compute theta EAP estimation under the graded response model

Usage

compute_eap_grm(
  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="GRM")

Examples

bank<-matrix(c(1.227,0.845,1.694,1.012,2.103,
               -2.183,-1.542,-1.021,-2.460,-0.812,
               -0.874,-0.415,-0.231,-1.105,0.094,
               0.316,0.783,0.652,0.047,0.881,
               1.592,2.064,1.438,1.271,1.736),
             nrow=5,
             dimnames=list(c(1,2,3,4,5),
                           c("alphaj","betaj1","betaj2","betaj3","betaj4")))
response<-c(4,3,2,1,0)
catR::eapEst(bank,response,model="GRM")
#> [1] -0.1877913
compute_eap_grm(bank,response)
#> [1] -0.1877913