Nothing
flagged.resp <- function(x, #x = an object from 'PerFit' class
cutoff.obj=NULL, #cutoff.obj = an object from 'PerFit.cutoff' class
scores=TRUE, ord=TRUE,
ModelFit="NonParametric", Nreps=1000,
IP=x$IP, IRT.PModel=x$IRT.PModel, Ability=x$Ability, Ability.PModel=x$Ability.PModel, mu=0, sigma=1,
Blvl = 0.05, Breps = 1000, CIlvl = 0.95,
UDlvl=NA)
{
# Sanity check - Class PerFit:
Sanity.cls(x)
#
upp.PFS <- c("Cstar", "C.Sato", "U3", "ZU3", "G", "Gnormed", "Gpoly", "Gnormed.poly", "U3poly", "D.KB")
low.PFS <- c("r.pbis", "NCI", "Ht", "A.KB", "E.KB", "lz", "lzstar", "lzpoly")
#
if (is.null(cutoff.obj))
{
cutoff.res <- cutoff(x, ModelFit, Nreps, IP, IRT.PModel, Ability, Ability.PModel, mu, sigma, Blvl, Breps, CIlvl, UDlvl)
} else
{
Sanity.clsPO(cutoff.obj)
cutoff.res <- cutoff.obj
}
#
if (any(x$PFStatistic == upp.PFS))
{
flagged.subs <- which(x$PFscores[, 1] >= cutoff.res$Cutoff)
}
if (any(x$PFStatistic == low.PFS))
{
flagged.subs <- which(x$PFscores[, 1] <= cutoff.res$Cutoff)
}
Ps <- round(colMeans(x$Matrix, na.rm = TRUE), 3)
# Not ordered by pvalue:
if (ord == FALSE)
{
flagged.scores <- x$Matrix[flagged.subs, ]
colnames(flagged.scores) <- paste("It", 1:dim(x$Matrix)[2], sep = "")
}
# Ordered by pvalue:
if (ord == TRUE)
{
matrix.ord <- x$Matrix[, order(Ps, decreasing = TRUE)] # ordered from easy to difficult
flagged.scores <- matrix.ord[flagged.subs, ]
colnames(flagged.scores) <- paste("It", order(Ps, decreasing = TRUE), sep = "")
Ps <- sort(Ps, decreasing = TRUE)
}
flagged.scores <- as.matrix(flagged.scores)
rownames(flagged.scores) <- NULL
res <- if (scores == FALSE)
{
list(PFSscores = cbind(FlaggedID = flagged.subs, PFscores = x$PFscores[flagged.subs, 1]),
Cutoff.lst = cutoff.res, PFS = x[[2]])
} else
{
list(Scores = cbind(FlaggedID = flagged.subs, flagged.scores, PFscores = x$PFscores[flagged.subs, 1]),
MeanItemValue = Ps, Cutoff.lst = cutoff.res,
PFS = x[[2]])
}
return(res)
}
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