BER 670 Data Analysis 1
Qingzhou Shi
9/25/2020
library(TAM)## Loading required package: CDM## Loading required package: mvtnorm## **********************************
## ** CDM 7.5-15 (2020-03-10 14:19:21)
## ** Cognitive Diagnostic Models **
## **********************************## * TAM 3.5-19 (2020-05-05 22:45:39)library(WrightMap)
library(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:base':
##
## format.pval, unitslibrary(formattable)
transreas <- read.csv("~/Box/Courses/2020 Fall/BER 670 Rasch/HW/transreas.csv")
library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:Hmisc':
##
## describe## The following objects are masked from 'package:ggplot2':
##
## %+%, alphadescribe(transreas)## vars n mean sd median trimmed mad min max range skew
## Student 1 425 213.00 122.83 213 213.00 157.16 1 425 424 0.00
## Grade 2 425 4.00 1.43 4 4.01 1.48 2 6 4 0.00
## task_01 3 425 0.94 0.24 1 1.00 0.00 0 1 1 -3.74
## task_02 4 425 0.81 0.39 1 0.89 0.00 0 1 1 -1.57
## task_03 5 425 0.88 0.32 1 0.98 0.00 0 1 1 -2.40
## task_04 6 425 0.78 0.41 1 0.85 0.00 0 1 1 -1.37
## task_05 7 425 0.80 0.40 1 0.88 0.00 0 1 1 -1.51
## task_06 8 425 0.97 0.16 1 1.00 0.00 0 1 1 -5.95
## task_07 9 425 0.84 0.36 1 0.93 0.00 0 1 1 -1.90
## task_08 10 425 0.97 0.18 1 1.00 0.00 0 1 1 -5.22
## task_09 11 425 0.30 0.46 0 0.25 0.00 0 1 1 0.86
## task_10 12 425 0.52 0.50 1 0.52 0.00 0 1 1 -0.08
## kurtosis se
## Student -1.21 5.96
## Grade -1.33 0.07
## task_01 11.99 0.01
## task_02 0.47 0.02
## task_03 3.77 0.02
## task_04 -0.12 0.02
## task_05 0.29 0.02
## task_06 33.49 0.01
## task_07 1.60 0.02
## task_08 25.26 0.01
## task_09 -1.26 0.02
## task_10 -2.00 0.02Above is a summary of our
data <- transreas[,c(-1,-2)]
describe(data)## vars n mean sd median trimmed mad min max range skew kurtosis se
## task_01 1 425 0.94 0.24 1 1.00 0 0 1 1 -3.74 11.99 0.01
## task_02 2 425 0.81 0.39 1 0.89 0 0 1 1 -1.57 0.47 0.02
## task_03 3 425 0.88 0.32 1 0.98 0 0 1 1 -2.40 3.77 0.02
## task_04 4 425 0.78 0.41 1 0.85 0 0 1 1 -1.37 -0.12 0.02
## task_05 5 425 0.80 0.40 1 0.88 0 0 1 1 -1.51 0.29 0.02
## task_06 6 425 0.97 0.16 1 1.00 0 0 1 1 -5.95 33.49 0.01
## task_07 7 425 0.84 0.36 1 0.93 0 0 1 1 -1.90 1.60 0.02
## task_08 8 425 0.97 0.18 1 1.00 0 0 1 1 -5.22 25.26 0.01
## task_09 9 425 0.30 0.46 0 0.25 0 0 1 1 0.86 -1.26 0.02
## task_10 10 425 0.52 0.50 1 0.52 0 0 1 1 -0.08 -2.00 0.02model1 <- tam(data) ## ....................................................
## Processing Data 2020-09-28 16:55:47
## * Response Data: 425 Persons and 10 Items
## * Numerical integration with 21 nodes
## * Created Design Matrices ( 2020-09-28 16:55:47 )
## * Calculated Sufficient Statistics ( 2020-09-28 16:55:47 )
## ....................................................
## Iteration 1 2020-09-28 16:55:47
## E Step
## M Step Intercepts |----
## Deviance = 3376.8929
## Maximum item intercept parameter change: 0.299715
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.081181
## ....................................................
## Iteration 2 2020-09-28 16:55:47
## E Step
## M Step Intercepts |---
## Deviance = 3356.6709 | Absolute change: 20.222 | Relative change: 0.00602442
## Maximum item intercept parameter change: 0.042548
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.006668
## ....................................................
## Iteration 3 2020-09-28 16:55:47
## E Step
## M Step Intercepts |---
## Deviance = 3355.3971 | Absolute change: 1.2738 | Relative change: 0.00037963
## Maximum item intercept parameter change: 0.021467
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000424
## ....................................................
## Iteration 4 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3355.0655 | Absolute change: 0.3316 | Relative change: 9.884e-05
## Maximum item intercept parameter change: 0.011539
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.002073
## ....................................................
## Iteration 5 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9737 | Absolute change: 0.0918 | Relative change: 2.736e-05
## Maximum item intercept parameter change: 0.006618
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.002935
## ....................................................
## Iteration 6 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9439 | Absolute change: 0.0298 | Relative change: 8.89e-06
## Maximum item intercept parameter change: 0.004037
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.003046
## ....................................................
## Iteration 7 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9314 | Absolute change: 0.0125 | Relative change: 3.71e-06
