PP 1.0.0 rebuilds the numerical core. Every estimate is now checked
against independent reference implementations. For ordinary data
(complete responses, no extreme scores) the MLE, WLE, MAP and EAP
estimates of 0.6.4-1 are reproduced (differences below 0.001, within the
convergence criterion exac; robust estimates differ by
about 0.005). Results change in the places listed below: standard errors
of MAP and robust estimates, the jackknife, plausible values, person
fit, and edge cases such as missing responses, extreme scores and the
3PL. Function and argument names are unchanged unless stated.
JKpp()): the standard error
now follows the textbook formula for pseudovalues; it is larger than
before (by a factor of about L − 1, L = number of items) and comparable
to the MLE/WLE standard error. The jackknife runs over the answered
items only (before: all items), leave-one-out samples without a finite
estimate are counted in the new column excluded with a
warning, and the EAP jackknife works for mixed tests.cmeth = "AMT"): now the
AMT-robustified jackknife of Wainer & Wright (1980): Andrews’ sine
estimator (c = 2.1, MAD scale), applied to the pseudovalues of the right
and of the wrong answers separately and averaged with the group sizes as
weights.exac (before
up to 3e-3 off).status.PV()) are drawn from
the posterior on a grid (approx = FALSE) or from a normal
distribution with its mean and SD (approx = TRUE, default).
Both use the prior of each person and work with missing responses. The
Metropolis-Hastings sampler is gone, and its arguments
thinning, burnin and mult are
ignored with a warning.Pfit()): lz* uses the
answered items only, so it works with missing responses; infit and
outfit of the PCM take the number of categories from the item parameters
and respect the slopes of the GPCM; the jackknife SE of lz uses
person-specific priors. Results are no longer rounded (p values of
0.0004 were shown as 0).mu, sigma2 or item parameters are
recycled to the persons or items.killdupli is gone (see Deprecated).sim_4pl() and
sim_gpcm().PV() needs an estimate with a prior,
i.e. type = "eap" or "map"; it stops for MLE,
WLE and robust estimates. These have no prior any more: mu
and sigma2 given there are ignored with a warning and
stored as NA.range (new argument, default c(-6, 6)) get the
bound with status "boundary" and a warning (before: NaN or
drifting values). Persons without any response get NA
(before: +Inf); for EAP the prior mean and SD.respm are not carried over (use a
person variable in PPass() for ids).PPass() on eRm objects: the method
PPass.Rm is replaced by PPass.eRm, and the
result of PPass() is a data.frame of class
ppass with the new column status. For
type = "eap" and "robust" it contains the
estimates only (with a message).sim_4pl() and sim_gpcm() draw differently,
so simulated data are not identical to 0.6.4-1 for the same seed.lzstar_mixed(), Pfit_lzstarmix()
and print_personfit_summary() (new in 0.6.4-1).
Pfit() now gives lz and lz* for all models (lz* after
Sinharay, 2016, with the correction of the estimation type),
summary() of its result replaces the printed summary.
Pfit() takes the item parameters from the estimation
object.$ipar of estimation objects (the processed
input in the 0.6.4 layout) is no longer part of the result; the
estimates are in $estimates.JKpp(maxsteps, exac) default to the values of the
estimation object (before 500 and 0.001).Pfit(pp, fitindices, SE): the argument
respm is removed, the responses come from the estimation
object. Use Pfit(res) instead of
Pfit(respm = X, pp = res). The estimation objects of
0.6.4-1 did not contain the rows of persons with an extreme score (MLE)
and of duplicated patterns, so the responses had to be passed again; the
new objects have one row per person.$estimates, a data.frame with
estimate, SE, nsteps and
status ("ok", "extreme",
"boundary", "no_response",
"not_converged") per person; coef() and
as.data.frame() methods. JKpp() returns
$estimates with L, excluded and
status.range and verbose in
PP_4pl(), PP_gpcm() and PPall().
Defaults are unified: maxsteps = 100,
exac = 0.001.Pfit() works for all models (1PL–4PL, PCM, GPCM and
mixed tests), for MLE, WLE and MAP, with missing responses. Jackknife
SEs for all four statistics (new columns in_se,
ou_se), print() and
summary().PPass():
mod and model2est: the
model (1PL to 4PL, PCM, GPCM or a mixed test) follows from the item
parameters, as in PP_4pl(); both are ignored with a
warning. Before, mod only switched between
PP_4pl(), PP_gpcm() and PPall(),
and mod = "MIXED" did not work.items can be given by name; the
other columns (e.g. an id) are kept as person variables.eRm::person.parameter(). eRm is not needed at run
time.print() and summary() give an overview
with the persons flagged by the person fit; JKpp() and
PV() work directly on the result.plot() for the results of PPass(),
PP_4pl(), PP_gpcm() and PPall()
(help page ?"pp-plots"), drawn with the new dependency
tinyplot:
which = "fit": person fit map, the fit statistic
against the estimate with the flagged persons labelled;which = "precision": the standard error of each person
and of the whole test, 1/sqrt(I(theta)) from the known item parameters,
below the distribution of the estimates and with the location of every
item;which = "profile": observed against expected scores of
the answered items of single persons, with the unexpected answers
labelled.sim_mixed() simulates tests of 4PL and GPCM items.PPall(): slopes, lowerA and
upperA are optional; PP_4pl() with
lowerA but without slopes fits the 3PL with
slopes 1.cat() output and no warning for
the default prior.ctrl in PP_4pl(),
PP_gpcm() and PPall() are ignored with a
warning: killdupli (estimate duplicated response patterns
once) and skipcheck (skip the checks of the responses)
saved time with the old estimation routine, which iterated all persons
together. Every person is now estimated separately, and the checks
always run. JKpp(ctrl) had no effect already in 0.6.4-1 and
is ignored with a warning, too.PPall(model2est) is ignored with a warning: the model
of each item follows from thres (one threshold: 4PL item,
several: GPCM item). A dichotomous item declared as "GPCM"
was the same as a 4PL item with asymptotes 0 and 1.
findmodel() shows the model of each item.$resPP (estimation objects) and $resjk
(jackknife) are kept as aliases of the 0.6.4 layout; use
$estimates.The C++ core estimates every person separately with its own
convergence check (before: all persons iterated until the slowest
converged). Median times on 5000 persons × 40 items: 1PL–3PL estimates
0.02 s (before 0.3–2.9 s), EAP 0.03 s (0.8 s); GPCM with 2000 × 20: 0.01
s (0.2–1.3 s); JKpp() on 500 × 40: 0.05 s (142 s);
PV() 150 times and sim_gpcm() 400 times
faster.
lzstar_mixed(),
Pfit_lzstarmix(),
print_personfit_summary().