estimate_3pl {Rirt} | R Documentation |
Estimate the 3PL model using the joint or marginal maximum likelihood estimation methods
model_3pl_eap
scores response vectors using the EAP method
model_3pl_map
scores response vectors using the MAP method
model_3pl_jmle
estimates the item and ability parameters
using the joint maximum likelihood estimation (JMLE) method
model_3pl_mmle
estimates the item parameters using the
marginal maximum likelihood estimation (MMLE) method
model_3pl_eap(u, a, b, c, D = 1.702, priors = c(0, 1), bounds_t = c(-4, 4)) model_3pl_map(u, a, b, c, D = 1.702, priors = c(0, 1), bounds_t = c(-4, 4), iter = 30, conv = 0.001) model_3pl_dv_Pt(t, a, b, c, D) model_3pl_dv_Pa(t, a, b, c, D) model_3pl_dv_Pb(t, a, b, c, D) model_3pl_dv_Pc(t, a, b, c, D) model_3pl_dv_jmle(pdv_fn, u, t, a, b, c, D) model_3pl_jmle(u, t = NA, a = NA, b = NA, c = NA, D = 1.702, iter = 100, conv = 0.001, nr_iter = 10, scale = c(0, 1), bounds_t = c(-4, 4), bounds_a = c(0.01, 2.5), bounds_b = c(-4, 4), bounds_c = c(0, 0.4), priors = list(t = c(0, 1)), decay = 1, verbose = FALSE, true_params = NULL) model_3pl_dv_mmle(pdv_fn, u, quad, a, b, c, D) model_3pl_mmle(u, t = NA, a = NA, b = NA, c = NA, D = 1.702, iter = 100, conv = 0.001, nr_iter = 10, bounds_t = c(-4, 4), bounds_a = c(0.01, 2.5), bounds_b = c(-4, 4), bounds_c = c(0, 0.4), priors = list(t = c(0, 1)), decay = 1, quad = "11", score_fn = c("eap", "map"), verbose = FALSE, true_params = NULL) model_3pl_fitplot(u, t, a, b, c, D = 1.702, index = NULL, intervals = seq(-3, 3, 0.5))
u |
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) |
c |
pseudo-guessing parameters, 1d vector (fixed value) or NA (freely estimate) |
D |
the scaling constant, 1.702 by default |
priors |
prior distributions, a list |
bounds_t |
the bounds of ability parameters |
iter |
the maximum iterations, default=100 |
conv |
the convergence criterion |
t |
ability parameters, 1d vector (fixed value) or NA (freely estimate) |
pdv_fn |
the function to compute derivatives of P w.r.t the estimating parameters |
nr_iter |
the maximum newton-raphson iterations, default=10 |
scale |
the mean and SD of the theta scale, default= |
bounds_a |
the bounds of discrimination parameters |
bounds_b |
the bounds of difficulty parameters |
bounds_c |
the bounds of guessing parameters |
decay |
decay rate, default=1 |
verbose |
TRUE to print details for debugging |
true_params |
a list of true parameters for evaluating the parameter recovery |
quad |
the number of quadrature points |
score_fn |
the scoring function: 'eap' or 'map' |
index |
the indices of items being plotted |
intervals |
intervals on the x-axis |
model_3pl_eap
returns theta estimates and standard errors in a list
model_3pl_map
returns theta estimates in a list
model_3pl_jmle
returns estimated t, a, b, c parameters in a list
model_3pl_mmle
returns estimated t, a, b, c parameters in a list
model_3pl_fitplot
returns a ggplot
object
with(model_3pl_gendata(10, 40), cbind(true=t, est=model_3pl_eap(u, a, b, c)$t)) with(model_3pl_gendata(10, 40), cbind(true=t, est=model_3pl_map(u, a, b, c)$t)) # generate data x <- model_3pl_gendata(2000, 40) # free estimation, 40 iterations y <- model_3pl_jmle(x$u, true_params=x, iter=40, verbose=TRUE) # fix c-parameters, 40 iterations y <- model_3pl_jmle(x$u, c=0, true_params=x, iter=40) # generate data x <- model_3pl_gendata(2000, 40) # free estimation, 40 iterations y <- model_3pl_mmle(x$u, true_params=x, iter=40, verbose=TRUE) with(model_3pl_gendata(1000, 20), model_3pl_fitplot(u, t, a, b, c, index=c(1, 3, 5)))