DPelicit的学习.docx

DPelicit {DPpackage}R DocumentationPerforms a prior elicitation for the precision parameter of a DP priorDescriptionThis function performs a prior elicitation for the precision parameter of a DP prior. The function calculates:1) the expected value and the standard deviation of the number of clusters, given the values of the parameters of the?gamma?prior for the precision parameter,?a0?and?b0, or2) the value of the parameters?a0?and?b0?of the?gamma?prior distribution for the precision parameter,?alpha, given the prior expected number and the standard deviation of the number of clusters.UsageDPelicit(n,method=JGL,a0=NULL,b0=NULL,mean=NULL,std=NULL)Argumentsnnumber of observations which distribution follows a DP prior.methodthe method to be used. See?details.a0hyperparameter for the?Gamma?prior distribution of the precision parameter of the Dirichlet process prior,alpha ~ Gamma(a0,b0).b0hyperparameter for the?Gamma?prior distribution of the precision parameter of the Dirichlet process prior,alpha ~ Gamma(a0,b0).meanprior expected number of clusters when?alpha ~ Gamma(a0,b0).stdprior standard deviation for the number of clusters when?alpha ~ Gamma(a0,b0).DetailsThe methods supported by these functions are based on the fact that a priori?E(alpha) = a0/b0?and?Var(alpha) = a0/b0^2, and an additional approximation based on Taylor series expansion.The default method,?JGL, is based on the exact value of the mean and the variance of the number of clusters given the precision parameter alpha (see, Jara, Garcia-Zatera and Lesaffre, 2007).The Method?KMQ?is base on the Liu (1996) approximation to the expected value and the variance of the number of clusters given the precision parameter alpha (see, Kottas, Muller and Quintana, 2005).Given the prior judgement for the mean and variance of the number of clusters, the equations are numerically solve for?a0?and?b0. With this objective, the Newton-Raphson algorithm and the forward-difference approximation to Jacobian are used.Author(s)A