optical {optical} | R Documentation |
Optimal item calibration
Description
Calibrate items following a 2PL, 3PL, mixture of 2PL and 3PL model, or 2PL with common discrimination for all items (Rasch-type).
Usage
optical(
ip,
oc = "D",
uncert = FALSE,
ipop,
imf = c(0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.45),
maxiter = rep(300, 6),
eps = rep(0.002, 6),
nnn = c(0, 50, 50, 200, 200, 200),
nsp = c(0.001, 1e-04, 1e-04, 1e-05, 1e-05, 1e-05),
sss = 0.001,
falpha = 1.08,
sdr = TRUE,
ig = 3,
ex = 0,
integ = TRUE,
show_progress = 1
)
Arguments
ip |
matrix with item parameters for all items (number of rows determines number of items; number of column is 2 (2PL or Rasch-type with NA from second item in first column) or 3 (3PL or mixed 2/3-PL with NA for 2PL-items in third column). |
oc |
optimality criterion: "D" (D-optimality, default), "I" (I-optimality with standard normal weight function), "A" (A-optimality). |
uncert |
if false (default), abilities are assumed to be known; if true, handling of uncertainties of Bjermo et al. (2021) is used. |
ipop |
matrix with item parameters for operational items (used if uncert=TRUE, only). |
imf |
the vector of step-lengths; default c(0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.45). |
maxiter |
maximal number of iterations in each inner loop, the length of this vector defines the number of outer loops. |
eps |
convergence criterion (maximum violation of eq.th.), vector with value for each iteration in the outer loop, but the same number for all iterations is recommended. |
nnn |
number of new nodes added at each position in the adaptive grid, vector with value for each iteration in the outer loop (nnn [1] not used). |
nsp |
node spacing between new nodes, vector with value for each iteration in the outer loop (nsp [1] is the spacing between nodes of the starting grid). |
sss |
step size stopping criterion. |
falpha |
factor alpha for adjusting the step size vector (should be > 1). |
sdr |
stop if design repeated (flag TRUE/FALSE). |
ig |
inner grid between -ig and ig. |
ex |
intervals of size < ex will be removed (consolidate); if ex=0, no consolidation will be done. |
integ |
if true (default), integrate() is used for computation of partial information matrices; if false, Riemann rule is used. |
show_progress |
if 1 (default), no output will be printed for each iteration. If 2, the + symbols will be printed on a line for each Iteration If 3, some output of the function will be printed. |
Value
Result of this function is a list with following instances:
dd |
directional derivatives of optimal solution. |
xi |
optimal solution. |
t |
final grid of ability values which was used. |
viomax |
largest violation of eq.th. from final solution (if < eps, alg. has converged, otherwise not). |
h1 |
interval boundaries for optimal solution. |
ht |
Refined table of interval boundaries for optimal design with calibrated items and their corresponding probabilities |
mooiter |
monitoring iterations; information about each iteration to produce convergence plots. |
time |
running time of algorithm in minutes. |
oc |
optimality criterion ("D", "I", "A", "L"). |
L |
L-matrix (not for D-optimality). |
Author(s)
Mahmood Ul Hassan (scenic555@gmail.com); Frank Miller (frank.miller@liu.se)
References
Ul Hassan and Miller (2021). An exchange algorithm for optimal calibration of items in computerized achievement tests.Computational Statistics and Data Analysis, 157: 107177.
Ul Hassan and Miller (2019). Optimal item calibration for computerized achievement tests. Psychometrika, 84, 1101-1128.
Bjermo, Fackle-Fornius, and Miller (2021). Optimizing Calibration Designs with Uncertainty in Abilities. Manuscript.
See Also
drawdesign
, convergenceplot
,
efficiency
Examples
# 2PL-models for two items; parameters (a, b)=(1.6, -1) and (1.6, 1), respectively
ip <- cbind(c(1.6, 1.6),c(-1, 1))
yyy <- optical(ip)
# Table of interval boundaries for D-optimal design with items and
# probabilities (expected proportion of examinees in this interval)
yyy$ht
# 1PL-models with common discrimination parameter for two items
# (model assumption is that both have same discrimination);
# parameters (a, b)=(1.6, -1) and (1.6, 1), respectively;
# NA for discrimination means that item has same parameter as preceeding item
ip <- cbind(c(1.6, NA), c(-1, 1))
yyy <- optical(ip)
# Table of interval boundaries for D-optimal design with items and
# probabilities (expected proportion of examinees in this interval)
yyy$ht
# 3PL-models for three items; parameters (a, b, c)=(1, 2, 2.5),
# (-1.5, 0.5, 2) and (0.2, 0.1, 0.05), respectively.
ip <- cbind(c(1, 2, 2.5),c(-1.5, 0.5, 2),c(0.2, 0.1, 0.05))
yyy <- optical(ip)
# Table of interval boundaries for D-optimal design with items and
# probabilities (expected proportion of examinees in this interval)
yyy$ht