Nothing
## ---- 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
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