Commit cf2291f5 authored by matbuoro's avatar matbuoro
Browse files

Update 2016

parent 95c5511e
......@@ -45,7 +45,7 @@ if(!file.exists(paste("inits/init-",site,"-",stade,year,".txt",sep=""))){
#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.tmp <- read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep=""))
inits <- rep(list(inits.tmp),3)
inits <- rep(list(inits.tmp),2)
#------------------------MODEL----------------------------------##
model <- paste("model/model_",stade,"-",site,".R",sep="") # path of the model
......@@ -57,28 +57,43 @@ filename <- file.path(work.dir, model)
#---------------------------ANALYSIS-----------------------------##
nChains = 3 #length(inits) # Number of chains to run.
nChains = 2 #length(inits) # Number of chains to run.
adaptSteps = 1000 # Number of steps to "tune" the samplers.
nburnin=5000 # Number of steps to "burn-in" the samplers.
nstore=25000 # Total number of steps in chains to save.
nthin=4 # Number of steps to "thin" (1=keep every step).
nburnin=1000 # Number of steps to "burn-in" the samplers.
nstore=5000 # Total number of steps in chains to save.
nthin=1 # Number of steps to "thin" (1=keep every step).
#nPerChain = ceiling( ( numSavedSteps * thinSteps ) / nChains ) # Steps per chain.
### Start of the run ###
start.time = Sys.time(); cat("Start of the run\n");
######### BUGS ##########
fit <- bugs(
data
,inits
,model.file = filename
,parameters
,n.chains = nChains, n.iter = nstore + nburnin, n.burnin = nburnin, n.thin = nthin
,DIC=FALSE
,codaPkg = FALSE, clearWD=FALSE
#,debug=TRUE
,working.directory=paste(work.dir,"bugs",sep="/")
# fit <- bugs(
# data
# ,inits
# ,model.file = filename
# ,parameters
# ,n.chains = nChains, n.iter = nstore + nburnin, n.burnin = nburnin, n.thin = nthin
# ,DIC=FALSE
# ,codaPkg = FALSE, clearWD=FALSE
# #,debug=TRUE
# ,working.directory=paste(work.dir,"bugs",sep="/")
# )
SEED <- floor(runif(2, 100000, 999999))
cl <- makePSOCKcluster(nChains)
## fitting the model with OpenBUGS
## using the preferred R2OpenBUGS interface
fit <- bugs.parfit(cl, data, parameters, filename,
n.iter=nstore + nburnin, n.thin=nthin,
seed=SEED,
program="openbugs",
DIC=FALSE,
#codaPkg = FALSE, clearWD=FALSE,
#debug=TRUE,
working.directory=paste(work.dir,"bugs",sep="/")
)
stopCluster(cl)
## cleaning
system("rm bugs/CODA*")
......
OpenBUGS did not run correctly.
OpenBUGS did not run correctly.
OpenBUGS did not run correctly.
list(lambda_tot0=1.04200E+02, logit_flow_Eu=c(-4.26200E-01, -7.07900E-01), lflow_fall_Eu=c(-5.00000E-01, -5.00000E-01), logit_int_Eu=c(5.08200E-01, 8.30400E-01), mupi_B=c(8.27000E-02, 1.03200E-01), pi_Eu00=c(5.87100E-01, 5.99200E-01), pi_Eu01=c(2.00600E-01, 7.99400E-01), rate_lambda=4.23800E-02, s=c(1.56700E+01, 3.91400E+00), shape_lambda=5.99500E+00, sigmapi_B=c(6.44000E-01, 6.96900E-01), sigmapi_Eu=c(1.01900E+00, 8.86500E-01), lambda_tot=c(1.02000E+02, 1.15500E+02, 2.20800E+02, 1.76200E+02, 1.30000E+02, NA, 1.24500E+02, 1.94300E+02, 1.57000E+02, NA, 9.30000E+01, 