## Maximum item intercept parameter change: 0.002638
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.002835
## ....................................................
## Iteration 8 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9248 | Absolute change: 0.0066 | Relative change: 1.98e-06
## Maximum item intercept parameter change: 0.00184
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.002508
## ....................................................
## Iteration 9 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9207 | Absolute change: 0.0041 | Relative change: 1.22e-06
## Maximum item intercept parameter change: 0.001357
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.00216
## ....................................................
## Iteration 10 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.918 | Absolute change: 0.0027 | Relative change: 8.1e-07
## Maximum item intercept parameter change: 0.001042
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.001832
## ....................................................
## Iteration 11 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9161 | Absolute change: 0.0018 | Relative change: 5.5e-07
## Maximum item intercept parameter change: 0.000824
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.00154
## ....................................................
## Iteration 12 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9149 | Absolute change: 0.0013 | Relative change: 3.8e-07
## Maximum item intercept parameter change: 0.000664
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.001288
## ....................................................
## Iteration 13 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.914 | Absolute change: 9e-04 | Relative change: 2.6e-07
## Maximum item intercept parameter change: 0.000541
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.001074
## ....................................................
## Iteration 14 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9134 | Absolute change: 6e-04 | Relative change: 1.8e-07
## Maximum item intercept parameter change: 0.000444
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000894
## ....................................................
## Iteration 15 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.913 | Absolute change: 4e-04 | Relative change: 1.2e-07
## Maximum item intercept parameter change: 0.000366
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000742
## ....................................................
## Iteration 16 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9127 | Absolute change: 3e-04 | Relative change: 8e-08
## Maximum item intercept parameter change: 0.000302
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000616
## ....................................................
## Iteration 17 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9125 | Absolute change: 2e-04 | Relative change: 6e-08
## Maximum item intercept parameter change: 0.00025
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000511
## ....................................................
## Iteration 18 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9124 | Absolute change: 1e-04 | Relative change: 4e-08
## Maximum item intercept parameter change: 0.000207
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000424
## ....................................................
## Iteration 19 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9123 | Absolute change: 1e-04 | Relative change: 3e-08
## Maximum item intercept parameter change: 0.000172
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000352
## ....................................................
## Iteration 20 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9123 | Absolute change: 1e-04 | Relative change: 2e-08
