Description Usage Arguments Value References Examples
This function calculates engineering optimal information (EOI ) function as introduced in Kingsbury & Wise (2020)
1 | EOI(theta, t.hat, items.administered, bank)
|
theta |
a numeric vector containing the true ability level of students |
t.hat |
a numeric vector containing the estimated ability level of students |
items.administered |
A data matrix that has the set of item items administered to individuals. This input assumes that every row in the data frame corresponds to the set of item names administered to an individual. |
bank |
A data matrix that have item parameters in the following order: discrimination, difficulty, guessing and slipping. |
Returns a single numeric value for EOI
Kingsbury, G. G., & Wise, S. L. (2020). Three Measures of Test Adaptation Based on Optimal Test Information. Journal of Computerized Adaptive Testing, 8(1), 1-19.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | library(catR)
N=1000 #number of students
bank=250 #number of items
items=45
theta=rnorm(N,0,1) #level of trait
model="2PL" #IRT model to use
start <- list(theta = -1:1, randomesque = 1)
stop <- list(rule = c( "length"), thr = items)
final <- list(method = "ML")
test=list(method = "ML", itemSelect = "MFI")
bank=genDichoMatrix(items =bank, cbControl = NULL,
model = model)
res <- simulateRespondents(thetas = theta, bank,
start = start, test = test, stop = stop,
final = final, model = NULL)
t.hat=res$final.values.df$estimated.theta
items.administered=res$responses.df[,grepl("items.administrated",
names( res$responses.df ) ) ]
colnames(items.administered)=NULL
diff=matrix(ncol = ncol(items.administered),nrow = nrow(items.administered))
for (k in 1:nrow(items.administered)) {
xx= as.numeric(items.administered[k,])
diff[k,]=bank[xx,2]
}
EOI(theta, t.hat, items.administered, bank)
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