Slice3 | R Documentation |
MC Analysis TL (slice +gibbs) following gibbs3.R Hickey (2006)
Slice3(
Dose,
df.T,
df.y,
mu_0,
var_0,
mu_alpha,
var_alpha,
mu_beta,
var_beta,
y0 = 0,
a,
b,
n.iter
)
Dose |
numeric (required) set of the irradiation doses |
df.T |
matrix (required) Luminescence data: the temperatures |
df.y |
matrix (required) Luminescence data; the luminescence signal |
mu_0 |
numeric (required) Prior of the true dose's average |
var_0 |
numeric (required) Prior of the true dose's variance |
mu_alpha |
numeric (required) Prior of the intercept's average |
var_alpha |
numeric (required) Prior of the intercept's variance |
mu_beta |
numeric (required) Prior of the slope's average |
var_beta |
numeric (required) Prior of the slope's variance |
y0 |
numeric (with default) Prior of the luminescence for the true dose (Default y0=0, extrapolation) need ??? |
a |
numeric (required) the lower end points of the experimental calibration range |
b |
numeric (required) the upper end points of the experimental calibration range |
n.iter |
numeric (required) the number of iteration |
additional scalar variable T (Slice sampler)
an (r × 5)-matrix, #'
column | Type | Description |
De | numeric | 'True' Dose value |
sigma2 | numeric | variance |
alpha | numeric | intercept |
beta | numeric | slope |
T | numeric | Temperature |
Gibbs sampler: Hickey, G. L. 2006. « The Linear Calibration Problem: A Bayesian Analysis ». PhD Thesis, PhD dissertation, University of Durham. 1–148. http://www.dur.ac.uk/g.l.hickey/dissertation.pdf.
chapter 5 and Appendix G.6
Slice sampler: Neal, R. 2003. « Slice Sampling ». Annals of Statistics 31 (3): 705‑67.
##load data
if(dev.cur()!=1) dev.off()
data(multiTL, envir = environment())
mu_0= -105
var_0=50
mu_alpha=6.3e-3
var_alpha=9e-8
mu_beta=5e5
var_beta=2e-12
y0=0
a=6.8274
b=1.3819
attach(multiTL)
test<-Slice3(Dose,df.T,df.y,mu_0,var_0,mu_alpha,var_alpha, mu_beta, var_beta,y0,a,b,n.iter)
detach(multiTL)
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