## Maximum item intercept parameter change: 0.000142
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000292
## ....................................................
## Iteration 21 2020-09-28 16:55:47
## E Step
## M Step Intercepts |--
## Deviance = 3354.9122 | Absolute change: 0 | Relative change: 1e-08
## Maximum item intercept parameter change: 0.000118
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000242
## ....................................................
## Iteration 22 2020-09-28 16:55:47
## E Step
## M Step Intercepts |-
## Deviance = 3354.9122 | Absolute change: 0 | Relative change: 1e-08
## Maximum item intercept parameter change: 9.8e-05
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 2e-04
## ....................................................
## Iteration 23 2020-09-28 16:55:47
## E Step
## M Step Intercepts |-
## Deviance = 3354.9122 | Absolute change: 0 | Relative change: 1e-08
## Maximum item intercept parameter change: 8.1e-05
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000166
## ....................................................
## Iteration 24 2020-09-28 16:55:47
## E Step
## M Step Intercepts |-
## Deviance = 3354.9122 | Absolute change: 0 | Relative change: 0
## Maximum item intercept parameter change: 6.7e-05
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000138
## ....................................................
## Iteration 25 2020-09-28 16:55:47
## E Step
## M Step Intercepts |-
## Deviance = 3354.9121 | Absolute change: 0 | Relative change: 0
## Maximum item intercept parameter change: 5.6e-05
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 0.000114
## ....................................................
## Iteration 26 2020-09-28 16:55:47
## E Step
## M Step Intercepts |-
## Deviance = 3354.9121 | Absolute change: 0 | Relative change: 0
## Maximum item intercept parameter change: 4.6e-05
## Maximum item slope parameter change: 0
## Maximum regression parameter change: 0
## Maximum variance parameter change: 9.4e-05
## ....................................................
## Item Parameters
## xsi.index xsi.label est
## 1 1 task_01 -3.1686
## 2 2 task_02 -1.7009
## 3 3 task_03 -2.3678
## 4 4 task_04 -1.5172
## 5 5 task_05 -1.6492
## 6 6 task_06 -4.0699
## 7 7 task_07 -1.9824
## 8 8 task_08 -3.8109
## 9 9 task_09 1.0024
## 10 10 task_10 -0.0932
## ...................................
## Regression Coefficients
## [,1]
## [1,] 0
##
## Variance:
## [,1]
## [1,] 0.9378
##
##
## EAP Reliability:
## [1] 0.503
##
## -----------------------------
## Start: 2020-09-28 16:55:47
## End: 2020-09-28 16:55:47
## Time difference of 0.04288316 secssummary(model1)## ------------------------------------------------------------
## TAM 3.5-19 (2020-05-05 22:45:39)
## R version 4.0.2 (2020-06-22) x86_64, darwin17.0 | nodename=Qingzhous-iMac.local | login=root
##
## Date of Analysis: 2020-09-28 16:55:47
## Time difference of 0.04288316 secs
## Computation time: 0.04288316
##
## Multidimensional Item Response Model in TAM
##
## IRT Model: 1PL
## Call:
## tam.mml(resp = resp)
##
## ------------------------------------------------------------
## Number of iterations = 26
## Numeric integration with 21 integration points
##
## Deviance = 3354.91
## Log likelihood = -1677.46
## Number of persons = 425
## Number of persons used = 425
## Number of items = 10
## Number of estimated parameters = 11
## Item threshold parameters = 10
## Item slope parameters = 0
## Regression parameters = 0
## Variance/covariance parameters = 1
##
## AIC = 3377 | penalty=22 | AIC=-2*LL + 2*p
## AIC3 = 3388 | penalty=33 | AIC3=-2*LL + 3*p
## BIC = 3421 | penalty=66.57 | BIC=-2*LL + log(n)*p
## aBIC = 3386 | penalty=31.56 | aBIC=-2*LL + log((n-2)/24)*p (adjusted BIC)
## CAIC = 3432 | penalty=77.57 | CAIC=-2*LL + [log(n)+1]*p (consistent AIC)
## AICc = 3378 | penalty=22.64 | AICc=-2*LL + 2*p + 2*p*(p+1)/(n-p-1) (bias corrected AIC)
## GHP = 0.39728 | GHP=( -LL + p ) / (#Persons * #Items) (Gilula-Haberman log penalty)
##
## ------------------------------------------------------------
## EAP Reliability
## [1] 0.503
## ------------------------------------------------------------
## Covariances and Variances
## [,1]
## [1,] 0.938
## ------------------------------------------------------------
## Correlations and Standard Deviations (in the diagonal)
## [,1]
## [1,] 0.968
## ------------------------------------------------------------
## Regression Coefficients
## [,1]
## [1,] 0
## ------------------------------------------------------------
## Item Parameters -A*Xsi
## item N M xsi.item AXsi_.Cat1 B.Cat1.Dim1
## 1 task_01 425 0.941 -3.169 -3.169 1
## 2 task_02 425 0.809 -1.701 -1.701 1
## 3 task_03 425 0.885 -2.368 -2.368 1
## 4 task_04 425 0.784 -1.517 -1.517 1
## 5 task_05 425 0.802 -1.649 -1.649 1
## 6 task_06 425 0.974 -4.070 -4.070 1
## 7 task_07 425 0.845 -1.982 -1.982 1
## 8 task_08 425 0.967 -3.811 -3.811 1
## 9 task_09 425 0.301 1.002 1.002 1
## 10 task_10 425 0.520 -0.093 -0.093 1
##
## Item Parameters in IRT parameterization
## item alpha beta
## 1 task_01 1 -3.169
## 2 task_02 1 -1.701
## 3 task_03 1 -2.368
## 4 task_04 1 -1.517
## 5 task_05 1 -1.649
## 6 task_06 1 -4.070
## 7 task_07 1 -1.982
## 8 task_08 1 -3.811
## 9 task_09 1 1.002
## 10 task_10 1 -0.093IRT.WrightMap(model1, show.thr.lab=FALSE)
difficulty = model1$xsi
head(difficulty,10)## xsi se.xsi
## task_01 -3.16864785 0.2133573
## task_02 -1.70089350 0.1321453
## task_03 -2.36782604 0.1600094
## task_04 -1.51715819 0.1265292
## task_05 -1.64916897 0.1304829
## task_06 -4.06989050 0.3113083
## task_07 -1.98241949 0.1423829
## task_08 -3.81092249 0.2780165
## task_09 1.00240165 0.1145384
## task_10 -0.09324812 0.1061752mean(difficulty$xsi)## [1] -1.935777sd(difficulty$xsi)## [1] 1.567723mean(difficulty$se.xsi)## [1] 0.1714946sd(difficulty$se.xsi)## [1] 0.07179407hist(difficulty$xsi,breaks = 10)
i_fit = tam.fit(model1)## Item fit calculation based on 40 simulations
