Nothing
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# U3 (van der Flier, 1980, 1982):
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U3 <- function(matrix,
NA.method="Pairwise", Save.MatImp=FALSE,
IP=NULL, IRT.PModel="2PL", Ability=NULL, Ability.PModel="ML", mu=0, sigma=1)
{
matrix <- as.matrix(matrix)
N <- dim(matrix)[1]; I <- dim(matrix)[2]
IP.NA <- is.null(IP); Ability.NA <- is.null(Ability)
# Sanity check - Data matrix adequacy:
Sanity.dma(matrix, N, I)
# Dealing with missing values:
res.NA <- MissingValues(matrix, NA.method, Save.MatImp, IP, IRT.PModel, Ability, Ability.PModel, mu, sigma)
matrix <- res.NA[[1]]
# Sanity check - Perfect response vectors:
part.res <- Sanity.prv(matrix, N, I)
NC <- part.res$NC
all.0s <- part.res$all.0s
all.1s <- part.res$all.1s
matrix.sv <- matrix
matrix <- part.res$matrix.red
# Compute PFS:
pi <- colMeans(matrix.sv, na.rm = TRUE); qi <- 1-pi
# If there are answer options not chosen by any respondent then some entries in pi are 0 or 1.
# Below all corresponding logs are set from Inf to 0.
# (Reason: They carry no individual information regarding aberrant response behavior.):
log.odds <- log(pi/qi)
log.odds[is.infinite(log.odds)] <- 0
log.odds.ord <- sort(log.odds, decreasing = TRUE)
#
sum.first.logodds <- if (sum(is.na(matrix)) > 0)
{
apply(matrix, 1, function(vec)
{
NA.vec <- sum(vec, na.rm = TRUE)
sum(log.odds.ord[!is.na(vec)][1:NA.vec])
})
} else
{
cumsum(log.odds.ord)[NC]
}
log.odds.ordrev <- sort(log.odds, decreasing = FALSE)
sum.last.logodds <- if (sum(is.na(matrix)) > 0)
{
apply(matrix, 1, function(vec)
{
NA.vec <- sum(vec, na.rm = TRUE)
sum(log.odds.ordrev[!is.na(vec)][1:NA.vec])
})
} else
{
cumsum(log.odds.ordrev)[NC]
}
matrix.NAs.0 <- matrix
matrix.NAs.0[is.na(matrix.NAs.0)] <- 0
res.red <- as.vector((sum.first.logodds - as.vector(matrix.NAs.0 %*% log.odds)) / (sum.first.logodds - sum.last.logodds))
# Compute final PFS vector:
res <- final.PFS(res.red, all.0s, all.1s, N)
# Export results:
export.res.NP(matrix.sv, N, res, "U3", part.res, Ncat=2, NA.method,
IRT.PModel, res.NA[[2]], Ability.PModel, res.NA[[3]], IP.NA, Ability.NA, res.NA[[4]])
}
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