Nothing
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# A, D, E (Kane & Brennan, 1980)
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A.KB <- 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)
matrix.NAs.0 <- matrix
matrix.NAs.0[is.na(matrix.NAs.0)] <- 0
res.red <- as.vector(matrix.NAs.0 %*% pi)
# Compute final PFS vector:
res <- final.PFS(res.red, all.0s, all.1s, N)
# Export results:
export.res.NP(matrix.sv, N, res, "A.KB", part.res, Ncat=2, NA.method,
IRT.PModel, res.NA[[2]], Ability.PModel, res.NA[[3]], IP.NA, Ability.NA, res.NA[[4]])
}
D.KB <- 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)
pi.ord <- sort(pi, decreasing = TRUE)
matrix.NAs.0 <- matrix
matrix.NAs.0[is.na(matrix.NAs.0)] <- 0
a <- matrix.NAs.0 %*% pi
N.red <- dim(matrix)[1]
a.max <- if (sum(is.na(matrix)) > 0)
{
unlist(lapply(1:N.red, function(i) {cumsum(pi.ord[!is.na(matrix[i, ])])[NC[i]]}))
} else
{
cumsum(pi.ord)[NC]
}
res.red <- as.vector(a.max - a)
# Compute final PFS vector:
res <- final.PFS(res.red, all.0s, all.1s, N)
# Export results:
export.res.NP(matrix.sv, N, res, "D.KB", part.res, Ncat=2, NA.method,
IRT.PModel, res.NA[[2]], Ability.PModel, res.NA[[3]], IP.NA, Ability.NA, res.NA[[4]])
}
E.KB <- 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)
pi.ord <- sort(pi, decreasing=TRUE)
matrix.NAs.0 <- matrix
matrix.NAs.0[is.na(matrix.NAs.0)] <- 0
a <- matrix.NAs.0 %*% pi
N.red <- dim(matrix)[1]
a.max <- if (sum(is.na(matrix)) > 0)
{
unlist(lapply(1:N.red, function(i) {cumsum(pi.ord[!is.na(matrix[i, ])])[NC[i]]}))
} else
{
cumsum(pi.ord)[NC]
}
res.red <- as.vector(a / a.max)
# Compute final PFS vector:
res <- final.PFS(res.red, all.0s, all.1s, N)
# Export results:
export.res.NP(matrix.sv, N, res, "E.KB", 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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