## |**********|
## |----------|head(i_fit,10)## $itemfit
## parameter Outfit Outfit_t Outfit_p Outfit_pholm Infit
## 1 task_01 0.8661123 -0.81413242 0.4155690953 1.000000000 0.9309860
## 2 task_02 1.1527227 1.91455026 0.0555498830 0.355202301 1.0894633
## 3 task_03 0.8005011 -1.95364525 0.0507431858 0.355202301 0.9100646
## 4 task_04 1.2560647 3.46477479 0.0005306757 0.005306757 1.1210663
## 5 task_05 1.0363349 0.47977820 0.6313851172 1.000000000 1.0262831
## 6 task_06 0.3921922 -2.85637333 0.0042851095 0.038565985 0.8909956
## 7 task_07 0.8712103 -1.51850026 0.1288883350 0.644441675 0.9445009
## 8 task_08 0.5619545 -2.16906103 0.0300780508 0.240624406 0.9100683
## 9 task_09 1.0659736 1.22759402 0.2195994085 0.878397634 1.0104951
## 10 task_10 0.9969621 -0.08435675 0.9327727920 1.000000000 1.0000688
## Infit_t Infit_p Infit_pholm
## 1 -0.366815506 0.71375664 1.0000000
## 2 1.162402789 0.24507189 1.0000000
## 3 -0.820153038 0.41212887 1.0000000
## 4 1.720665512 0.08531154 0.8531154
## 5 0.370140517 0.71127779 1.0000000
## 6 -0.325128706 0.74508366 1.0000000
## 7 -0.619285978 0.53572798 1.0000000
## 8 -0.312139485 0.75493452 1.0000000
## 9 0.204911640 0.83764116 1.0000000
## 10 0.003772792 0.99698975 1.0000000
##
## $time
## [1] "2020-09-28 16:55:48 CDT" "2020-09-28 16:55:48 CDT"
##
## $CALL
## tam.fit(tamobj = model1)mean(i_fit[["itemfit"]][["Outfit"]])## [1] 0.9000028sd(i_fit[["itemfit"]][["Outfit"]])## [1] 0.2646187mean(i_fit[["itemfit"]][["Infit"]])## [1] 0.9833992sd(i_fit[["itemfit"]][["Infit"]])## [1] 0.07930738ability = tam.wle(model1)## Iteration in WLE/MLE estimation 1 | Maximal change 2.783
## Iteration in WLE/MLE estimation 2 | Maximal change 0.8352
## Iteration in WLE/MLE estimation 3 | Maximal change 0.0976
## Iteration in WLE/MLE estimation 4 | Maximal change 0.0058
## Iteration in WLE/MLE estimation 5 | Maximal change 4e-04
## Iteration in WLE/MLE estimation 6 | Maximal change 0
## ----
## WLE Reliability= 0.308mean(ability$theta)## [1] -0.05565378sd(ability$theta)## [1] 1.281021mean(ability$error)## [1] 1.022701sd(ability$error)## [1] 0.2990593hist(ability$theta)
p_fit = tam.personfit(model1)
head(p_fit)## outfitPerson outfitPerson_t infitPerson infitPerson_t
## 1 0.23116561 -0.6747306 0.3612124 -1.3926697
## 2 1.06501332 0.3075433 1.1169890 0.4450260
## 3 4.21519230 2.8424540 2.0818362 2.5767465
## 4 0.04344653 0.8893776 0.1568586 -0.5789409
## 5 0.54777995 -0.4591824 0.6968196 -0.6503183
## 6 0.16335767 -0.1230185 0.3844617 -0.9719057describe(p_fit)## vars n mean sd median trimmed mad min max range skew
## outfitPerson 1 425 0.61 0.57 0.46 0.53 0.44 0.04 4.22 4.17 1.79
## outfitPerson_t 2 425 0.06 0.65 -0.07 0.02 0.89 -0.96 2.84 3.80 0.54
## infitPerson 3 425 0.77 0.50 0.64 0.73 0.42 0.16 2.20 2.04 0.74
## infitPerson_t 4 425 -0.30 0.92 -0.58 -0.38 0.91 -1.50 2.58 4.08 0.74
## kurtosis se
## outfitPerson 5.03 0.03
## outfitPerson_t -0.14 0.03
## infitPerson -0.37 0.02
## infitPerson_t -0.30 0.04mean(p_fit$outfitPerson)## [1] 0.6100661sd(p_fit$outfitPerson)## [1] 0.5672632mean(p_fit$infitPerson)## [1] 0.7734645sd(p_fit$infitPerson)## [1] 0.4990325i_table = data.frame()
i_table = setNames(data.frame(matrix(ncol=8,nrow=10)),c("TaskID", "PropCorrect","Delta","SE","Outfit","Outfit_P","Infit","Infit_P"))
TaskCorect = apply(data,2,sum)
PropCorrect = percent(TaskCorect/425)
i_table$TaskID = 1:10
i_table$PropCorrect = PropCorrect
i_table$Delta = difficulty$xsi
i_table$SE = difficulty$se.xsi
i_table$Outfit = i_fit[["itemfit"]][["Outfit"]]
i_table$Outfit_P = i_fit[["itemfit"]][["Outfit_p"]]
i_table$Infit = i_fit[["itemfit"]][["Infit"]]
i_table$Infit_P = i_fit[["itemfit"]][["Infit_p"]]
i_table = i_table[order(-PropCorrect),]View(i_table)p_table = data.frame()
p_table = setNames(data.frame(matrix(ncol=8,nrow=425)),c("TestTakerID", "P_PropCorrect","Theta","SE","Outfit","Outfit_t","Infit","Infit_t"))
p_score = rowSums(data,na.rm = FALSE)
P_PropCorrect = percent(p_score/10)
p_table$TestTakerID = 1:425
p_table$P_PropCorrect = P_PropCorrect
p_table$Theta = ability$theta
p_table$SE = ability$error
p_table$Outfit = p_fit$outfitPerson
p_table$Outfit_t = p_fit$outfitPerson_t
p_table$Infit = p_fit$infitPerson
p_table$Infit_t = p_fit$infitPerson_tView(p_table)subset(p_table, p_table$P_PropCorrect==1)## TestTakerID P_PropCorrect Theta SE Outfit Outfit_t Infit
## 4 4 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 7 7 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 13 13 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 14 14 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 67 67 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 73 73 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 79 79 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 80 80 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 88 88 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 117 117 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 131 131 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 138 138 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 144 144 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 147 147 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 154 154 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 164 164 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 166 166 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 169 169 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 187 187 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 188 188 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 190 190 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 191 191 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 194 194 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 197 197 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 199 199 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 202 202 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 204 204 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 214 214 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 224 224 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 232 232 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 233 233 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 251 251 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 254 254 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 266 266 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 277 277 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 282 282 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 292 292 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 308 308 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 317 317 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 328 328 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 358 358 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 380 380 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 398 398 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 402 402 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 405 405 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 407 407 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 414 414 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 