r.pbis Donlon and Fisher (1968) Dicho NParam
C.Sato Sato (1975) Dicho NParam
G, Gnormed van der Flier (1977), Meijer (1994) Dicho NParam
A.KB, D.KB, E.KB Kane and Brennan (1980) Dicho NParam
U3, ZU3 van der Flier (1980, 1982) Dicho NParam
Cstar Harnisch and Linn (1981) Dicho NParam
NCI Tatsuoka and Tatsuoaka (1982, 1983) Dicho NParam
lz Drasgow, Levine, and Williams (1985) Dicho Param
lzpoly Drasgow, Levine, and Williams (1985) Poly Param
Ht Sijtsma (1986) Dicho NParam
Gpoly Molenaar (1991) Poly NParam
Gnormed.poly Molenaar (1991), Emons (2008) Poly NParam
lzstar Snijders (2001) Dicho Param
U3poly Emons (2008) Poly NParam
}
All functions above have an output of class PerFit.
The package provides other functions that help analyzing the data when conducting person-fit analyses:
cutoff Estimate cutoff values for the person-fit statistics, to be used as decision rules.
flagged.resp Identify which respondents were flagged according to the chosen cutoff.
plot (class PerFit) Plot the distribution of person-fit scores with the cutoff superimposed.
PRFplot Plot the nonparametric person response function (Sijtsma and Meijer, 2001).
}
More person-fit statistics will be added to the package in future updates.
Versions
plot.PerFitandPRFplotnow allow the user to edit the axes labels and the titles.PerFitnow consists of a list with 12 objects.PerFitwere added (summary,print).cutoffwas updated. Now, model-fitting item response patterns are generated in order to find the cutoff value.plot.PerFitnow allows displaying a bootstrap percentile confidence interval for the cutoff statistic, as well as ticks marking the flagged respondents.fdapackage. The functional data objects are returned to the user.PerFit.SE).# 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
# Compute the cutoff value at 1% level:
set.seed(124) # To fix the random seed generator.
Ht.cut <- cutoff(Ht.out, Blvl=.01)
# Plot the sample distribution of the Ht scores with the above cutoff superimposed:
plot(Ht.out, cutoff.obj=Ht.cut)
# Determine which respondents were flagged by Ht at 1% level:
flagged.resp(Ht.out, cutoff.obj=Ht.cut, scores=FALSE)
# Flagged respondents: 30, 37, 46, 49,...
# Plot the person response function of respondent 30 (flagged as aberrant):
Resp30 <- PRFplot(InadequacyData, respID=30)
# Plot the person response function of respondent 35 (not flagged as aberrant):
Resp35 <- PRFplot(InadequacyData, respID=35)Run the code above in your browser using DataLab