1.44000E+02, 6.23300E+01, 3.40000E+01, 2.80500E+02, 1.75000E+02, NA, NA, 1.30300E+02, 2.30000E+01, 8.72300E+01, 3.18200E+02, 2.43200E+02, 2.64000E+02, 1.62000E+02, 1.09400E+02, 1.93500E+02, 1.47000E+02, 1.51200E+02, 2.97000E+02, 2.02400E+02, 1.58900E+02, 1.96500E+02), logit_pi_Eu= structure(.Data= c(1.68200E+00, 3.92200E-02, NA, -7.12400E-01, NA, -1.57100E+00, NA, -9.26000E-01, 1.48500E+00, -1.48500E+00, NA, NA, 1.40600E+00, 1.20600E-01, NA, -1.41400E+00, NA, -2.17400E-01, NA, NA, 2.37700E-01, -2.11600E+00, -7.56300E-01, -4.26300E+00, 3.12700E-01, 5.34900E-02, NA, 3.56700E-01, NA, -2.56700E+00, -1.83900E+00, -1.74600E+00, NA, NA, NA, NA, 1.81000E+00, -1.38900E+00, NA, 1.89700E+00, NA, -1.34700E+00, 7.63400E+00, -1.45900E+00, 4.06300E-01, 1.71000E-01, -1.18100E+00, -2.21400E+00, 3.25800E+00, -3.25800E+00, NA, 4.95300E-01, NA, -1.10900E+00, NA, -7.23900E-01, NA, -7.66000E-01, 5.16200E-01, -9.30500E-01, 5.42600E-01, -9.61000E-01, NA, -3.41400E-01, NA, -1.71400E+00), .Dim=c(33, 2)), logit_pi_B= structure(.Data= c(-2.46400E+00, -4.61500E+00, -1.33800E+00, -4.74000E+00, -2.69300E+00, -4.69500E+00, -2.92200E+00, -3.76200E+00, -4.86000E+00, -4.86000E+00, NA, NA, -3.70100E+00, NA, -2.91400E+00, NA, -3.41400E+00, -4.35000E+00, NA, NA, -3.81800E+00, -4.52200E+00, -2.01100E+00, -4.96300E+00, -2.24000E+00, -2.43900E+00, -3.49700E+00, NA, -3.10800E+00, -4.52700E+00, -4.46000E+00, -3.52600E+00, NA, NA, NA, NA, -1.43800E+00, -2.60100E+00, NA, -3.09100E+00, -1.34700E+00, -3.33500E+00, -1.43900E+00, -4.13700E+00, -2.46800E+00, -3.25900E+00, -3.76100E+00, NA, -2.83300E+00, -5.08100E+00, -2.00400E+00, -3.27200E+00, -2.63100E+00, -3.63000E+00, -2.17500E+00, -4.98400E+00, -2.76000E+00, -5.01200E+00, -3.58700E+00, -4.58500E+00, -3.48800E+00, -3.67600E+00, -2.50500E+00, -3.65600E+00, -2.82500E+00, -5.27600E+00), .Dim=c(33, 2)), n= structure(.Data= c(6.40000E+01, 3.90000E+01, 9.30000E+01, 2.30000E+01, 1.91000E+02, 3.10000E+01, 1.41000E+02, 3.60000E+01, 1.06000E+02, 2.40000E+01, NA, NA, 7.50000E+01, 5.00000E+01, 1.68000E+02, 2.70000E+01, 1.23000E+02, 3.50000E+01, NA, NA, 7.80000E+01, 1.50000E+01, 1.38000E+02, 6.00000E+00, 3.30000E+01, 3.00000E+01, 2.40000E+01, 1.00000E+01, 2.66000E+02, 1.50000E+01, 8.40000E+01, 9.10000E+01, NA, NA, NA, NA, 1.06000E+02, 2.50000E+01, 1.30000E+01, 1.00000E+01, 7.30000E+01, 1.50000E+01, 2.68000E+02, 5.10000E+01, 1.28000E+02, 1.16000E+02, 1.86000E+02, 7.80000E+01, 1.56000E+02, 6.00000E+00, 7.50000E+01, 3.60000E+01, 1.58000E+02, 3.60000E+01, 1.23000E+02, 2.40000E+01, 1.22000E+02, 3.00000E+01, 2.05000E+02, 9.30000E+01, 1.41000E+02, 6.20000E+01, 1.22000E+02, 3.80000E+01, 1.74000E+02, 2.30000E+01), .Dim=c(33, 2)))