415 415 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 419 419 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 420 420 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## 422 422 100.00% 2.347125 1.762825 0.04344653 0.8893776 0.1568586
## Infit_t
## 4 -0.5789409
## 7 -0.5789409
## 13 -0.5789409
## 14 -0.5789409
## 67 -0.5789409
## 73 -0.5789409
## 79 -0.5789409
## 80 -0.5789409
## 88 -0.5789409
## 117 -0.5789409
## 131 -0.5789409
## 138 -0.5789409
## 144 -0.5789409
## 147 -0.5789409
## 154 -0.5789409
## 164 -0.5789409
## 166 -0.5789409
## 169 -0.5789409
## 187 -0.5789409
## 188 -0.5789409
## 190 -0.5789409
## 191 -0.5789409
## 194 -0.5789409
## 197 -0.5789409
## 199 -0.5789409
## 202 -0.5789409
## 204 -0.5789409
## 214 -0.5789409
## 224 -0.5789409
## 232 -0.5789409
## 233 -0.5789409
## 251 -0.5789409
## 254 -0.5789409
## 266 -0.5789409
## 277 -0.5789409
## 282 -0.5789409
## 292 -0.5789409
## 308 -0.5789409
## 317 -0.5789409
## 328 -0.5789409
## 358 -0.5789409
## 380 -0.5789409
## 398 -0.5789409
## 402 -0.5789409
## 405 -0.5789409
## 407 -0.5789409
## 414 -0.5789409
## 415 -0.5789409
## 419 -0.5789409
## 420 -0.5789409
## 422 -0.5789409subset(p_table, p_table$P_PropCorrect==0.1)## TestTakerID P_PropCorrect Theta SE Outfit Outfit_t Infit
## 148 148 10.00% -4.528473 1.029438 1.859619 1.012249 1.284249
## Infit_t
## 148 0.6294794subset(p_table, p_table$Outfit>1.4)## TestTakerID P_PropCorrect Theta SE Outfit Outfit_t Infit
## 3 3 40.00% -2.5351672 0.7587624 4.215192 2.8424540 2.081836
## 21 21 40.00% -2.5351672 0.7587624 1.471466 0.8180884 1.696670
## 46 46 60.00% -1.4663564 0.7688974 2.339554 1.8161267 1.892995
## 60 60 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 74 74 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 89 89 80.00% -0.1632616 0.9172265 1.849681 0.9850767 2.037078
## 90 90 80.00% -0.1632616 0.9172265 1.849681 0.9850767 2.037078
## 94 94 80.00% -0.1632616 0.9172265 3.149713 1.6295306 2.200562
## 102 102 60.00% -1.4663564 0.7688974 1.640166 1.0861630 1.718998
## 103 103 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 107 107 70.00% -0.8772971 0.8190927 2.067709 1.3305471 1.647437
## 116 116 90.00% 0.7916991 1.1106587 1.802020 0.9675062 1.602602
## 148 148 10.00% -4.5284734 1.0294383 1.859619 1.0122491 1.284249
## 198 198 40.00% -2.5351672 0.7587624 1.778577 1.1322964 1.891745
## 209 209 60.00% -1.4663564 0.7688974 1.886952 1.3639660 1.482815
## 212 212 50.00% -2.0031302 0.7508743 1.403611 0.8007844 1.243289
## 245 245 80.00% -0.1632616 0.9172265 2.259629 1.2141350 1.152576
## 247 247 60.00% -1.4663564 0.7688974 1.496786 0.9117326 1.100455
## 264 264 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 280 280 70.00% -0.8772971 0.8190927 1.852449 1.1505426 1.581965
## 299 299 70.00% -0.8772971 0.8190927 1.583239 0.9045869 1.819347
## 340 340 80.00% -0.1632616 0.9172265 2.331384 1.2513049 1.189245
## 354 354 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 360 360 30.00% -3.0995710 0.7930736 1.422658 0.7114111 1.533438
## 361 361 50.00% -2.0031302 0.7508743 2.589188 2.0763806 1.973656
## 362 362 50.00% -2.0031302 0.7508743 2.643382 2.1242545 1.319733
## 370 370 60.00% -1.4663564 0.7688974 1.878329 1.3546815 1.217796
## 372 372 70.00% -0.8772971 0.8190927 1.834491 1.1349078 1.996782
## 384 384 90.00% 0.7916991 1.1106587 2.290376 1.1322744 1.647064
## 385 385 30.00% -3.0995710 0.7930736 1.980269 1.1303443 2.138197
## 392 392 40.00% -2.5351672 0.7587624 1.471466 0.8180884 1.696670
## 399 399 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 411 411 30.00% -3.0995710 0.7930736 2.437419 1.4180786 1.064266
## 417 417 90.00% 0.7916991 1.1106587 2.292715 1.1330042 1.701384
## 423 423 20.00% -3.7363473 0.8659962 2.048229 1.0534841 1.808065
## Infit_t
## 3 2.5767465
## 21 1.8275475
## 46 2.2068066
## 60 0.9288260
## 74 0.9288260
## 89 1.6477059
## 90 1.6477059
## 94 1.8285632
## 102 1.8612377
## 103 0.9288260
## 107 1.4091239
## 116 0.9758182
## 148 0.6294794
## 198 2.2197350
## 209 1.3526161
## 212 0.8289327
## 245 0.4480047
## 247 0.3995857
## 264 0.9288260
## 280 1.2982300
## 299 1.6869571
## 340 0.5082289
## 354 0.9288260
## 360 1.3263844
## 361 2.5537001
## 362 1.0369827
## 370 0.7128075
## 372 1.9559238
## 384 1.0229552
## 385 2.3839739
## 392 1.8275475
## 399 0.9288260
## 411 0.2919580
## 417 1.0794071
## 423 1.6196842subset(p_table, p_table$Outfit<0.6)## TestTakerID P_PropCorrect Theta SE Outfit Outfit_t
## 1 1 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 4 4 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 5 5 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 6 6 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 7 7 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 9 9 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 10 10 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 11 11 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 13 13 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 14 14 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 15 15 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 16 16 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 22 22 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 23 23 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 24 24 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 25 25 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 26 26 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 27 27 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 28 28 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 29 29 60.00% -1.4663564 0.7688974 0.51083922 -0.7849302
## 30 30 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 31 31 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 32 32 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 33 33 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 34 34 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 35 35 60.00% -1.4663564 0.7688974 0.47366530 -0.8828442
## 36 36 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 37 37 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 41 41 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 42 42 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 44 44 40.00% -2.5351672 0.7587624 0.56395886 -0.5000307
## 47 47 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 50 50 50.00% -2.0031302 0.7508743 0.45714814 -0.9568200
## 52 52 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 53 53 60.00% -1.4663564 0.7688974 0.48423744 -0.8544915
## 56 56 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 