[Yuima-commits] r740 - pkg/yuima/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Feb 5 05:53:19 CET 2021
Author: eguchi
Date: 2021-02-05 05:53:18 +0100 (Fri, 05 Feb 2021)
New Revision: 740
Modified:
pkg/yuima/man/adaBayes.Rd
Log:
modified
Modified: pkg/yuima/man/adaBayes.Rd
===================================================================
--- pkg/yuima/man/adaBayes.Rd 2021-02-05 04:52:54 UTC (rev 739)
+++ pkg/yuima/man/adaBayes.Rd 2021-02-05 04:53:18 UTC (rev 740)
@@ -1,84 +1,96 @@
-\name{adaBayes}
-\alias{adaBayes}
-\alias{adaBayes,yuima-method}
-\title{Adaptive Bayes estimator for the parameters in sde model}
-\description{Adaptive Bayes estimator for the parameters in a specific type of sde.}
-\usage{
-adaBayes(yuima, start, prior, lower, upper, method = "mcmc", mcmc = 1000,
-rate =1, rcpp = TRUE, algorithm = "randomwalk")
-}
-\arguments{
- \item{yuima}{a 'yuima' object.}
- \item{start}{initial suggestion for parameter values }
- \item{prior}{a list of prior distributions for the parameters specified by 'code'. Currently, dunif(z, min, max), dnorm(z, mean, sd), dbeta(z, shape1, shape2), dgamma(z, shape, rate) are available. }
- \item{lower}{a named list for specifying lower bounds of parameters}
- \item{upper}{a named list for specifying upper bounds of parameters}
- \item{method}{\code{nomcmc} requires package \code{cubature} }
- \item{mcmc}{number of iteration of Markov chain Monte Carlo method}
- \item{rate}{a thinning parameter. Only the first n^rate observation will be used for inference. }
- \item{rcpp}{Logical value. If \code{rcpp = TRUE} (default), Rcpp code will be performed. Otherwise, usual R code will be performed. }
- \item{algorithm}{Logical value when \code{method = mcmc}. If \code{algorithm = randomwalk} (default), the random-walk Metropolis algorithm will be performed. If \code{algorithm = MpCN}, the Mixed preconditioned Crank-Nicolson algorithm will be performed.}
-}
-\details{
-Calculate the Bayes estimator for stochastic processes by using the quasi-likelihood function. The calculation is performed by the Markov chain Monte Carlo method. Currently, the Random-walk Metropolis algorithm and the Mixed preconditioned Crank-Nicolson algorithm is implemented.}
-\value{
- \item{vector}{a vector of the paramter estimate}
-}
-\author{Kengo Kamatani with YUIMA project Team}
-\note{
-\code{algorithm = nomcmc} is unstable.
-}
-\references{
-Yoshida, N. (2011). Polynomial type large deviation inequalities and quasi-likelihood analysis for stochastic differential equations. Annals of the Institute of Statistical Mathematics, 63(3), 431-479.
-
-Uchida, M., & Yoshida, N. (2014). Adaptive Bayes type estimators of ergodic diffusion processes from discrete observations. Statistical Inference for Stochastic Processes, 17(2), 181-219.
-
-Kamatani, K. (2017). Ergodicity of Markov chain Monte Carlo with reversible proposal. Journal of Applied Probability, 54(2).
