MCMCpack: Markov chain Monte Carlo (MCMC) Package

> library(MCMCpack)

バージョン: 1.3.3

関数名 概略
BayesFactor Create an object of class BayesFactor from MCMCpack output
Dirichlet The Dirichlet Distribution
HMMpanelFE Markov Chain Monte Carlo for the Hidden Markov Fixed-effects Model
HMMpanelRE Markov Chain Monte Carlo for the Hidden Markov Random-effects Model
InvGamma The Inverse Gamma Distribution
InvWishart The Inverse Wishart Distribution
MCMCSVDreg Markov Chain Monte Carlo for SVD Regression
MCMCbinaryChange Markov Chain Monte Carlo for a Binary Multiple Changepoint Model
MCMCdynamicEI Markov Chain Monte Carlo for Quinn's Dynamic Ecological Inference Model
MCMCdynamicIRT1d Markov Chain Monte Carlo for Dynamic One Dimensional Item Response Theory Model
MCMCfactanal Markov Chain Monte Carlo for Normal Theory Factor Analysis Model
MCMChierEI Markov Chain Monte Carlo for Wakefield's Hierarchial Ecological Inference Model
MCMChlogit Markov Chain Monte Carlo for the Hierarchical Binomial Linear Regression Model using the logit link function
MCMChpoisson Markov Chain Monte Carlo for the Hierarchical Poisson Linear Regression Model using the log link function
MCMChregress Markov Chain Monte Carlo for the Hierarchical Gaussian Linear Regression Model
MCMCintervention Markov Chain Monte Carlo for a linear Gaussian Multiple Changepoint Model
MCMCirt1d Markov Chain Monte Carlo for One Dimensional Dimensional Item Response Theory Model, Covariates Predicting Latent Ideal Point (Ability)
MCMCirtHier1d Markov Chain Monte Carlo for Hierarchical One Dimensional Item Response Theory Model, Covariates Predicting Latent Ideal Point (Ability)
MCMCirtKd Markov Chain Monte Carlo for K-Dimensional Item Response Theory Model
MCMCirtKdHet Markov Chain Monte Carlo for Heteroskedastic K-Dimensional Item Response Theory Model
MCMCirtKdRob Markov Chain Monte Carlo for Robust K-Dimensional Item Response Theory Model
MCMClogit Markov Chain Monte Carlo for Logistic Regression
MCMCmetrop1R Metropolis Sampling from User-Written R function
MCMCmixfactanal Markov Chain Monte Carlo for Mixed Data Factor Analysis Model
MCMCmnl Markov Chain Monte Carlo for Multinomial Logistic Regression
MCMCoprobit Markov Chain Monte Carlo for Ordered Probit Regression
MCMCoprobitChange Markov Chain Monte Carlo for Ordered Probit Changepoint Regression Model
MCMCordfactanal Markov Chain Monte Carlo for Ordinal Data Factor Analysis Model
MCMCpoisson Markov Chain Monte Carlo for Poisson Regression
MCMCpoissonChange Markov Chain Monte Carlo for a Poisson Regression Changepoint Model
MCMCprobit Markov Chain Monte Carlo for Probit Regression
MCMCprobitChange Markov Chain Monte Carlo for a linear Gaussian Multiple Changepoint Model
MCMCquantreg Bayesian quantile regression using Gibbs sampling
MCMCregress Markov Chain Monte Carlo for Gaussian Linear Regression
MCMCregressChange Markov Chain Monte Carlo for a linear Gaussian Multiple Changepoint Model
MCMCresidualBreakAnalysis Break Analysis of Univariate Time Series using Markov Chain Monte Carlo
MCMCtobit Markov Chain Monte Carlo for Gaussian Linear Regression with a Censored Dependent Variable
MCbinomialbeta Monte Carlo Simulation from a Binomial Likelihood with a Beta Prior
MCmultinomdirichlet Monte Carlo Simulation from a Multinomial (with known variance) with a Normal Prior
MCnormalnormal Monte Carlo Simulation from a Normal Likelihood (with known variance) with a Normal Prior
MCpoissongamma Monte Carlo Simulation from a Poisson Likelihood with a Gamma Prior Nethvote
mptable Calculate the marginal posterior probabilities of predictors being included in a quantile regression model.
Nethvote Dutch Voting Behavior in 1989
NoncenHypergeom The Noncentral Hypergeometric Distribution
PErisk Political Economic Risk Data from 62 Countries in 1987
PostProbMod Calculate Posterior Probability of Model
Rehnquist U.S. Supreme Court Vote Matrix, Rehnquist Court (1994-2004)
SSVSquantreg Stochastic search variable selection for quantile regression
Senate 106th U.S. Senate Roll Call Vote Matrix
SupremeCourt U.S. Supreme Court Vote Matrix
Wishart The Wishart Distribution
choicevar Handle Choice-Specific Covariates in Multinomial Choice Models
dtomogplot Dynamic Tomography Plot
make.breaklist Vector of break numbers
mptable Calculate the marginal posterior probabilities of predictors being included in a quantile regression model.
plot.qrssvs Plot output from quantile regression stochastic search variable selection (QR-SSVS).
plotChangepoint Posterior Density of Regime Change Plot
plotIntervention Plot of intervention analysis
plotState Changepoint State Plot
procrustes Procrustes Transformation
read.Scythe Read a Matrix from a File written by Scythe
summary.qrssvs Summarising the results of quantile regression stochastic search variable selection (QR-SSVS).
testpanelGroupBreak A Test for the Group-level Break using a Multivariate Linear Regression Model with Breaks
testpanelSubjectBreak A Test for the Subject-level Break using a Unitivariate Linear Regression Model with Breaks
tomogplot Tomography Plot
topmodels Shows an ordered list of the most frequently visited models sampled during quantile regression stochastic search variable selection (QR-SSVS).`
vech Extract Lower Triangular Elements from a Symmetric Matrix
write.Scythe Write a Matrix to a File to be Read by Scythe
xpnd Expand a Vector into a Symmetric Matrix

MCMCregress

Arguments

  • formula
  • data
  • burnin
  • mcmc
  • thin
  • verbose
  • seed
  • beta.start
  • b0
  • B0
  • c0
  • d0
  • sigma.mu
  • sigma.var
  • marginal.likelihood
  • ...
> line   <- list(X = c(-2, -1, 0, 1, 2), 
+                Y = c(1, 3, 3, 3, 5))
> line %>% lm(Y ~ X, data = .) %>% broom::tidy()
> line %>% MCMCregress(formula   = Y ~ X, 
+                           b0        = 0, 
+                           B0        = 0.1, 
+                           sigma.mu  = 5, 
+                           sigma.var = 25, 
+                           data      = ., 
+                           verbose   = 1000) -> posterior
> summary(posterior)
> plot(posterior)