57 57 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 59 59 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 61 61 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 62 62 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 66 66 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 67 67 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 69 69 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 71 71 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 72 72 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 73 73 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 79 79 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 80 80 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 81 81 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 82 82 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 83 83 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 84 84 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 86 86 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 87 87 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 88 88 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 92 92 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 93 93 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 95 95 60.00% -1.4663564 0.7688974 0.51083922 -0.7849302
## 96 96 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 97 97 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 99 99 60.00% -1.4663564 0.7688974 0.47366530 -0.8828442
## 100 100 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 101 101 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 109 109 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 111 111 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 112 112 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 113 113 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 114 114 60.00% -1.4663564 0.7688974 0.57135586 -0.6352286
## 117 117 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 118 118 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 120 120 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 121 121 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 122 122 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 124 124 60.00% -1.4663564 0.7688974 0.54475408 -0.6996636
## 127 127 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 128 128 50.00% -2.0031302 0.7508743 0.45714814 -0.9568200
## 129 129 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 130 130 60.00% -1.4663564 0.7688974 0.48423744 -0.8544915
## 131 131 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 132 132 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 133 133 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 135 135 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 137 137 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 138 138 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 139 139 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 141 141 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 144 144 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 146 146 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 147 147 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 149 149 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 152 152 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 154 154 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 156 156 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 157 157 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 161 161 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 163 163 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 164 164 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 165 165 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 166 166 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 167 167 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 169 169 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 170 170 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 171 171 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 173 173 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 174 174 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 175 175 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 176 176 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 178 178 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 181 181 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 183 183 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 184 184 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 185 185 60.00% -1.4663564 0.7688974 0.54475408 -0.6996636
## 186 186 60.00% -1.4663564 0.7688974 0.48423744 -0.8544915
## 187 187 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 188 188 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 189 189 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 190 190 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 191 191 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 192 192 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 193 193 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 194 194 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 195 195 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 197 197 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 199 199 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 201 201 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 202 202 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 203 203 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 204 204 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 205 205 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 206 206 60.00% -1.4663564 0.7688974 0.57135586 -0.6352286
## 213 213 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 214 214 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 215 215 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 216 216 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 219 219 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 220 220 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 221 221 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 223 223 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 224 224 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 227 227 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 229 229 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 230 230 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 232 232 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 