compute theta EAP estimation under the generalized graded response model

Usage

compute_eap_ggrm(
  bank,
  x,
  D = 1,
  priorPar = c(0, 1),
  lower = -4,
  upper = 4,
  nqp = 33
)

Arguments

bank: matrix of item parameters
x: vector of item responses scored 0 to m
D: metric constant can be 1 or 1.702
priorPar: mean and sd for normal distribution default is mean 0 and sd 1
lower: lower bound for numerical integration
upper: upper bound for numerical integration
nqp: number of quadrature points

Note

this function should return the same result as catR::eapEst(model="MGRM")

Examples

bank<-matrix(c(1.227,0.845,1.694,1.012,2.103,
               -0.482,0.137,0.715,-0.936,0.298,
               rep(1.812,5),rep(0.603,5),rep(-0.452,5),rep(-1.963,5)),
             nrow=5,
             dimnames=list(c(1,2,3,4,5),
                           c("alphaj","bj","c1","c2","c3","c4")))
response<-c(4,3,2,1,0)
catR::eapEst(bank,response,model="MGRM")
#> [1] -0.2280354
compute_eap_ggrm(bank,response)
#> [1] -0.2280354