flagged.resp {PerFit}

Find (potentially) aberrant response patterns

Description

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

Usage

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)

Arguments

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.

Details

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).

Value

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.

Author(s)

Jorge N. Tendeiro [e-mail]

See Also

cutoff, plot.PerFit, PRFplot

Examples

# 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