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"
# )