list(lambda_tot0=1.04200E+02, logit_flow_Eu=c(-4.26200E-01, -7.07900E-01), lflow_fall_Eu=c(-5.00000E-01, -5.00000E-01), logit_int_Eu=c(5.08200E-01, 8.30400E-01), mupi_B=c(8.27000E-02, 1.03200E-01), pi_Eu00=c(5.87100E-01, 5.99200E-01), pi_Eu01=c(2.00600E-01, 7.99400E-01), rate_lambda=4.23800E-02, s=c(1.56700E+01, 3.91400E+00), shape_lambda=5.99500E+00, sigmapi_B=c(6.44000E-01, 6.96900E-01), sigmapi_Eu=c(1.01900E+00, 8.86500E-01), lambda_tot=c(1.02000E+02, 1.15500E+02, 2.20800E+02, 1.76200E+02, 1.30000E+02, NA, 1.24500E+02, 1.94300E+02, 1.57000E+02, NA, 9.30000E+01, 1.44000E+02, 6.23300E+01, 3.40000E+01, 2.80500E+02, 1.75000E+02, NA, NA, 1.30300E+02, 2.30000E+01, 8.72300E+01, 3.18200E+02, 2.43200E+02, 2.64000E+02, 1.62000E+02, 1.09400E+02, 1.93500E+02, 1.47000E+02, 1.51200E+02, 2.97000E+02, 2.02400E+02, 1.58900E+02, 1.96500E+02), logit_pi_Eu= structure(.Data= c(1.68200E+00, 3.92200E-02, NA, -7.12400E-01, NA, -1.57100E+00, NA, -9.26000E-01, 1.48500E+00, -1.48500E+00, NA, NA, 1.40600E+00, 1.20600E-01, NA, -1.41400E+00, NA, -2.17400E-01, NA, NA, 2.37700E-01, -2.11600E+00, -7.56300E-01, -4.26300E+00, 3.12700E-01, 5.34900E-02, NA, 3.56700E-01, NA, -2.56700E+00, -1.83900E+00, -1.74600E+00, NA, NA, NA, NA, 1.81000E+00, -1.38900E+00, NA, 1.89700E+00, NA, -1.34700E+00, 7.63400E+00, -1.45900E+00, 4.06300E-01, 1.71000E-01, -1.18100E+00, -2.21400E+00, 3.25800E+00, -3.25800E+00, NA, 4.95300E-01, NA, -1.10900E+00, NA, -7.23900E-01, NA, -7.66000E-01, 5.16200E-01, -9.30500E-01, 5.42600E-01, -9.61000E-01, NA, -3.41400E-01, NA, -1.71400E+00), .Dim=c(33, 2)), logit_pi_B= structure(.Data= c(-2.46400E+00, -4.61500E+00, -1.33800E+00, -4.74000E+00, -2.69300E+00, -4.69500E+00, -2.92200E+00, -3.76200E+00, -4.86000E+00, -4.86000E+00, NA, NA, -3.70100E+00, NA, -2.91400E+00, NA, -3.41400E+00, -4.35000E+00, NA, NA, -3.81800E+00, -4.52200E+00, -2.01100E+00, -4.96300E+00, -2.24000E+00, -2.43900E+00, -3.49700E+00, NA, -3.10800E+00, -4.52700E+00, -4.46000E+00, -3.52600E+00, NA, NA, NA, NA, -1.43800E+00, -2.60100E+00, NA, -3.09100E+00, -1.34700E+00, -3.33500E+00, -1.43900E+00, -4.13700E+00, -2.46800E+00, -3.25900E+00, -3.76100E+00, NA, -2.83300E+00, -5.08100E+00, -2.00400E+00, -3.27200E+00, -2.63100E+00, -3.63000E+00, -2.17500E+00, -4.98400E+00, -2.76000E+00, -5.01200E+00, -3.58700E+00, -4.58500E+00, -3.48800E+00, -3.67600E+00, -2.50500E+00, -3.65600E+00, -2.82500E+00, -5.27600E+00), .Dim=c(33, 2)), n= structure(.Data= c(6.40000E+01, 3.90000E+01, 9.30000E+01, 2.30000E+01, 1.91000E+02, 3.10000E+01, 1.41000E+02, 3.60000E+01, 1.06000E+02, 2.40000E+01, NA, NA, 7.50000E+01, 5.00000E+01, 1.68000E+02, 2.70000E+01, 1.23000E+02, 3.50000E+01, NA, NA, 7.80000E+01, 1.50000E+01, 1.38000E+02, 6.00000E+00, 3.30000E+01, 3.00000E+01, 2.40000E+01, 1.00000E+01, 2.66000E+02, 1.50000E+01, 8.40000E+01, 9.10000E+01, NA, NA, NA, NA, 1.06000E+02, 2.50000E+01, 1.30000E+01, 1.00000E+01, 7.30000E+01, 1.50000E+01, 2.68000E+02, 5.10000E+01, 1.28000E+02, 1.16000E+02, 1.86000E+02, 7.80000E+01, 1.56000E+02, 6.00000E+00, 7.50000E+01, 3.60000E+01, 1.58000E+02, 3.60000E+01, 1.23000E+02, 2.40000E+01, 1.22000E+02, 3.00000E+01, 2.05000E+02, 9.30000E+01, 1.41000E+02, 6.20000E+01, 1.22000E+02, 3.80000E+01, 1.74000E+02, 2.30000E+01), .Dim=c(33, 2)))
