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Creates a data frame of item parameters.
item_gen(
b_bounds,
a_bounds = NULL,
c_bounds = NULL,
thresholds = 1,
n_1pl = NULL,
n_2pl = NULL,
n_3pl = NULL
)
a vector containing the bounds of the the uniform distribution for sampling the difficulty parameters.
a vector containing the bounds of the the uniform distribution for sampling the discrimination parameters.
a vector containing the bounds of the the uniform distribution for sampling the guessing parameters.
if numeric, number of thresholds for 1- and/or 2- parameter dichotomous items, if vector, each element is the number of thresholds corresponding to the vector of n_1pl and/or n_2pl.
if integer, number of 1-parameter dichotomous items, if vector, each element is the number of partial credit items corresponding to thresholds number.
if integer, number of 2-parameter dichotomous items, if vector, each element is the number of generalized partial credit items corresponding to thresholds number.
integer, number of 3-parameter items.
The data frame includes two variables p
and k
which indicate the
number of parameters and the number of thresholds, respectively
item_gen(b_bounds = c(-2, 2), a_bounds = c(.75, 1.25),
thresholds = c(1, 2, 3), n_1pl = c(5, 5, 5), n_2pl = c(0, 0, 5))
item_gen(b_bounds = c(-2, 2), a_bounds = c(.75, 1.25), c_bounds = c(0, .25),
n_2pl = 5, n_3pl = 5)
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