233 233 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 234 234 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 235 235 60.00% -1.4663564 0.7688974 0.54475408 -0.6996636
## 236 236 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 237 237 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 238 238 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 239 239 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 240 240 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 241 241 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 243 243 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 244 244 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 246 246 50.00% -2.0031302 0.7508743 0.45714814 -0.9568200
## 248 248 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 249 249 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 250 250 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 251 251 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 252 252 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 254 254 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 256 256 60.00% -1.4663564 0.7688974 0.48423744 -0.8544915
## 259 259 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 260 260 60.00% -1.4663564 0.7688974 0.48423744 -0.8544915
## 261 261 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 262 262 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 265 265 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 266 266 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 267 267 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 268 268 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 270 270 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 272 272 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 273 273 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 277 277 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 278 278 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 279 279 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 281 281 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 282 282 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 283 283 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 284 284 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 286 286 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 287 287 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 289 289 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 290 290 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 292 292 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 294 294 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 295 295 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 296 296 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 298 298 20.00% -3.7363473 0.8659962 0.30371981 -0.3044809
## 300 300 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 304 304 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 305 305 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 307 307 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 308 308 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 309 309 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 310 310 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 312 312 60.00% -1.4663564 0.7688974 0.57135586 -0.6352286
## 314 314 60.00% -1.4663564 0.7688974 0.48423744 -0.8544915
## 316 316 50.00% -2.0031302 0.7508743 0.52528548 -0.7730871
## 317 317 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 318 318 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 319 319 50.00% -2.0031302 0.7508743 0.45714814 -0.9568200
## 321 321 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 322 322 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 324 324 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 325 325 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 327 327 70.00% -0.8772971 0.8190927 0.46292055 -0.6344895
## 328 328 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 329 329 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 330 330 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 331 331 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 334 334 60.00% -1.4663564 0.7688974 0.48423744 -0.8544915
## 335 335 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 336 336 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 337 337 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 344 344 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 346 346 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 349 349 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 352 352 60.00% -1.4663564 0.7688974 0.57135586 -0.6352286
## 358 358 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 363 363 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 366 366 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 368 368 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 369 369 50.00% -2.0031302 0.7508743 0.51437753 -0.8013862
## 371 371 70.00% -0.8772971 0.8190927 0.44910415 -0.6650168
## 373 373 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 374 374 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 375 375 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 376 376 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 378 378 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 380 380 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 382 382 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 386 386 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 388 388 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 394 394 70.00% -0.8772971 0.8190927 0.54777995 -0.4591824
## 396 396 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 398 398 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 400 400 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 401 401 90.00% 0.7916991 1.1106587 0.40682274 0.1931481
## 402 402 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 403 403 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 404 404 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 405 405 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 406 406 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 407 407 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 408 408 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 409 409 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 414 414 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 415 415 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 416 416 