Description Usage Arguments Details Value Examples
Estimate a smoothed BETEL model by generating a sample
from the parameter posterior density with a RWMH algorithm. Subchains
are used for integrating over the stochastic prior defined by the prior
data generating process prior_fun()
.
1 2 3 4 5 6 7 8 9 10 11 12 |
model |
A list returned by |
chain_length |
The length of one subchain. |
chain_number |
The number of subchains. The total (not independent)
size of the sample equals |
tune |
A parameter that controls the step size of the RWMH algorithm.
Should be chosen to accomplish an acceptance rate of approximately 20-25
|
type |
Should be either "posterior", "likelihood" or "prior". Defaults to "posterior". |
burn |
The length of the burn-in period. Should be enough for correlation of the subchain from its initial value to vanish. |
parallel |
Specifies the number of logical processors used for parallel
processing of the subchains. Defaults to 1 in which case no parallel processing
is used. |
itermax |
Maximum number of Newton-Rhapson iterations within the evaluation of the likelihood. Defaults to 20 which should be more than enough. |
backup |
|
verbose |
Logical. Defaults to |
TBA
est_sbetel()
returns a list containing the output of the
RWMH algorithm.
1 2 3 4 | ## Not run:
output <- est_sbetel(model, tune = 0.1)
## End(Not run)
|
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