## ----eval=F-------------------------------------------------------------------
# fitMPT <- traitMPT(
#   eqnfile = "2htm.txt",
#   data = "data_ind.csv",
#   restrictions = list("Dn=Do", "g=.5"),
#   covData = "data_covariates.csv",
#   corProbit = TRUE,
#   predStructure = list("Do ; IQ"), # IQ as predictor for Do=Dn
#   ...
# )

## ----eval = FALSE-------------------------------------------------------------
# fitMPT <- traitMPT(
#   eqnfile = "2htm.txt",
#   data = "data_ind.csv",
#   covData = "data_covariates.csv",
#   predStructure = list(
#     "Do ; factor1",
#     "Dn ; factor2"
#   ), # discrete factors
#   predType = c("c", "c", "f", "r")
# )

## ----eval=F-------------------------------------------------------------------
# getGroupMeans(fitMPT)

## ----eval=FALSE---------------------------------------------------------------
# transformedParameters <- list(
#   "deltaG = G_1-G_2", # difference of parameters
#   "G1_larger = G_1>G_2"
# ) # Bayesian p-value / testing order constraints

## ----eval=FALSE---------------------------------------------------------------
# # beta-MPT
# genBeta <- genBetaMPT(
#   N = 100, # number of participants
#   numItems = c(Target = 250, Lure = 250), # number of responses per tree
#   eqnfile = "2htm.eqn", # path to MPT file
#   mean = c(Do = .7, Dn = .7, g = .5), # true group-level parameters
#   sd = c(Do = .1, Dn = .1, g = .05)
# ) # SD of individual parameters
# 
# # latent-trait MPT
# genTrait <- genTraitMPT(
#   N = 100, # number of participants
#   numItems = c(Target = 250, Lure = 250), # number of responses per tree
#   eqnfile = "2htm.eqn", # path to MPT file
#   mean = c(Do = .7, Dn = .7, g = .5), # true group-level parameters
#   sigma = c(Do = .25, Dn = .25, g = .05), # SD of latent (!) individual parameters
#   rho = diag(3)
# ) # correlation matrix. here: no correlation