mle | R Documentation |
mle
is a function for computing maximum likelihood estimates of theta.
mle(
object,
select = NULL,
resp,
start_theta = NULL,
max_iter = 100,
crit = 0.001,
truncate = FALSE,
theta_range = c(-4, 4),
max_change = 1,
use_step_size = FALSE,
step_size = 0.5,
do_Fisher = TRUE
)
## S4 method for signature 'item_pool'
mle(
object,
select = NULL,
resp,
start_theta = NULL,
max_iter = 50,
crit = 0.005,
truncate = FALSE,
theta_range = c(-4, 4),
max_change = 1,
use_step_size = FALSE,
step_size = 0.5,
do_Fisher = TRUE
)
MLE(
object,
select = NULL,
start_theta = NULL,
max_iter = 100,
crit = 0.001,
theta_range = c(-4, 4),
truncate = FALSE,
max_change = 1,
do_Fisher = TRUE
)
## S4 method for signature 'test'
MLE(
object,
select = NULL,
start_theta = NULL,
max_iter = 100,
crit = 0.001,
theta_range = c(-4, 4),
truncate = FALSE,
max_change = 1,
do_Fisher = TRUE
)
## S4 method for signature 'test_cluster'
MLE(object, select = NULL, start_theta = NULL, max_iter = 100, crit = 0.001)
object |
an |
select |
(optional) if item indices are supplied, only the specified items are used. |
resp |
item response on all (or selected) items in the |
start_theta |
(optional) initial theta values. If not supplied, EAP estimates using uniform priors are used as initial values. Uniform priors are computed using the |
max_iter |
maximum number of iterations. (default = |
crit |
convergence criterion to use. (default = |
truncate |
set |
theta_range |
a range of theta values to bound the estimate. Only effective when |
max_change |
upper bound to impose on the absolute change in theta between iterations. Absolute changes exceeding this value will be capped to |
use_step_size |
set |
step_size |
upper bound to impose on the absolute change in initial theta and estimated theta. Absolute changes exceeding this value will be capped to |
do_Fisher |
set |
mle
returns a list containing estimated values.
th
theta value.
se
standard error.
conv
TRUE
if estimation converged.
trunc
TRUE
if truncation was applied on th
.
mle(itempool_fatigue, resp = resp_fatigue_data[10, ])
mle(itempool_fatigue, select = 1:20, resp = resp_fatigue_data[10, 1:20])
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