ad.propSd_random: Adaptation For The Proposal Variance
In charlottedion/mixedsde: Estimation Methods for Stochastic Differential Mixed Effects Models
Description
Usage
Arguments
References
Description
Calculation of new proposal standard deviation for the random effects
Usage
1
2
ad.propSd_random(chain, propSd, iteration, lower = 0.3, upper = 0.6,
delta.n = function(n) min(0.1, 1/sqrt(n)))
Arguments
chain
matrix of Markov chain samples
propSd
old proposal standard deviation
iteration
number of current iteration
lower
lower bound
upper
upper bound
delta.n
function for adding/subtracting from the log propSd
References
Rosenthal, J. S. (2011). Optimal proposal distributions and adaptive MCMC. Handbook of Markov Chain Monte Carlo, 93-112.
charlottedion/mixedsde documentation built on May 13, 2019, 3:35 p.m.
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Description Usage Arguments References
Description
Calculation of new proposal standard deviation for the random effects
Usage
1 2 | ad.propSd_random(chain, propSd, iteration, lower = 0.3, upper = 0.6,
delta.n = function(n) min(0.1, 1/sqrt(n)))
|
Arguments
chain |
matrix of Markov chain samples |
propSd |
old proposal standard deviation |
iteration |
number of current iteration |
lower |
lower bound |
upper |
upper bound |
delta.n |
function for adding/subtracting from the log propSd |
References
Rosenthal, J. S. (2011). Optimal proposal distributions and adaptive MCMC. Handbook of Markov Chain Monte Carlo, 93-112.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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