## Dichotomous models ##
# Loading the 'tcals' parameters
data(tcals)
# Item bank creation with 'tcals' item parameters
prov <- breakBank(tcals)
bank <- prov$itemPar
cbGroup <- prov$cbGroup
## MFI criterion
# Selecting the next item, current ability estimate is 0
nextItem(bank, theta = 0) # item 63 is selected
# Selecting the next item, current ability estimate is 0 and item 63 is removed
nextItem(bank, theta = 0, out = 63) # item 10 is selected
# Selecting the next item, current ability estimate is 0 and items 63 and 10 are
# removed
nextItem(bank, theta = 0, out = c(63, 10)) # item 62 is selected
# Item exposure control by selecting three items (selected item will be either 10, 62
# or 63)
nextItem(bank, theta = 0, randomesque = 3)
## bOpt method
# Selecting the next item, current ability estimate is 0
nextItem(bank, theta = 0, criterion = "bOpt") # item 24 is selected
# Selecting the next item, current ability estimate is 0 and item 24 is removed
nextItem(bank, theta = 0, out = 24, criterion = "bOpt")
## thOpt method
# Selecting the next item, current ability estimate is 0
nextItem(bank, theta = 0, criterion = "thOpt") # item 76 is selected
# Selecting the next item, current ability estimate is 0 and item 76 is removed
nextItem(bank, theta = 0, out = 76, criterion = "thOpt") # item 70 is selected
## MLWI and MPWI methods
# Selecting the next item, current response pattern is 0 and item 63 was administered
# first
nextItem(bank, x = 0, out = 63, criterion = "MLWI")
nextItem(bank, x = 0, out = 63, criterion = "MPWI")
# Selecting the next item, current response pattern is (0,1) and item 19 is removed
nextItem(bank, x = c(0, 1), out = c(63, 19), criterion = "MLWI")
nextItem(bank, x = c(0, 1), out = c(63, 19), criterion = "MPWI")
## MEI method
# Selecting the next item, current response pattern is 0 and item 63 was administered
# first
# Ability estimation by WL method
th <- thetaEst(rbind(bank[63,]), 0, method = "WL")
nextItem(bank, x = 0, out = 63, theta = th, criterion = "MEI") # item 49 is selected
# With Fisher information
nextItem(bank, x = 0, out = 63, theta = th, criterion = "MEI", infoType = "Fisher")
# item 10 is selected
## MEPV method
# Selecting the next item, current response pattern is 0 and item 63 was administered
# first
# Ability estimation by WL method
nextItem(bank, x = 0, out = 63, theta = th, criterion = "MEPV") # item 19 is selected
## KL and KLP methods
# Selecting the next item, current response pattern is 0 and item 63 was administered
# first
# Ability estimation by WL method
nextItem(bank, x = 0, out = 63, theta = th, criterion = "KL") # item 19 is selected
nextItem(bank, x = 0, out = 63, theta = th, criterion = "KLP") # item 44 is selected
## Progressive method
# Selecting the next item, current ability estimate is 0 and item 63 was administered
# first
# (default options: "length" rule with "thr = 20")
nextItem(bank, out = 63, theta = 0, criterion = "progressive")
nextItem(bank, out = 63, theta = 0, criterion = "progressive")
# result can be different!
## Proportional method
# Selecting the next item, current ability estimate is 0 and item 63 was administered
# first
# (default options: "length" rule with "thr = 20")
nextItem(bank, out = 63, theta = 0, criterion = "proportional")
nextItem(bank, out = 63, theta = 0, criterion = "proportional")
# result can be different!
## Random method
# Selecting the next item, item 63 was administered first
nextItem(bank, out = 63, criterion = "random")
nextItem(bank, out = 63, criterion = "random") # may produce a different result
## Content balancing
# Creation of the 'cbList' list with arbitrary proportions
cbList <- list(names = c("Audio1", "Audio2", "Written1", "Written2", "Written3"),
props = c(0.1, 0.2, 0.2, 0.2, 0.3))
# Selecting the next item, MFI criterion, current ability estimate is 0, items 12, 33,
# 46 and 63 previously administered
nextItem(bank, theta = 0, out = c(12, 33, 46, 63), cbControl = cbList,
cbGroup = cbGroup) # item 70 is selected
## Polytomous models ##
# Generation of an item bank under GRM with 100 items and at most 4 categories
m.GRM <- genPolyMatrix(100, 4, "GRM")
m.GRM <- as.matrix(m.GRM)
# Current ability estimate is 0
# Selecting the next item, current response pattern is 1 and item 70 was administered
# first
## MFI method
nextItem(m.GRM, model = "GRM", theta = 0, criterion = "MFI", out = 70)
## Progressive method
nextItem(m.GRM, model = "GRM", theta = 0, criterion = "progressive", out = 70)
## KL method
nextItem(m.GRM, model = "GRM", theta = 0, criterion = "KL", out = 70, x = 1)
## MFI with content balancing
cbList <- list(names = c("Audio1","Audio2","Written1","Written2", "Written3"),
props = c(0.1,0.2,0.2,0.2,0.3))
m.GRM<-genPolyMatrix(100, 4, model = "GRM", cbControl = cbList)
bank<-breakBank(m.GRM)
nextItem(bank$itemPar, model = "GRM", theta = 0, criterion = "MFI", out = 70,
cbControl = cbList, cbGroup = bank$cbGroup)
# Generation of an item bank under PCM with 20 items and 4 categories
m.PCM <- genPolyMatrix(20, 4, "PCM", same.nrCat = TRUE)
m.PCM <- as.matrix(m.PCM)
# Current ability estimate is 0
# Selecting the next item, current response pattern is 1 and item 15 was administered
# first
## MFI method
nextItem(m.PCM, model = "PCM", theta = 0, criterion = "MFI", out = 15)
## Progressive method
nextItem(m.PCM, model = "PCM", theta = 0, criterion = "progressive", out = 15)
## KL method
nextItem(m.PCM, model = "PCM", theta = 0, criterion = "KL", out = 15, x = 1)
## MFI with content balancing
cbList <- list(names = c("Audio1", "Audio2", "Written1", "Written2", "Written3"),
props = c(0.1, 0.2, 0.2, 0.2, 0.3))
m.PCM<-genPolyMatrix(100, 4, model = "PCM", cbControl = cbList)
bank<-breakBank(m.PCM)
nextItem(bank$itemPar, model = "PCM", theta = 0, criterion = "MFI", out = 15,
cbControl = cbList, cbGroup = bank$cbGroup)Run the code above in your browser using DataLab