This function computes the efficiency of the D-, I-, and A-optimal designs compared to the random design for each block.
efficiency(
yyy,
uncert = FALSE,
ipop,
oc = "D",
L = NULL,
items = FALSE,
integ = TRUE
)if false (default), abilities are assumed to be known; if true, handling of uncertainties of Bjermo et al. (2021) is used.
matrix with item parameters for operational items (used if uncert=TRUE, only).
optimality criterion: "D" (D-optimality, default), "I" (I-optimality with standard normal weight function), "A" (A-optimality).
L-matrix (not used for D-optimality).
If false (default), only total block efficiency is returned. If true, for each block, criteria for optimal and random, and the efficiency for each item are reported in each column of output, except for 1-pl model where each column represents the parameter efficiency. The last column shows total criteria and efficiency. D-, L-, I-, A-optimality.
if true (default), integrate() is used for computation of partial information matrices; if false, Riemann rule is used.
# Example No.1
# 2PL-models for two items; parameters (a, b)=(1.6, -1) and (1.6, 1), respectively
a<-c(1.6, 1.6); b<-c(-1, 1)
ip <- cbind(a,b)
yyy <- optical(ip)
# Efficiency of A-optimal design compared to random design
efficiency(yyy, oc="A")
#> $block1
#> [1] 1.369236
#>
if (FALSE) {
# Example No.2
# 2PL-models for six items; the parameters for these items are a=(1.62, 1.4, 0.98, 0.66, 0.92, 0.9),
# and b=(-0.47, -1.71, 0.62, -0.15, -1.71, 1.6), respectively.
a <- c(1.62, 1.4, 0.98, 0.66, 0.92, 0.9)
b <- c(-0.47, -1.71, 0.62, -0.15, -1.71, 1.6)
ip <- cbind(a, b)
bid <- c(1, 1, 1, 2, 2, 2)
yyy <- optical(ip, bid = bid, show_progress = 2)
# Efficiency of D-optimal design compared to random design
efficiency(yyy, oc = "D")
# Efficiency of A-optimal design compared to random design
efficiency(yyy, oc = "A")
# Efficiency of I-optimal design compared to random design
efficiency(yyy, oc = "I")
}