model_grm function

Graded Response Model

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)

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))