Data matrix of dichotomous item scores: Persons as rows, items as columns, item scores are either 0 or 1, missing values allowed.
NA.method
Method to deal with missing values. The default is pairwise elimination ("Pairwise"). Alternatively, simple imputation methods are also available. The options available are "Hotdeck", "NPModel" (default), and "PModel".
Save.MatImp
Logical. Save (imputted) data matrix to file? Default is FALSE.
IP
Matrix with previously estimated item parameters: One row per item, and three columns ([,1] item discrimination; [,2] item difficulty; [,3] lower-asymptote, also referred to as pseudo-guessing parameter).
In case no item parameters are available then IP=NULL.
IRT.PModel
Specify the IRT model to use in order to estimate the item parameters (only if IP=NULL). The options available are "1PL", "2PL" (default), and "3PL".
Ability
Vector with previoulsy estimated latent ability parameters, one per respondent, following the order of the row index of matrix.
In case no ability parameters are available then Ability=NULL.
Ability.PModel
Specify the method to use in order to estimate the latent ability parameters (only if Ability=NULL). The options available are "ML" (default), "BM", and "WL".
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.
Details
Donlon and Fischer (1968) suggested to use the correlation between a respondent's score vector and the item proportion-correct scores in the sample as a measure of person fit. Low values should be indicative of misfit of the response vector with respect to the group of respondents.
Missing values in matrix are dealt with by means of pairwise elimination by default. Alternatively, single imputation is also available. Three single imputation methods exist: Hotdeck imputation (NA.method = "Hotdeck"), nonparametric model imputation (NA.method = "NPModel"), and parametric model imputation (NA.method = "PModel"); see Zhang and Walker (2008).
Hotdeck imputation replaces missing responses of an examinee ('recipient') by item scores from the examinee which is closest to the recipient ('donor'), based on the recipient's nonmissing item scores. The similarity between nonmissing item scores of recipients and donors is based on the sum of absolute differences between the corresponding item scores. The donor's response pattern is deemed to be the most similar to the recipient's response pattern in the group, so item scores of the former are used to replace the corresponding missing values of the latter. When multiple donors are equidistant to a recipient, one donor is randomly drawn from the set of all donors.
The nonparametric model imputation method is similar to the hotdeck imputation, but item scores are generated from Bernoulli distributions with probabilities defined by donors with similar total score than the recipient (based on all items except the NAs).
The parametric model imputation method is similar to the hotdeck imputation, but item scores are generated from Bernoulli distributions with probabilities estimated by means of parametric IRT models (IRT.PModel = "1PL", "2PL", or "3PL"). Item parameters (IP) and ability parameters (Ability) may be provided for this purpose (otherwise the algorithm finds estimates for these parameters).
Value
An object of class "PerFit", which is a list with 12 elements:
PFscores
A list of length N (number of respondents) with the values of the person-fit statistic.
PFstatistic
The person-fit statistic used.
PerfVects
A message indicating whether perfect response vectors (all-0s or all-1s) were removed from the analysis.
ID.all0s
Row indices of all-0s response vectors removed from the analysis (if applicable).
ID.all1s
Row indices of all-1s response vectors removed from the analysis (if applicable).
matrix
The data matrix after imputation of missing values was performed (if applicable).
Ncat
The number of response categories (2 in this case).
IRT.PModel
The parametric IRT model used in case NA.method="PModel", otherwise NULL.
IP
The Ix3 matrix of estimated item parameters in case NA.method="PModel", otherwise NULL.
Ability.PModel
The method used to estimate abilities in case NA.method="PModel", otherwise NULL.
Ability
The vector of N estimated ability parameters in case NA.method="PModel", otherwise NULL.
Donlon, T. F., and Fischer, F. E. (1968) An index of an individual's agreement with group-defined item difficulties. Educational and Psychological Measurement, 28(1), 105–113.
Karabatsos, G. (2003) Comparing the Aberrant Response Detection Performance of Thirty-Six Person-Fit Statistics. Applied Measurement In Education, 16(4), 277–298.
Meijer, R. R., and Sijtsma, K. (2001) Methodology review: Evaluating person fit. Applied Psychological Measurement, 25(2), 107–135.
Zhang, B., and Walker, C. M. (2008) Impact of missing data on person-model fit and person trait estimation. Applied Psychological Measurement, 32(6), 466–479.
Examples
# Load the inadequacy scale data (dichotomous item scores):
data(InadequacyData)
# Compute the r.pbis scores:
rpbis.out <- r.pbis(InadequacyData)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(PerFit)
Loading required package: ltm
Loading required package: MASS
Loading required package: msm
Loading required package: polycor
Loading required package: mvtnorm
Loading required package: sfsmisc
Loading required package: mirt
Loading required package: stats4
Loading required package: lattice
Attaching package: 'mirt'
The following object is masked from 'package:ltm':
Science
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/PerFit/r.pbis.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Personal biserial statistic
> ### Title: Personal biserial statistic
> ### Aliases: r.pbis
> ### Keywords: univar
>
> ### ** Examples
>
> # Load the inadequacy scale data (dichotomous item scores):
> data(InadequacyData)
>
> # Compute the r.pbis scores:
> rpbis.out <- r.pbis(InadequacyData)
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.