model_grm: Graded Response Model
In xxIRT: Item Response Theory and Computer-Based Testing in R

Routine functions for the GRM

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)

`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

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))