createGroup: Create a user defined group-level object with correct generic...
In xzhaopsy/MIRT: Multidimensional Item Response Theory

Description Usage Arguments Author(s) References Examples

Initializes the proper S4 class and methods necessary for mirt functions to use in estimation for definiting 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.

1 2	createGroup(par, est, den, nfact, gr = NULL, hss = NULL, gen = NULL, lbound = NULL, ubound = NULL, derivType = "Richardson")

`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`
`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 idential 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'

Phil Chalmers rphilip.chalmers@gmail.com

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

## Not run: 

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

mod <- mirt(Science, 1, 'Rasch')
modcustom <- mirt(Science, 1, 'Rasch', customGroup=grp)

coef(mod)
coef(modcustom)


## End(Not run)