xxIRT (version 2.1.2)

model_gpcm: Generalized Partial Credit Model

Description

Routine functions for the GPCM

Usage

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)

Arguments

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

Details

Use NA to represent unused category.

Examples

# NOT RUN {
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))
# }