model_gpcm: Generalized Partial Credit Model
In xluo11/xxIRT: Item Response Theory and Computer-Based Testing

Description Usage Arguments Details Examples

Routine functions for the GPCM

model_gpcm_prob(t, a, b, d, D = 1.702, insert_d0 = NULL)

model_gpcm_info(t, a, b, d, D = 1.702, insert_d0 = NULL)

model_gpcm_lh(u, t, a, b, d, D = 1.702, insert_d0 = NULL,
  log = FALSE)

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

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

model_gpcm_plot(a, b, d, D = 1.702, insert_d0 = NULL,
  type = c("prob", "info"), by_item = FALSE, total = FALSE,
  xaxis = seq(-6, 6, 0.1))

model_gpcm_plot_loglh(u, a, b, d, D = 1.702, insert_d0 = NULL,
  xaxis = seq(-6, 6, 0.1), show_mle = FALSE)

`t`	ability parameters, 1d vector
`a`	discrimination parameters, 1d vector
`b`	item location parameters, 1d vector
`d`	item category parameters, 2d vector
`D`	the scaling constant, 1.702 by default
`insert_d0`	insert an initial category value
`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
`sort_d`	`TRUE` to sort d parameters for each item
`t_dist`	parameters of the normal distribution used to generate t-parameters
`a_dist`	parameters of the lognormal distribution parameters of 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

Use NA to represent unused category.

with(model_gpcm_gendata(10, 5, 3), model_gpcm_prob(t, a, b, d))
with(model_gpcm_gendata(10, 5, 3), model_gpcm_info(t, a, b, d))
with(model_gpcm_gendata(10, 5, 3), model_gpcm_lh(u, t, a, b, d))
model_gpcm_gendata(10, 5, 3)
model_gpcm_gendata(10, 5, 3, missing=.1)
# Figure 1 in Muraki, 1992 (APM)
b <- matrix(c(-2,0,2,-.5,0,2,-.5,0,2), nrow=3, byrow=TRUE)
model_gpcm_plot(a=c(1,1,.7), b=rowMeans(b), d=rowMeans(b)-b, D=1.0, insert_d0=0)
# Figure 2 in Muraki, 1992 (APM)
b <- matrix(c(.5,0,NA,0,0,0), nrow=2, byrow=TRUE)
model_gpcm_plot(a=.7, b=rowMeans(b, na.rm=TRUE), d=rowMeans(b, na.rm=TRUE)-b, D=1.0, insert_d0=0)
# Figure 3 in Muraki, 1992 (APM)
b <- matrix(c(1.759,-1.643,3.970,-2.764), nrow=2, byrow=TRUE)
model_gpcm_plot(a=c(.778,.946), b=rowMeans(b), d=rowMeans(b)-b, D=1.0, insert_d0=0)
# Figure 1 in Muraki, 1993 (APM)
b <- matrix(c(0,-2,4,0,-2,2,0,-2,0,0,-2,-2,0,-2,-4), nrow=5, byrow=TRUE)
model_gpcm_plot(a=1, b=rowMeans(b), d=rowMeans(b)-b, D=1.0)
# Figure 2 in Muraki, 1993 (APM)
b <- matrix(c(0,-2,4,0,-2,2,0,-2,0,0,-2,-2,0,-2,-4), nrow=5, byrow=TRUE)
model_gpcm_plot(a=1, b=rowMeans(b), d=rowMeans(b)-b, D=1.0, type='info', by_item=TRUE)
with(model_gpcm_gendata(5, 50, 3), model_gpcm_plot_loglh(u, a, b, d))