estimate_grm {Rirt} | R Documentation |
Estimate the GRM using the joint or marginal maximum likelihood estimation
model_grm_eap
scores response vectors using the EAP method
model_grm_map
scores response vectors using the MAP method
model_grm_jmle
estimates the parameters using the
joint maximum likelihood estimation (JMLE) method
model_grm_mmle
estimates the parameters using the
marginal maximum likelihood estimation (MMLE) method
model_grm_eap(u, a, b, D = 1.702, priors = c(0, 1), bounds_t = c(-4, 4)) model_grm_map(u, a, b, D = 1.702, priors = c(0, 1), bounds_t = c(-4, 4), iter = 30, conv = 0.001) model_grm_dv_Pt(t, a, b, D) model_grm_dv_Pa(t, a, b, D) model_grm_dv_Pb(t, a, b, D) model_grm_dv_jmle(u_ix, dvp) model_grm_jmle(u, t = NA, a = NA, b = 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), priors = list(t = c(0, 1)), decay = 1, verbose = FALSE, true_params = NULL) model_grm_dv_mmle(u_ix, quad, pdv) model_grm_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), priors = list(t = c(0, 1)), decay = 1, quad_degree = "11", score_fn = c("eap", "map"), verbose = FALSE, true_params = NULL) model_grm_fitplot(u, t, a, b, D = 1.702, 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, 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 for 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 newton-raphson iterations, default=10 |
scale |
the scale of theta parameters |
bounds_a |
bounds of discrimination parameters |
bounds_b |
bounds of location 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' |
index |
the indices of items being plotted |
intervals |
intervals on the x-axis |
model_grm_eap
returns theta estimates and standard errors in a list
model_grm_map
returns theta estimates in a list
model_grm_jmle
returns estimated t, a, b parameters in a list
model_grm_mmle
returns estimated t, a, b parameters in a list
model_grm_fitplot
returns a ggplot
object
with(model_grm_gendata(10, 50, 3), cbind(true=t, est=model_grm_eap(u, a, b)$t)) with(model_grm_gendata(10, 50, 3), cbind(true=t, est=model_grm_map(u, a, b)$t)) # generate data x <- model_grm_gendata(1000, 40, 3) # free calibration, 40 iterations y <- model_grm_jmle(x$u, true_params=x, iter=40, verbose=TRUE) # generate data x <- model_grm_gendata(1000, 40, 3) # free estimation, 40 iterations y <- model_grm_mmle(x$u, true_params=x, iter=40, verbose=TRUE) with(model_grm_gendata(1000, 20, 3), model_grm_fitplot(u, t, a, b, index=c(1, 3, 5)))