-}
-\examples{
-\dontrun{
-set.seed(123)
-
-b <- c("-theta1*x1+theta2*sin(x2)+50","-theta3*x2+theta4*cos(x1)+25")
-a <- matrix(c("4+theta5","1","1","2+theta6"),2,2)
-
-true = list(theta1 = 0.5, theta2 = 5,theta3 = 0.3,
- theta4 = 5, theta5 = 1, theta6 = 1)
-lower = list(theta1=0.1,theta2=0.1,theta3=0,
- theta4=0.1,theta5=0.1,theta6=0.1)
-upper = list(theta1=1,theta2=10,theta3=0.9,
- theta4=10,theta5=10,theta6=10)
-start = list(theta1=runif(1),
- theta2=rnorm(1),
- theta3=rbeta(1,1,1),
- theta4=rnorm(1),
- theta5=rgamma(1,1,1),
- theta6=rexp(1))
-
-yuimamodel <- setModel(drift=b,diffusion=a,state.variable=c("x1", "x2"),solve.variable=c("x1","x2"))
-yuimasamp <- setSampling(Terminal=50,n=50*10)
-yuima <- setYuima(model = yuimamodel, sampling = yuimasamp)
-yuima <- simulate(yuima, xinit = c(100,80),
- true.parameter = true,sampling = yuimasamp)
-
-prior <-
- list(
- theta1=list(measure.type="code",df="dunif(z,0,1)"),
- theta2=list(measure.type="code",df="dnorm(z,0,1)"),
- theta3=list(measure.type="code",df="dbeta(z,1,1)"),
- theta4=list(measure.type="code",df="dgamma(z,1,1)"),
- theta5=list(measure.type="code",df="dnorm(z,0,1)"),
- theta6=list(measure.type="code",df="dnorm(z,0,1)")
- )
-
-
-set.seed(123)
-mle <- qmle(yuima, start = start, lower = lower, upper = upper, method = "L-BFGS-B",rcpp=TRUE)
-print(mle at coef)
-bayes <- adaBayes(yuima, start=start, prior=prior,
- method="mcmc",
- mcmc=1000,rcpp=TRUE, lower = lower, upper = upper)
-print(bayes at coef)
-}
-}
-\keyword{ts}
+\name{adaBayes}
+\alias{adaBayes}
+\alias{adaBayes,yuima-method}
+\title{Adaptive Bayes estimator for the parameters in sde model}
+\description{
+ The \code{adabayes.mcmc} class is a class of the \pkg{yuima} package that extends the \code{mle-class}.}
+\usage{
+adaBayes(yuima, start, prior, lower, upper, method = "mcmc", iteration = NULL,mcmc,
+rate =1, rcpp = TRUE, algorithm = "randomwalk",center=NULL,sd=NULL,rho=NULL,
+path = FALSE)
+}
+\arguments{
+ \item{yuima}{a 'yuima' object.}
+ \item{start}{initial suggestion for parameter values }
+ \item{prior}{a list of prior distributions for the parameters specified by 'code'. Currently, dunif(z, min, max), dnorm(z, mean, sd), dbeta(z, shape1, shape2), dgamma(z, shape, rate) are available. }
+ \item{lower}{a named list for specifying lower bounds of parameters}
+ \item{upper}{a named list for specifying upper bounds of parameters}
+ \item{method}{\code{"nomcmc"} requires package \code{cubature} }
+ \item{iteration}{number of iteration of Markov chain Monte Carlo method}
+ \item{mcmc}{number of iteration of Markov chain Monte Carlo method}
+ \item{rate}{a thinning parameter. Only the first n^rate observation will be used for inference. }
+ \item{rcpp}{Logical value. If \code{rcpp = TRUE} (default), Rcpp code will be performed. Otherwise, usual R code will be performed. }
+ \item{algorithm}{If \code{algorithm = "randomwalk"} (default), the random-walk Metropolis algorithm will be performed. If \code{algorithm = "MpCN"}, the Mixed preconditioned Crank-Nicolson algorithm will be performed.}
+ \item{center}{A list of parameters used to center MpCN algorithm.}
+ \item{sd}{A list for specifying the standard deviation of proposal distributions.}
+ \item{path}{Logical value when \code{method = "mcmc"}. If \code{path=TRUE}, then the sample path for each variable will be included in the MCMC object in the output.}
+ \item{rho}{A parameter used for MpCN algorithm.}
+}
+\details{
+Calculate the Bayes estimator for stochastic processes by using the quasi-likelihood function. The calculation is performed by the Markov chain Monte Carlo method. Currently, the Random-walk Metropolis algorithm and the Mixed preconditioned Crank-Nicolson algorithm is implemented.}
+\section{Slots}{
+ \describe{
+ \item{\code{mcmc}:}{is a list of MCMC objects for all estimated parameters.}
+ \item{\code{accept_rate}:}{is a list acceptance rates for diffusion and drift parts.}
+ \item{\code{call}:}{is an object of class \code{language}.}
+ \item{\code{fullcoef}:}{is an object of class \code{list} that contains estimated parameters.}
+ \item{\code{vcov}:}{is an object of class \code{matrix}.}
+ \item{\code{coefficients}:}{is an object of class \code{vector} that contains estimated parameters.}
+ }
+}
+\author{Kengo Kamatani with YUIMA project Team}
+\note{
+\code{algorithm = nomcmc} is unstable.
