inst/doc/TreeBUGS_1_intro.R

In TreeBUGS: Hierarchical Multinomial Processing Tree Modeling

## ---- eval=F------------------------------------------------------------------
#  readEQN(
#    file = "pathToFile.eqn", # relative or absolute path
#    restrictions = list("Dn=Do"), # equality constraints
#    paramOrder = TRUE
#  ) # show parameter order

## ---- eval=FALSE--------------------------------------------------------------
#  restrictions <- list("Dn=Do", "g=0.5")

## ---- eval=FALSE--------------------------------------------------------------
#  # load the package:
#  library(TreeBUGS)
#  
#  # fit the model:
#  fitHierarchicalMPT <- betaMPT(
#    eqnfile = "2htm.txt", # .eqn file
#    data = "data_ind.csv", # individual data
#    restrictions = list("Dn=Do"), # parameter restrictions (or path to file)
#  
#    ### optional MCMC input:
#    n.iter = 20000, # number of iterations
#    n.burnin = 5000, # number of burnin samples that are removed
#    n.thin = 5, # thinning rate of removing samples
#    n.chains = 3 # number of MCMC chains (run in parallel)
#  )

## ---- eval=FALSE--------------------------------------------------------------
#  # Default: Traceplot and density
#  plot(fitHierarchicalMPT, # fitted model
#    parameter = "mean" # which parameter to plot
#  )
#  # further arguments are passed to ?plot.mcmc.list
#  
#  # Auto-correlation plots:
#  plot(fitHierarchicalMPT, parameter = "mean", type = "acf")
#  
#  # Gelman-Rubin plots:
#  plot(fitHierarchicalMPT, parameter = "mean", type = "gelman")

## ---- eval=FALSE--------------------------------------------------------------
#  summary(fitHierarchicalMPT)

## ---- eval=FALSE--------------------------------------------------------------
#  plotParam(fitHierarchicalMPT, # estimated parameters
#    includeIndividual = TRUE # whether to plot individual estimates
#  )
#  plotDistribution(fitHierarchicalMPT) # estimated hierarchical parameter distribution
#  plotFit(fitHierarchicalMPT) # observed vs. predicted mean frequencies
#  plotFit(fitHierarchicalMPT, stat = "cov") # observed vs. predicted covariance
#  plotFreq(fitHierarchicalMPT) # individual and mean raw frequencies per tree
#  plotPriorPost(fitHierarchicalMPT) # comparison of prior/posterior (group level parameters)

## ---- eval=FALSE--------------------------------------------------------------
#  # matrix for further use within R:
#  tt <- getParam(fitHierarchicalMPT,
#    parameter = "theta",
#    stat = "mean"
#  )
#  tt
#  
#  # save complete summary of individual estimates to file:
#  getParam(fitHierarchicalMPT,
#    parameter = "theta",
#    stat = "summary", file = "parameter.csv"
#  )