postLambdai: Compute random draw from full conditional posterior for...
In skipTrack: A Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking
postLambdai R Documentation
Compute random draw from full conditional posterior for lambda_i in Li algorithm.
Description
This function calculates a random draw from the full conditional posterior distribution
for lambda_i in the Li algorithm, given the observed values y_ij, the indicators s_ij,
and the prior hyperparameters priorK and priorG.
Usage
postLambdai(yij, sij, priorK, priorG)
Arguments
yij
Vector of observed values for individual i.
sij
Vector of cycle skip indicators for individual i.
priorK
Prior hyperparameter kappa.
priorG
Prior hyperparameter gamma.
Value
A random draw from the posterior distribution of lambda_i.
skipTrack documentation built on April 3, 2025, 6:21 p.m.
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| postLambdai | R Documentation |
Compute random draw from full conditional posterior for lambda_i in Li algorithm.
Description
This function calculates a random draw from the full conditional posterior distribution for lambda_i in the Li algorithm, given the observed values y_ij, the indicators s_ij, and the prior hyperparameters priorK and priorG.
Usage
postLambdai(yij, sij, priorK, priorG)
Arguments
yij |
Vector of observed values for individual i. |
sij |
Vector of cycle skip indicators for individual i. |
priorK |
Prior hyperparameter kappa. |
priorG |
Prior hyperparameter gamma. |
Value
A random draw from the posterior distribution of lambda_i.
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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