list(lambda_tot0=1.04200E+02, logit_flow_Eu=c(-4.26200E-01, -7.07900E-01), lflow_fall_Eu=c(-5.00000E-01, -5.00000E-01), logit_int_Eu=c(5.08200E-01, 8.30400E-01), mupi_B=c(8.27000E-02, 1.03200E-01), pi_Eu00=c(5.87100E-01, 5.99200E-01), pi_Eu01=c(2.00600E-01, 7.99400E-01), rate_lambda=4.23800E-02, s=c(1.56700E+01, 3.91400E+00), shape_lambda=5.99500E+00, sigmapi_B=c(6.44000E-01, 6.96900E-01), sigmapi_Eu=c(1.01900E+00, 8.86500E-01), lambda_tot=c(1.02000E+02, 1.15500E+02, 2.20800E+02, 1.76200E+02, 1.30000E+02, NA, 1.24500E+02, 1.94300E+02, 1.57000E+02, NA, 9.30000E+01, 1.44000E+02, 6.23300E+01, 3.40000E+01, 2.80500E+02, 1.75000E+02, NA, NA, 1.30300E+02, 2.30000E+01, 8.72300E+01, 3.18200E+02, 2.43200E+02, 2.64000E+02, 1.62000E+02, 1.09400E+02, 1.93500E+02, 1.47000E+02, 1.51200E+02, 2.97000E+02, 2.02400E+02, 1.58900E+02, 1.96500E+02), logit_pi_Eu= structure(.Data= c(1.68200E+00, 3.92200E-02, NA, -7.12400E-01, NA, -1.57100E+00, NA, -9.26000E-01, 1.48500E+00, -1.48500E+00, NA, NA, 1.40600E+00, 1.20600E-01, NA, -1.41400E+00, NA, -2.17400E-01, NA, NA, 2.37700E-01, -2.11600E+00, -7.56300E-01, -4.26300E+00, 3.12700E-01, 5.34900E-02, NA, 3.56700E-01, NA, -2.56700E+00, -1.83900E+00, -1.74600E+00, NA, NA, NA, NA, 1.81000E+00, -1.38900E+00, NA, 1.89700E+00, NA, -1.34700E+00, 7.63400E+00, -1.45900E+00, 4.06300E-01, 1.71000E-01, -1.18100E+00, -2.21400E+00, 3.25800E+00, -3.25800E+00, NA, 4.95300E-01, NA, -1.10900E+00, NA, -7.23900E-01, NA, -7.66000E-01, 5.16200E-01, -9.30500E-01, 5.42600E-01, -9.61000E-01, NA, -3.41400E-01, NA, -1.71400E+00), .Dim=c(33, 2)), logit_pi_B= structure(.Data= c(-2.46400E+00, -4.61500E+00, -1.33800E+00, -4.74000E+00, -2.69300E+00, -4.69500E+00, -2.92200E+00, -3.76200E+00, -4.86000E+00, -4.86000E+00, NA, NA, -3.70100E+00, NA, -2.91400E+00, NA, -3.41400E+00, -4.35000E+00, NA, NA, -3.81800E+00, -4.52200E+00, -2.01100E+00, -4.96300E+00, -2.24000E+00, -2.43900E+00, -3.49700E+00, NA, -3.10800E+00, -4.52700E+00, -4.46000E+00, -3.52600E+00, NA, NA, NA, NA, -1.43800E+00, -2.60100E+00, NA, -3.09100E+00, -1.34700E+00, -3.33500E+00, -1.43900E+00, -4.13700E+00, -2.46800E+00, -3.25900E+00, -3.76100E+00, NA, -2.83300E+00, -5.08100E+00, -2.00400E+00, -3.27200E+00, -2.63100E+00, -3.63000E+00, -2.17500E+00, -4.98400E+00, -2.76000E+00, -5.01200E+00, -3.58700E+00, -4.58500E+00, -3.48800E+00, -3.67600E+00, -2.50500E+00, -3.65600E+00, -2.82500E+00, -5.27600E+00), .Dim=c(33, 2)), n= structure(.Data= c(6.40000E+01, 3.90000E+01, 9.30000E+01, 2.30000E+01, 1.91000E+02, 3.10000E+01, 1.41000E+02, 3.60000E+01, 1.06000E+02, 2.40000E+01, NA, NA, 7.50000E+01, 5.00000E+01, 1.68000E+02, 2.70000E+01, 1.23000E+02, 3.50000E+01, NA, NA, 7.80000E+01, 1.50000E+01, 1.38000E+02, 6.00000E+00, 3.30000E+01, 3.00000E+01, 2.40000E+01, 1.00000E+01, 2.66000E+02, 1.50000E+01, 8.40000E+01, 9.10000E+01, NA, NA, NA, NA, 1.06000E+02, 2.50000E+01, 1.30000E+01, 1.00000E+01, 7.30000E+01, 1.50000E+01, 2.68000E+02, 5.10000E+01, 1.28000E+02, 1.16000E+02, 1.86000E+02, 7.80000E+01, 1.56000E+02, 6.00000E+00, 7.50000E+01, 3.60000E+01, 1.58000E+02, 3.60000E+01, 1.23000E+02, 2.40000E+01, 1.22000E+02, 3.00000E+01, 2.05000E+02, 9.30000E+01, 1.41000E+02, 6.20000E+01, 1.22000E+02, 3.80000E+01, 1.74000E+02, 2.30000E+01), .Dim=c(33, 2)))
