+01
---------------------------
Geweke's convergence diagnostic
......@@ -54,9 +54,9 @@ Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
sigmapi_MP sigmapi_R mupi_oF sigmapi_oF diffF_1SW diffF_MSW diff1SW diffMSW pi_oD
-0.612 0.448 2.261 0.633 2.681 3.960 3.518 0.110 -2.038
-2.386 -1.801 -3.473 -3.028 0.727 -1.230 -0.590 -0.213 3.160
shape_lambda rate_lambda lambda_tot0
0.316 0.215 -1.870
0.647 0.559 1.148
---------------------------
......@@ -68,16 +68,16 @@ Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
sigmapi_MP 108 101576 3746 27.10
sigmapi_R 12 17367 3746 4.64
mupi_oF 12 13347 3746 3.56
sigmapi_oF 91 101052 3746 27.00
diffF_1SW 8 8928 3746 2.38
diffF_MSW 8 9864 3746 2.63
diff1SW 20 21575 3746 5.76
diffMSW 25 26975 3746 7.20
pi_oD 24 22008 3746 5.88
shape_lambda 15 16561 3746 4.42
rate_lambda 22 23208 3746 6.20
lambda_tot0 16 18154 3746 4.85
sigmapi_MP 32 30836 3746 8.23
sigmapi_R 8 11354 3746 3.03
mupi_oF 7 7397 3746 1.97
sigmapi_oF 121 131736 3746 35.20
diffF_1SW 5 5391 3746 1.44
diffF_MSW 24 25796 3746 6.89
diff1SW 21 24552 3746 6.55
diffMSW 12 12362 3746 3.30
pi_oD 35 34530 3746 9.22
shape_lambda 16 17809 3746 4.75
rate_lambda 18 21819 3746 5.82
lambda_tot0 24 24390 3746 6.51
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