model_grm {xxIRT}

Graded Response Model

Description

Routine functions for the GRM

Usage

model_grm_prob(t, a, b, D = 1.702, raw = FALSE)

model_grm_info(t, a, b, D = 1.702)

model_grm_lh(u, t, a, b, D = 1.702, log = FALSE)

model_grm_gendata(n_p, n_i, n_c, t = NULL, a = NULL, b = NULL,
  D = 1.702, t_dist = c(0, 1), a_dist = c(-0.1, 0.2), b_dist = c(0,
  0.8), missing = NULL)

model_grm_rescale(t, a, b, param = c("t", "b"), mean = 0, sd = 1)

model_grm_plot(a, b, D = 1.702, type = c("prob", "info"),
  by_item = FALSE, total = FALSE, xaxis = seq(-6, 6, 0.1),
  raw = FALSE)

model_grm_plot_loglh(u, a, b, D = 1.702, xaxis = seq(-6, 6, 0.1),
  show_mle = FALSE)

Arguments

t

ability parameters, 1d vector

a

discrimination parameters, 1d vector

b

item location parameters, 2d matrix

D

the scaling constant, 1.702 by default

raw

TRUE to return P*

u

the observed scores (starting from 0), 2d matrix

log

TRUE to return log-likelihood

n_p

the number of people to be generated

n_i

the number of items to be generated

n_c

the number of score categories

t_dist

parameters of the normal distribution used to generate t-parameters

a_dist

parameters of the lognormal distribution used to generate a-parameters

b_dist

parameters of the normal distribution used to generate b-parameters

missing

the proportion or number of missing responses

param

the parameter of the new scale: 't' or 'b'

mean

the mean of the new scale

sd

the standard deviation of the new scale

type

the type of plot, prob for ICC and info for IIFC

by_item

TRUE to combine categories

total

TRUE to sum values over items

xaxis

the values of x-axis

show_mle

TRUE to print maximum likelihood values

Examples

with(model_grm_gendata(10, 5, 3), model_grm_prob(t, a, b))
with(model_grm_gendata(10, 5, 3), model_grm_info(t, a, b))
with(model_grm_gendata(10, 5, 3), model_grm_lh(u, t, a, b))
model_grm_gendata(10, 5, 3)
model_grm_gendata(10, 5, 3, missing=.1)
with(model_grm_gendata(10, 5, 3), model_grm_plot(a, b, type='prob'))
with(model_grm_gendata(10, 5, 3), model_grm_plot(a, b, type='info', by_item=TRUE))
with(model_grm_gendata(5, 50, 3), model_grm_plot_loglh(u, a, b))