OpenBUGS version 3.2.3 rev 1012
model is syntactically correct
data loaded
model compiled
initial values loaded but chain contain uninitialized variables
initial values loaded but chain contain uninitialized variables
initial values loaded but chain contain uninitialized variables
error for node Cum_B[6,2] of type GraphBinomial.Node second argument invalid integer value given
model must be initialized before updating
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before monitors used
model must be initialized before updating
model must be initialized before monitors used
model must be initialized before monitors used
modelCheck('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs/model_adult-Bresle.R.txt')
modelData('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs/data.txt')
modelCompile(3)
modelSetRN(1)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs/inits1.txt',1)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs/inits2.txt',2)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs/inits3.txt',3)
modelGenInits()
modelUpdate(5000,4,5000)
samplesSet(pi_Eu00)
samplesSet(pi_Eu01)
samplesSet(logit_int_Eu)
samplesSet(logit_flow_Eu)
samplesSet(lflow_fall_Eu)
samplesSet(sigmapi_Eu)
samplesSet(epsilon_Eu)
samplesSet(mupi_B)
samplesSet(sigmapi_B)
samplesSet(pi_Eu)
samplesSet(p_Eu00_tot)
samplesSet(p_Eu01_tot)
samplesSet(pi_B)
samplesSet(test)
samplesSet(R2)
samplesSet(n_tot)
samplesSet(n_1SW)
samplesSet(n_MSW)
samplesSet(shape_lambda)
samplesSet(rate_lambda)
samplesSet(lambda_tot0)
samplesSet(Plambda0)
samplesSet(s)
samplesSet(lambda_tot)
samplesSet(Plambda)
summarySet(pi_Eu00)
summarySet(pi_Eu01)
summarySet(logit_int_Eu)
summarySet(logit_flow_Eu)
summarySet(lflow_fall_Eu)
summarySet(sigmapi_Eu)
summarySet(epsilon_Eu)
summarySet(mupi_B)
summarySet(sigmapi_B)
summarySet(pi_Eu)
summarySet(p_Eu00_tot)
summarySet(p_Eu01_tot)
summarySet(pi_B)
summarySet(test)
summarySet(R2)
summarySet(n_tot)
summarySet(n_1SW)
summarySet(n_MSW)
summarySet(shape_lambda)
summarySet(rate_lambda)
summarySet(lambda_tot0)
summarySet(Plambda0)
summarySet(s)
summarySet(lambda_tot)
summarySet(Plambda)
modelUpdate(25000,4,25000)
samplesCoda('*', '/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs//')
summaryStats('*')
modelQuit('y')
=============================
DIAGNOSTICS
=============================
Convergence: gelman-Rubin R test
Potential scale reduction factors:
Point est. Upper C.I.
shape_lambda 1 1
rate_lambda 1 1
lambda_tot0 1 1
Multivariate psrf
1
---------------------------
Heidelberger and Welch's convergence diagnostic
......@@ -9,18 +20,48 @@ heidel.diag is a run length control diagnostic based on a criterion of relative
heidel.diag also implements a convergence diagnostic, and removes up to half the chain in order to ensure that the means are estimated from a chain that has converged.
