flagged.resp: Find (potentially) aberrant response patterns
In PerFit: Person Fit

Description Usage Arguments Details Value Author(s) See Also Examples

Find which respondents in the sample were flagged by the specified person-fit statistic.

flagged.resp(x, cutoff.obj=NULL, 
             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)

`x`	Object of class "PerFit".
`cutoff.obj`	Object of class "PerFit.cutoff".
`scores`	Logical: Should item scores of flagged respondents be shown in the output? Default is `TRUE`.
`ord`	Logical: Should items be ordered in increasing order of difficulty (i.e., in decreasing proportion-correct order)? Default is `TRUE`. Only used if `scores=TRUE`.
`ModelFit`	Method required to compute model-fitting item score patterns. The options available are `"NonParametric"` (default) and `"Parametric"`.
`Nreps`	Number of model-fitting item score patterns generated. Default is 1000.
`IP`	Matrix with previously estimated item parameters. Default is `x$IP`.
`IRT.PModel`	Parametric IRT model (required if `"ModelFit=Parametric"` or if the person fit statistic is parametric). Default is `x$IRT.PModel`.
`Ability`	Matrix with previously estimated item parameters. Default is `x$Ability`.
`Ability.PModel`	Method to use in order to estimate the latent ability parameters (required if `"ModelFit=Parametric"` or if the person fit statistic is parametric). Default is `x$Ability.PModel`.
`mu`	Mean of the apriori distribution. Only used when `method="BM"`. Default is 0.
`sigma`	Standard deviation of the apriori distribution. Only used when `method="BM"`. Default is 1.
`Blvl`	Significance level for bootstrap distribution (value between 0 and 1). Default is 0.05.
`Breps`	Number of bootstrap resamples. Default is 1000.
`CIlvl`	Level of bootstrap percentile confidence interval for the cutoff statistic.
`UDlvl`	User-defined cutoff level.

This function finds the respondents in the dataset that were flagged by the person-fit statistic. This statistic is specified by means of the "PerFit" class object x (x$PFStatistic).

The cutoff score may be provided by means of the cutoff.obj object, otherwise it is internally computed (for which the function parameters ModelFit through CIlvl are required; see cutoff for more details).

If scores=TRUE then the respondents' item scores will be shown in the output, either in the original item order (ord=FALSE) or in increasing difficulty order (ord=TRUE).

If scores=FALSE the output is a list with 3 elements:

`PFSscores`	A two-column matrix with the row index and the value of the person-fit statistic for the flagged respondents.
`Cutoff.lst`	The corresponding `PerFit.cutoff` object.
`PFS`	The person-fit statistic.

If scores=TRUE the output is a list with four elements:

`Scores`	Matrix with columns: `FlaggedID`, item scores (`It**`), and `PFscores`.
`MeanItemValue`	The items mean value (which is nothing more than the proportion-correct for dichotomous items).
`Cutoff.lst`	The corresponding `PerFit.cutoff` object.
`PFS`	The person-fit statistic.

Jorge N. Tendeiro tendeiro@hiroshima-u.ac.jp

cutoff, plot.PerFit, PRFplot

# Load the inadequacy scale data (dichotomous item scores):
data(InadequacyData)

# As an example, compute the Ht person-fit scores:
Ht.out <- Ht(InadequacyData)
Ht.out$PFscores

# Estimate the cutoff value at 1% level:
Ht.cut <- cutoff(Ht.out, Blvl=.01)

# Determine which respondents were flagged by Ht at 1% level:
flagged.resp(Ht.out, Ht.cut, scores=FALSE)$PFSscores