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Last data update: 2014.03.03

R: Generate a mirt object from population parameters

generate.mirt_object

R Documentation

Generate a mirt object from population parameters

Description

This function generate a mirt object from known population parameters, which is then passed to mirtCAT function for running CAT applications.

Usage

generate.mirt_object(parameters, itemtype, latent_means = NULL,
  latent_covariance = NULL, key = NULL, min_category = rep(0L,
  length(itemtype)))

Arguments

`parameters`	a matrix or data.frame of parameters corresponding to the model definitions listed in `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 `NA`'s as placeholders
`itemtype`	a character vector indiciating the type of item to which the parameters refer. See the `itemtype` arguement in `mirt`. Note that this input is only used to determine the relavent item class for the rows in `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
`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
`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)
`key`	scoring key required for nested-logit models. See `mirt` for details
`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 `K - 1`

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

Examples

## Not run: 

### 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)


## End(Not run)