+}
+\references{
+Yoshida, N. (2011). Polynomial type large deviation inequalities and quasi-likelihood analysis for stochastic differential equations. Annals of the Institute of Statistical Mathematics, 63(3), 431-479.
+Uchida, M., & Yoshida, N. (2014). Adaptive Bayes type estimators of ergodic diffusion processes from discrete observations. Statistical Inference for Stochastic Processes, 17(2), 181-219.
+Kamatani, K. (2017). Ergodicity of Markov chain Monte Carlo with reversible proposal. Journal of Applied Probability, 54(2).
+}
+\examples{
+\dontrun{
+set.seed(123)
+b <- c("-theta1*x1+theta2*sin(x2)+50","-theta3*x2+theta4*cos(x1)+25")
+a <- matrix(c("4+theta5","1","1","2+theta6"),2,2)
+true = list(theta1 = 0.5, theta2 = 5,theta3 = 0.3,
+ theta4 = 5, theta5 = 1, theta6 = 1)
+lower = list(theta1=0.1,theta2=0.1,theta3=0,
+ theta4=0.1,theta5=0.1,theta6=0.1)
+upper = list(theta1=1,theta2=10,theta3=0.9,
+ theta4=10,theta5=10,theta6=10)
+start = list(theta1=runif(1),
+ theta2=rnorm(1),
+ theta3=rbeta(1,1,1),
+ theta4=rnorm(1),
+ theta5=rgamma(1,1,1),
+ theta6=rexp(1))
+yuimamodel <- setModel(drift=b,diffusion=a,state.variable=c("x1", "x2"),solve.variable=c("x1","x2"))
+yuimasamp <- setSampling(Terminal=50,n=50*10)
+yuima <- setYuima(model = yuimamodel, sampling = yuimasamp)
+yuima <- simulate(yuima, xinit = c(100,80),
+ true.parameter = true,sampling = yuimasamp)
+prior <-
+ list(
+ theta1=list(measure.type="code",df="dunif(z,0,1)"),
+ theta2=list(measure.type="code",df="dnorm(z,0,1)"),
+ theta3=list(measure.type="code",df="dbeta(z,1,1)"),
+ theta4=list(measure.type="code",df="dgamma(z,1,1)"),
+ theta5=list(measure.type="code",df="dnorm(z,0,1)"),
+ theta6=list(measure.type="code",df="dnorm(z,0,1)")
+ )
+set.seed(123)
+mle <- qmle(yuima, start = start, lower = lower, upper = upper, method = "L-BFGS-B",rcpp=TRUE)
+print(mle at coef)
+center<-list(theta1=0.5,theta2=5,theta3=0.3,theta4=4,theta5=3,theta6=3)
+sd<-list(theta1=0.001,theta2=0.001,theta3=0.001,theta4=0.01,theta5=0.5,theta6=0.5)
+bayes <- adaBayes(yuima, start=start, prior=prior,lower=lower,upper=upper,
+ method="mcmc",mcmc=1000,rate = 1, rcpp = TRUE,
+ algorithm = "randomwalk",center = center,sd=sd,
+ path=TRUE)
+print(bayes at fullcoef)
+print(bayes at accept_rate)
+print(bayes at mcmc$theta1[1:10])
+}
+}
+\keyword{ts}
+
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