[[1]]
Stationarity start p-value
test iteration
shape_lambda passed 1 0.461
rate_lambda passed 1 0.340
lambda_tot0 passed 1 0.785
Halfwidth Mean Halfwidth
test
shape_lambda passed 3.6257 0.040264
rate_lambda passed 0.0238 0.000271
lambda_tot0 passed 169.5738 1.258302
[[2]]
Stationarity start p-value
test iteration
shape_lambda passed 1 0.7763
rate_lambda passed 1 0.4195
lambda_tot0 passed 1 0.0592
Halfwidth Mean Halfwidth
test
shape_lambda passed 3.6237 0.043851
rate_lambda passed 0.0239 0.000295
lambda_tot0 passed 167.8131 1.419483
[[3]]
Stationarity start p-value
test iteration
shape_lambda passed 1 0.7430
rate_lambda passed 1 0.3791
lambda_tot0 passed 5001 0.0613
shape_lambda passed 1 0.315
rate_lambda passed 1 0.255
lambda_tot0 passed 1 0.499
Halfwidth Mean Halfwidth
test
shape_lambda passed 3.6844 0.047101
rate_lambda passed 0.0243 0.000303
lambda_tot0 passed 168.3312 1.382674
shape_lambda passed 3.6205 0.042441
rate_lambda passed 0.0237 0.000291
lambda_tot0 passed 170.4717 1.286544
---------------------------
Geweke's convergence diagnostic
......@@ -31,16 +72,50 @@ The test statistic is a standard Z-score: the difference between the two sample
The Z-score is calculated under the assumption that the two parts of the chain are asymptotically independent, which requires that the sum of frac1 and frac2 be strictly less than 1.
[[1]]
Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
shape_lambda rate_lambda lambda_tot0
0.917 1.249 0.471
[[2]]
Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
shape_lambda rate_lambda lambda_tot0
0.2514 0.0589 -0.8762
[[3]]
Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
shape_lambda rate_lambda lambda_tot0
0.910 0.476 -1.454
-1.2517 -1.3461 -0.0797
---------------------------
Raftery and Lewis's diagnostic
[[1]]
Quantile (q) = 0.025
Accuracy (r) = +/- 0.005
Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
shape_lambda 24 36224 3746 9.67
rate_lambda 32 38736 3746 10.30
lambda_tot0 12 18172 3746 4.85
[[2]]
Quantile (q) = 0.025
Accuracy (r) = +/- 0.005
......@@ -48,7 +123,21 @@ Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
shape_lambda 24 34744 3746 9.27
rate_lambda 32 35472 3746 9.47
lambda_tot0 12 18352 3746 4.90
shape_lambda 32 38008 3746 10.10
rate_lambda 24 38056 3746 10.20
lambda_tot0 12 18392 3746 4.91
[[3]]
Quantile (q) = 0.025
Accuracy (r) = +/- 0.005
Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
shape_lambda 32 35848 3746 9.57
rate_lambda 32 34720 3746 9.27
lambda_tot0 16 18600 3746 4.97
This diff is collapsed.
......@@ -3,22 +3,22 @@
#SITE=Scorff # Nivelle Oir Bresle
YEAR=2016
CHAINS=3
CHAINS=2
BURNIN=5000 # Number of steps to "burn-in" the samplers.
ITER=25000 # Total number of steps in chains to save.
THIN=4 # Number of steps to "thin" (1=keep every step).
ITER=10000 # Total number of steps in chains to save.
THIN=5 # Number of steps to "thin" (1=keep every step).
REPbase="/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance"
#"/media/ORE/Abundance"
for SITE in Bresle #Nivelle Oir Scorff
for SITE in Oir #Scorff #Nivelle Bresle
do
cd $REPbase/$SITE
echo $SITE
for STADE in tacon smolt adult
for STADE in tacon #adult #smolt
do
if [ -d "$STADE" ]; then # if directory exists...
# Control will enter here if $DIRECTORY exists.
......
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