mirt: createGroup – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

createGroup

Create a user defined group-level object with correct generic functions

Description

Initializes the proper S4 class and methods necessary for mirt functions to use in estimation for defining customized group-level functions. To use the defined objects pass to the mirt(..., customGroup = OBJECT) command, and ensure that the class parameters are properly labeled.

Usage

createGroup(
  par,
  est,
  den,
  nfact,
  standardize = FALSE,
  gr = NULL,
  hss = NULL,
  gen = NULL,
  lbound = NULL,
  ubound = NULL,
  derivType = "Richardson"
)

Arguments

`par`	a named vector of the starting values for the parameters
`est`	a logical vector indicating which parameters should be freely estimated by default
`den`	the probability density function given the Theta/ability values. First input contains a vector of all the defined parameters and the second input must be a matrix called `Theta`. Function also must return a `numeric` vector object corresponding to the associated densities for each row in the `Theta` input
`nfact`	number of factors required for the model. E.g., for unidimensional models with only one dimension of integration `nfact = 1`
`standardize`	logical; use standardization of the quadrature table method proposed by Woods and Thissen (2006)? If TRUE, the logical elements named `'MEAN_1'` and `'COV_11'` can be included in the parameter vector, and when these values are set to FALSE in the `est` input the E-table will be standardized to these fixed values (e.g., `par <- c(a1=1, d=0, MEAN_1=0, COV_11=1)` with `est <- c(TRUE, TRUE, FALSE, FALSE)` will standardize the E-table to have a 0 mean and unit variance)
`gr`	gradient function (vector of first derivatives) of the log-likelihood used in estimation. The function must be of the form `gr(x, Theta)`, where `x` is the object defined by `createGroup()` and `Theta` is a matrix of latent trait parameters
`hss`	Hessian function (matrix of second derivatives) of the log-likelihood used in estimation. If not specified a numeric approximation will be used. The input is identical to the `gr` argument
`gen`	a function used when `GenRandomPars = TRUE` is passed to the estimation function to generate random starting values. Function must be of the form `function(object) ...` and must return a vector with properties equivalent to the `par` object. If NULL, parameters will remain at the defined starting values by default
`lbound`	optional vector indicating the lower bounds of the parameters. If not specified then the bounds will be set to -Inf
`ubound`	optional vector indicating the lower bounds of the parameters. If not specified then the bounds will be set to Inf
`derivType`	if the `gr` or `hss` terms are not specified this type will be used to obtain them numerically. Default is 'Richardson'

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi: 10.18637/jss.v048.i06

Examples

# normal density example, N(mu, sigma^2)
den <- function(obj, Theta) dnorm(Theta, obj@par[1], sqrt(obj@par[2]))
par <- c(mu = 0, sigma2 = .5)
est <- c(FALSE, TRUE)
lbound <- c(-Inf, 0)
grp <- createGroup(par, est, den, nfact = 1, lbound=lbound)

dat <- expand.table(LSAT6)
mod <- mirt(dat, 1, 'Rasch')
modcustom <- mirt(dat, 1, 'Rasch', customGroup=grp)

coef(mod)
coef(modcustom)

mirt

Multidimensional Item Response Theory

v1.33.2

GPL (>= 3)

Authors

Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>), Joshua Pritikin [ctb], Alexander Robitzsch [ctb], Mateusz Zoltak [ctb], KwonHyun Kim [ctb], Carl F. Falk [ctb], Adam Meade [ctb], Lennart Schneider [ctb], David King [ctb], Chen-Wei Liu [ctb], Ogreden Oguzhan [ctb]

Initial release