Description Usage Arguments Value Examples
Estimate the GPCM using the joint or marginal maximum likelihood estimation
model_gpcm_eap
scores response vectors using the EAP method
model_gpcm_map
scores response vectors using the MAP method
model_gpcm_jmle
estimates the parameters using the
joint maximum likelihood estimation (JMLE) method
model_gpcm_mmle
estimates the parameters using the
marginal maximum likelihood estimation (MMLE) method
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | model_gpcm_eap(u, a, b, d, D = 1.702, priors = c(0, 1),
bounds_t = c(-4, 4))
model_gpcm_map(u, a, b, d, D = 1.702, priors = c(0, 1),
bounds_t = c(-4, 4), iter = 30, conv = 0.001)
model_gpcm_dv_Pt(t, a, b, d, D)
model_gpcm_dv_Pa(t, a, b, d, D)
model_gpcm_dv_Pb(t, a, b, d, D)
model_gpcm_dv_Pd(t, a, b, d, D)
model_gpcm_dv_jmle(u_ix, dvp)
model_gpcm_jmle(u, t = NA, a = NA, b = NA, d = NA, D = 1.702,
iter = 100, nr_iter = 10, conv = 0.001, scale = c(0, 1),
bounds_t = c(-4, 4), bounds_a = c(0.01, 2.5), bounds_b = c(-4, 4),
bounds_d = c(-4, 4), priors = list(t = c(0, 1)), decay = 1,
verbose = FALSE, true_params = NULL)
model_gpcm_dv_mmle(u_ix, quad, pdv)
model_gpcm_mmle(u, t = NA, a = NA, b = NA, d = NA, D = 1.702,
iter = 100, nr_iter = 10, conv = 0.001, bounds_t = c(-4, 4),
bounds_a = c(0.01, 2.5), bounds_b = c(-4, 4), bounds_d = c(-4, 4),
priors = list(t = c(0, 1)), decay = 1, quad_degree = "11",
score_fn = c("eap", "map"), verbose = FALSE, true_params = NULL)
model_gpcm_fitplot(u, t, a, b, d, D = 1.702, d0 = NULL, index = NULL,
intervals = seq(-3, 3, 0.5))
|
u |
the observed response matrix, 2d matrix |
a |
discrimination parameters, 1d vector (fixed value) or NA (freely estimate) |
b |
difficulty parameters, 1d vector (fixed value) or NA (freely estimate) |
d |
category parameters, 2d matrix (fixed value) or NA (freely estimate) |
D |
the scaling constant, 1.702 by default |
priors |
a list of prior distributions |
bounds_t |
bounds of ability parameters |
iter |
the maximum iterations |
conv |
the convergence criterion of the -2 log-likelihood |
t |
ability parameters, 1d vector (fixed value) or NA (freely estimate) |
u_ix |
the 3d indices |
dvp |
the derivatives of P |
nr_iter |
the maximum iterations of newton-raphson |
scale |
the scale of theta parameters |
bounds_a |
bounds of discrimination parameters |
bounds_b |
bounds of location parameters |
bounds_d |
bounds of category parameters |
decay |
decay rate |
verbose |
TRUE to print debuggin information |
true_params |
a list of true parameters for evaluating the estimation accuracy |
quad_degree |
the number of quadrature points |
score_fn |
the scoring method: 'eap' or 'map' |
d0 |
insert an initial category value |
index |
the indices of items being plotted |
intervals |
intervals on the x-axis |
model_gpcm_eap
returns theta estimates and standard errors in a list
model_gpcm_map
returns theta estimates in a list
model_gpcm_jmle
returns estimated t, a, b, d parameters in a list
model_gpcm_mmle
returns estimated t, a, b, d parameters in a list
model_gpcm_fitplot
returns a ggplot
object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | with(model_gpcm_gendata(10, 40, 3),
cbind(true=t, est=model_gpcm_eap(u, a, b, d)$t))
with(model_gpcm_gendata(10, 40, 3),
cbind(true=t, est=model_gpcm_map(u, a, b, d)$t))
# generate data
x <- model_gpcm_gendata(1000, 40, 3)
# free calibration, 40 iterations
y <- model_gpcm_jmle(x$u, true_params=x, iter=40, verbose=TRUE)
# generate data
x <- model_gpcm_gendata(1000, 40, 3)
# free estimation, 40 iterations
y <- model_gpcm_mmle(x$u, true_params=x, iter=40, verbose=TRUE)
with(model_gpcm_gendata(1000, 20, 3),
model_gpcm_fitplot(u, t, a, b, d, index=c(1, 3, 5)))
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