Transcript [R] –irtoys

[R] –irtoys –
[R] –irtoys –
• For binary response data
• Provides common interface to some functions of
– ICL (external to R)
– BILOG (external to R)
– ltm (R function)
• Syntax used is simpler and consistent across
these packages
• Other useful IRT functions ~ NPP
• Good plotting capabilities
Dataset
• BDI (21 items)
• 818 subjects
• See word-doc for items
• Split the items into three sets:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Dataset 1:
descript(beck[,c(2,5,8,11,14,17,20)])
Sample:
7 items and 818 sample units;
0 missing values
Proportions for each level of
response:
0
1
logit
t1bdi1 0.7922 0.2078 -1.3381
t1bdi4 0.5538 0.4462 -0.2160
t1bdi7 0.5477 0.4523 -0.1913
t1bdi10 0.6027 0.3973 -0.4167
t1bdi13 0.4853 0.5147 0.0587
t1bdi16 0.4279 0.5721 0.2905
t1bdi19 0.4315 0.5685 0.2756
Frequencies of total scores:
0
1 2
3
4
5 6 7
Freq 133 114 91 115 103 106 91 65
Biserial correlation with Total
Score:
Included Excluded
t1bdi1
0.6005
0.4647
t1bdi4
0.7018
0.5590
t1bdi7
0.6190
0.4513
t1bdi10
0.6565
0.5025
t1bdi13
0.7167
0.5780
t1bdi16
0.6143
0.4466
t1bdi19
0.7188
0.5824
Cronbach's alpha:
value
All Items
0.7861
Excluding t1bdi1 0.7680
Excluding t1bdi4 0.7494
Excluding t1bdi7 0.7708
Excluding t1bdi10 0.7607
Excluding t1bdi13 0.7454
Excluding t1bdi16 0.7716
Excluding t1bdi19 0.7446
-irtoys- fitting 1PL/2PL models
• irtoys_beck_1pl1 <est(beck[,c(2,5,8,11,14,17,20)], model="1PL",
engine="ltm")
• irtoys_beck_2pl1 <est(beck[,c(2,5,8,11,14,17,20)], model="2PL",
engine="ltm")
> irtoys_beck_2pl1
[,1]
[,2] [,3]
t1bdi1 2.166786 1.04466980
t1bdi4 2.102861 0.18276282
t1bdi7 1.363356 0.19506741
t1bdi10 1.727944 0.37145589
t1bdi13 2.408886 -0.03577061
t1bdi16 1.368479 -0.28294050
t1bdi19 2.527411 -0.19989956
0
0
0
0
0
0
0
> irtoys_beck_1pl1
[,1]
[,2] [,3]
t1bdi1 1.860460 1.11158683
t1bdi4 1.860460 0.19005034
t1bdi7 1.860460 0.16912484
t1bdi10 1.860460 0.35943444
t1bdi13 1.860460 -0.04331848
t1bdi16 1.860460 -0.24064261
t1bdi19 1.860460 -0.22790729
0
0
0
0
0
0
0
par(mfrow = c(1,2))
plot(irf(irtoys_beck_1pl1), co=NA, main="1PL")
plot(irf(irtoys_beck_2pl1), co=NA, main="2PL")
Compare with Non-parametric
• Plot 1PL/2PL response functions for each item and
compare with non-parametric curve which does not
assume logistic function
• par(mfrow = c(1,1))
• npp(beck, items=c(2), from = -2, to = 4, main = "Item 2",
co=3)
• plot(irf(irtoys_beck_1pl1[c(1),]), co="red", add = TRUE)
• plot(irf(irtoys_beck_2pl1[c(1),]), co="blue", add = TRUE)
Estimating ability
th.mle_1pl1 <mlebme(resp=beck[,c(2,5,8,11,14,17,20)],
ip=irtoys_beck_1pl1)
th.mle_1pl2 <mlebme(resp=beck[,c(2,5,8,11,14,17,20)],
ip=irtoys_beck_2pl1)
Patterns
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
i1
0
1
1
1
0
1
1
0
0
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
i4
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
i7
1
1
1
1
1
0
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
i10
1
0
1
0
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
0
1
1
1
i13
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Ability/SE for 1PL Ability/SE for 2PL
i16
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
i19
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1.276574869
1.276574869
3.999935513
1.276574869
1.276574869
1.276574869
3.999935513
1.276574869
1.276574869
0.734365930
3.999935513
3.999935513
3.999935513
3.999935513
1.276574869
3.999935513
3.999935513
1.276574869
3.999935513
3.999935513
3.999935513
3.999935513
1.276574869
3.999935513
3.999935513
3.999935513
0.6175342
0.6175342
5.7727585
0.6175342
0.6175342
0.6175342
5.7727585
0.6175342
0.6175342
0.4850223
5.7727585
5.7727585
5.7727585
5.7727585
0.6175342
5.7727585
5.7727585
0.6175342
5.7727585
5.7727585
5.7727585
5.7727585
0.6175342
5.7727585
5.7727585
5.7727585
1.171368774
1.327602513
3.999928649
1.327602513
1.171368774
1.482503737
3.999928649
1.171368774
1.171368774
0.713049229
3.999928649
3.999928649
3.999928649
3.999928649
1.327602513
3.999928649
3.999928649
1.171368774
3.999928649
3.999928649
3.999928649
3.999928649
1.327602513
3.999928649
3.999928649
3.999928649
0.5730090
0.6230293
5.6888878
0.6230293
0.5730090
0.6841684
5.6888878
0.5730090
0.5730090
0.4720853
5.6888878
5.6888878
5.6888878
5.6888878
0.6230293
5.6888878
5.6888878
0.5730090
5.6888878
5.6888878
5.6888878
5.6888878
0.6230293
5.6888878
5.6888878
5.6888878
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