% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generate.mirt_object.R
\name{generate.mirt_object}
\alias{generate.mirt_object}
\title{Generate a mirt object from population parameters}
\usage{
generate.mirt_object(parameters, itemtype, latent_means = NULL,
  latent_covariance = NULL, key = NULL, min_category = rep(0L,
  length(itemtype)))
}
\arguments{
\item{parameters}{a matrix or data.frame of parameters corresponding to the model definitions
listed in \code{\link{mirt}}. Each row represents a unqiue item, while the 
column names correspond to the respective parameter names. If a parameter is not relavent
for a particular item/row then use \code{NA}'s as placeholders}

\item{itemtype}{a character vector indiciating the type of item to which the parameters 
refer. See the \code{itemtype} arguement in \code{\link{mirt}}. Note that this input 
is only used to determine the relavent item class for the rows in \code{parameters}, 
therefore many inputs are interchangable (e.g., '2PL' generates the same model as '3PL').
If only a single value is provided then all items types will be assumed identical}

\item{latent_means}{(optional) a numeric vector used to define the population latent mean
structure. By default the mean structure is centered at a 0 centroid}

\item{latent_covariance}{(optional) a covariance matrix used to define the population 
variance-covariance structure between the latent traits. By default the relationship is 
assumed to be standard normal (i.e., and identity matrix)}

\item{key}{scoring key required for nested-logit models. See \code{\link{mirt}} for details}

\item{min_category}{the value representing the lowest category index. By default this is 0,
therefore the respond suitable for the first category is 0, second is 1, and so on up to 
\code{K - 1}}
}
\description{
This function generate a \code{mirt} object from known population parameters, which is  
then passed to \code{\link{mirtCAT}} function for running CAT applications.
}
\examples{
\dontrun{

### build a unidimensional test with all 3PL items

nitems <- 50
a1 <- rlnorm(nitems, .2,.2)
d <- rnorm(nitems)
g <- rbeta(nitems, 20, 80)

pars <- data.frame(a1=a1, d=d, g=g)
head(pars)

obj <- generate.mirt_object(pars, '3PL')
coef(obj, simplify = TRUE)
plot(obj, type = 'trace')

### build a two-dimensional test  
## all graded items with 5 response categories

nitems <- 30
as <- matrix(rlnorm(nitems*2, .2, .2), nitems)
diffs <- t(apply(matrix(runif(nitems*4, .3, 1), nitems), 1, cumsum)) 
diffs <- -(diffs - rowMeans(diffs)) 
ds <- diffs + rnorm(nitems)
pars2 <- data.frame(as, ds)
colnames(pars2) <- c('a1', 'a2', paste0('d', 1:4))
head(pars2)

obj <- generate.mirt_object(pars2, 'graded')
coef(obj, simplify = TRUE)

### unidimensional mixed-item test

library(plyr)
pars3 <- rbind.fill(pars, pars2) #notice the NA's where parmeters do not exist
obj <- generate.mirt_object(pars3, itemtype = c(rep('2PL', 50), rep('graded', 30)))
coef(obj)
itemplot(obj, 51)
itemplot(obj, 1, drop.zeros=TRUE)

}
}
\author{
Phil Chalmers \email{rphilip.chalmers@gmail.com}
}
\seealso{
\code{\link{mirt}}, \code{\link{mirtCAT}}, \code{\link{generate_pattern}}
}