# Partial DATASET for 15 turtles

# For use with Stage-2 model in:
#	Eckert, S., J. Moore, D. Dunn, R. Sagarminaga, K. Eckert, P. Halpin
#	"Hierarchical state-space models of loggerhead sea turtle (Caretta caretta)
#	movement in relation to turtle size and oceanographic features
#	in the western Mediterranean Sea"
#	jemoore@duke.edu, 20 July 2007	

list(

K = 15,		# number of turtles

# Interval index for all turtles
# Each value is the first location for turtle k
# Last value indexes 1 + (last location of turtle K) 
Ridx = c(1, 312, 331, 486, 624, 812, 819, 838, 948, 1358, 1558, 1615, 1726, 1772, 1795, 1862),

# binary size variable
size = c(1,1,1,1,0,1,0,0,1,1,0,0,1,1,1),

# movement rate at each step, for all turtles (all for turt1, then for turt 2, ...)
kmDay = c(NA, 39.1906820696781, 7.47771663514017, 
17.8956461947560, 39.460795360223, 45.7562007427463, 48.74583278286, 
46.6154049288975, 43.4391698669792, 33.0465173928551, 28.1877171002338,
etc....),

deltRad = c(NA, -0.609822667232184, -3.14159265358979, 
2.88338396615865, -0.00484383914706266, -0.00258442951466176, 
etc...),

depth = c(1.82134432506860, 1.42233520842110, 
1.41285755719432, 1.77490383405737, 1.08398305962500, 0.549443530434526,
etc...),

msla = c(0.578323077709504, -0.385826580297655, 
-0.385826580297655, -0.229861194443556, -0.10225315147202, 0.677573777798476,
etc...)

# depth and msla are standardized (i.e., z-scores)

)


### INITIAL VALUES for stochastic nodes (parms to be estimated)

list(

# Initial values for hyper-parameters

rateMean.mu = c(10,NA),
rateMean.muDiff = 20,

rateMean.tau1 = 0.05
rateMean.tauDiff = 0.02,

rateTau.mu = c(NA,0.01),
rateTau.muDiff = 0.03,

rateTau.tau2 = 0.01,
rateTau.tauDiff = 0.01,

rho.mu = c(0.2, NA),
r = c(1,1),
rhoDiff.mu = 0.2,

B0.mu = c(0,0),
B0.sig = c(1,1),

bDepth.mu=c(0,0),
bDepth.sig=c(1,1),

bMsla.mu=c(0,0),
bMsla.sig=c(1,1),

bSize = c(0,0),
bSizeMsla = c(0,0),

# Initial values for turtle-specific parameters 
# Number of indiv-level parms estimated corresponds to number of individuals 
# In example below there were 9 individuals (does not match 15 in data above)

km.mu = structure(.Data=c(2,NA,2,NA,2,NA,2,NA,2,NA,2,NA,2,NA,2,NA,2,NA),.Dim=c(9,2)),

km.muDiff = c(10,10,10,10,10,10,10,10,10),

km.tau = structure(.Data=c(NA,0.5,NA,0.5,NA,0.5,NA,0.5,NA,0.5,NA,0.5,NA,0.5,NA,0.5,NA,0.5),.Dim=c(9,2)),

km.tauDiff = c(0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01),

rho=structure(.Data=c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5),.Dim=c(9,2)),

B0=structure(.Data=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),.Dim=c(9,2)),
bDepth=structure(.Data=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),.Dim=c(9,2)),
bMsla=structure(.Data=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),.Dim=c(9,2)),

)