Description Usage Arguments Details Value See Also Examples
A function to set the hyperparameters of a first order autoregressive BPS prior distribution, approximately assigning constant prior mean hazard rate and corresponding coefficient of variation.
1  BPSpriorElicit(r0 = 1, H = 1, T00 = 1, ord = 4, G = 30, c = 0.9)

r0 
prior mean hazard rate (r_0) 
H 
corresponding coefficient of variation 
T00 
timehorizon of interest (T_∞) 
ord 
spline order (k) 
G 
number of internal spline knots 
c 
correlation coefficient between two consecutive spline weights 
A first order autoregressive BPS prior hazard rate is defined, for 0<t<T_∞, by
ρ(t)=\exp\{∑_{j=1}^{G+k2} η_j B_j(t)\}
where:
η_j is the jth element of a normally distributed vector of spline weights (see below for details)
B_j(t) is the jth Bspline basis function of order k, evaluated at t,
defined on a grid of G+2k2 equispaced knots with first internal knot at 0
and last internal knot at T_∞ (see splineDesign
for details)
The spline weights form a stationary AR(1) process with mean m, variance w and lagone autocorrelation c. The elicitation procedure takes w = H^2 and m = \log r_0  0.5 * w, based on the mean and variance formulas for the lognormal distribution. As Bspline basis functions form a partition of unity within internal nodes, the mean of ρ(t) is approximately equal to r0, for 0<t<T_∞, and its standard deviation to Hr_0.
A list with nine components:
r0 
prior mean hazard rate (copy of the input argument) 
H 
corresponding coefficient of variation (copy of the input argument) 
T00 
timehorizon of interest (copy of the input argument) 
ord 
spline order (copy of the input argument) 
G 
number of internal spline knots (copy of the input argument) 
c 
correlation coefficient between two consecutive spline weights (copy of the input argument) 
knots 
full grid of spline knots 
m 
mean of spline coefficients 
w 
variance of spline coefficients 
BayHazpackage
, BPSpriorSample
, BPSpostSample
1 2 3  # ten events per century with unit coefficient of variation and fifty year time horizon
# cubic splines with minimal number of knots and strongly correlated spline weights
hypars<BPSpriorElicit(r0 = 0.1, H = 1, T00 = 50, ord = 4, G = 3, c = 0.9)

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