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 419 419 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 420 420 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 421 421 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 422 422 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 424 424 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 425 425 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## Infit Infit_t
## 1 0.3612124 -1.3926697
## 4 0.1568586 -0.5789409
## 5 0.6968196 -0.6503183
## 6 0.3844617 -0.9719057
## 7 0.1568586 -0.5789409
## 9 0.3844617 -0.9719057
## 10 0.5673364 -1.0609277
## 11 0.3844617 -0.9719057
## 13 0.1568586 -0.5789409
## 14 0.1568586 -0.5789409
## 15 0.3612124 -1.3926697
## 16 0.3612124 -1.3926697
## 22 0.3612124 -1.3926697
## 23 0.3612124 -1.3926697
## 24 0.3844617 -0.9719057
## 25 0.3844617 -0.9719057
## 26 0.3612124 -1.3926697
## 27 0.6968196 -0.6503183
## 28 0.6065230 -0.9305953
## 29 0.6817199 -0.9457080
## 30 0.3844617 -0.9719057
## 31 0.6065230 -0.9305953
## 32 0.6213820 -0.8826450
## 33 0.3612124 -1.3926697
## 34 0.3612124 -1.3926697
## 35 0.6277196 -1.1566257
## 36 0.6968196 -0.6503183
## 37 0.3612124 -1.3926697
## 41 0.3844617 -0.9719057
## 42 0.6065230 -0.9305953
## 44 0.6861057 -0.9380218
## 47 0.6213820 -0.8826450
## 50 0.5789023 -1.5004777
## 52 0.6968196 -0.6503183
## 53 0.6428436 -1.0963720
## 56 0.3612124 -1.3926697
## 57 0.5673364 -1.0609277
## 59 0.3612124 -1.3926697
## 61 0.5673364 -1.0609277
## 62 0.6213820 -0.8826450
## 66 0.3612124 -1.3926697
## 67 0.1568586 -0.5789409
## 69 0.3612124 -1.3926697
## 71 1.0267316 0.2663208
## 72 0.6968196 -0.6503183
## 73 0.1568586 -0.5789409
## 79 0.1568586 -0.5789409
## 80 0.1568586 -0.5789409
## 81 0.5673364 -1.0609277
## 82 0.3612124 -1.3926697
## 83 0.3844617 -0.9719057
## 84 1.0267316 0.2663208
## 86 0.3844617 -0.9719057
## 87 0.3844617 -0.9719057
## 88 0.1568586 -0.5789409
## 92 0.6213820 -0.8826450
## 93 0.6213820 -0.8826450
## 95 0.6817199 -0.9457080
## 96 0.5673364 -1.0609277
## 97 0.3612124 -1.3926697
## 99 0.6277196 -1.1566257
## 100 0.6065230 -0.9305953
## 101 1.0267316 0.2663208
## 109 0.3844617 -0.9719057
## 111 0.6968196 -0.6503183
## 112 1.0267316 0.2663208
## 113 0.6213820 -0.8826450
## 114 0.7619586 -0.6519725
## 117 0.1568586 -0.5789409
## 118 1.0267316 0.2663208
## 120 0.3844617 -0.9719057
## 121 0.3612124 -1.3926697
## 122 0.6968196 -0.6503183
## 124 0.7230823 -0.7915723
## 127 0.6213820 -0.8826450
## 128 0.5789023 -1.5004777
## 129 0.3612124 -1.3926697
## 130 0.6428436 -1.0963720
## 131 0.1568586 -0.5789409
## 132 1.0267316 0.2663208
## 133 0.3612124 -1.3926697
## 135 1.0267316 0.2663208
## 137 1.0267316 0.2663208
## 138 0.1568586 -0.5789409
## 139 0.3844617 -0.9719057
## 141 1.0267316 0.2663208
## 144 0.1568586 -0.5789409
## 146 0.3844617 -0.9719057
## 147 0.1568586 -0.5789409
## 149 0.3844617 -0.9719057
## 152 0.6065230 -0.9305953
## 154 0.1568586 -0.5789409
## 156 0.3844617 -0.9719057
## 157 0.3612124 -1.3926697
## 161 0.3612124 -1.3926697
## 163 0.3844617 -0.9719057
## 164 0.1568586 -0.5789409
## 165 0.3612124 -1.3926697
## 166 0.1568586 -0.5789409
## 167 0.3844617 -0.9719057
## 169 0.1568586 -0.5789409
## 170 0.6065230 -0.9305953
## 171 0.3612124 -1.3926697
## 173 0.3612124 -1.3926697
## 174 0.3844617 -0.9719057
## 175 0.3612124 -1.3926697
## 176 0.3844617 -0.9719057
## 178 0.3844617 -0.9719057
## 181 0.3844617 -0.9719057
## 183 0.3844617 -0.9719057
## 184 1.0267316 0.2663208
## 185 0.7230823 -0.7915723
## 186 0.6428436 -1.0963720
## 187 0.1568586 -0.5789409
## 188 0.1568586 -0.5789409
## 189 0.6065230 -0.9305953
## 190 0.1568586 -0.5789409
## 191 0.1568586 -0.5789409
## 192 0.3612124 -1.3926697
## 193 0.3844617 -0.9719057
## 194 0.1568586 -0.5789409
## 195 0.3612124 -1.3926697
## 197 0.1568586 -0.5789409
## 199 0.1568586 -0.5789409
## 201 0.3612124 -1.3926697
## 202 0.1568586 -0.5789409
## 203 0.3612124 -1.3926697
## 204 0.1568586 -0.5789409
## 205 0.3844617 -0.9719057
## 206 0.7619586 -0.6519725
## 213 0.3612124 -1.3926697
## 214 0.1568586 -0.5789409
## 215 0.3844617 -0.9719057
## 216 0.6968196 -0.6503183
## 219 0.3844617 -0.9719057
## 220 0.3844617 -0.9719057
## 221 0.3844617 -0.9719057
## 223 0.3844617 -0.9719057
## 224 0.1568586 -0.5789409
## 227 0.3612124 -1.3926697
## 229 0.3612124 -1.3926697
## 230 0.3844617 -0.9719057
## 232 0.1568586 -0.5789409
## 233 0.1568586 -0.5789409
## 234 0.6065230 -0.9305953
## 235 0.7230823 -0.7915723
## 236 0.6065230 -0.9305953
## 237 0.3844617 -0.9719057
## 238 0.3844617 -0.9719057
## 239 0.3844617 -0.9719057
## 240 0.3844617 -0.9719057
## 241 0.6065230 -0.9305953
## 243 0.3844617 -0.9719057
## 244 0.3844617 -0.9719057
## 246 0.5789023 -1.5004777
## 248 0.3612124 -1.3926697
## 249 0.6065230 -0.9305953
## 250 0.6213820 -0.8826450
## 251 0.1568586 -0.5789409
## 252 0.5673364 -1.0609277
## 254 0.1568586 -0.5789409
## 256 0.6428436 -1.0963720
## 259 0.6213820 -0.8826450
## 260 0.6428436 -1.0963720
## 261 0.6968196 -0.6503183
## 262 0.3612124 -1.3926697
## 265 0.6065230 -0.9305953
## 266 0.1568586 -0.5789409
## 267 0.3612124 -1.3926697
## 268 0.3612124 -1.3926697
## 270 0.3844617 -0.9719057
## 272 0.3612124 -1.3926697
## 273 0.3844617 -0.9719057
## 277 0.1568586 -0.5789409
## 278 0.3844617 -0.9719057
## 279 0.3844617 -0.9719057
## 281 0.3612124 -1.3926697
## 282 0.1568586 -0.5789409
## 283 0.3844617 -0.9719057
## 284 0.6213820 -0.8826450
## 286 0.3612124 -1.3926697
## 287 0.3844617 -0.9719057
## 289 0.3612124 -1.3926697
## 290 0.3844617 -0.9719057
## 292 0.1568586 -0.5789409
## 294 0.3844617 -0.9719057
## 295 0.3844617 -0.9719057
## 296 0.3844617 -0.9719057
## 298 0.4766450 -1.3270601
## 300 0.3844617 -0.9719057
## 304 0.5673364 -1.0609277
## 305 0.3844617 -0.9719057
## 307 0.5673364 -1.0609277
## 308 0.1568586 -0.5789409
## 309 0.3612124 -1.3926697
## 310 0.6213820 -0.8826450
## 312 0.7619586 -0.6519725
## 314 0.6428436 -1.0963720
## 316 0.6718195 -1.0921402
## 317 0.1568586 -0.5789409
## 318 0.3844617 -0.9719057
## 319 0.5789023 -1.5004777
## 321 0.3612124 -1.3926697
## 322 0.3844617 -0.9719057
## 324 0.6213820 -0.8826450
## 325 1.0267316 0.2663208
## 327 0.6213820 -0.8826450
## 328 0.1568586 -0.5789409
## 329 0.3612124 -1.3926697
## 330 0.3612124 -1.3926697
## 331 1.0267316 0.2663208
## 334 0.6428436 -1.0963720
## 335 0.3612124 -1.3926697
## 336 0.3844617 -0.9719057
## 337 0.3612124 -1.3926697
## 344 1.0267316 0.2663208
## 346 0.3612124 -1.3926697
## 349 0.3844617 -0.9719057
## 352 0.7619586 -0.6519725
## 358 0.1568586 -0.5789409
## 363 0.3612124 -1.3926697
## 366 0.3612124 -1.3926697
## 368 0.3844617 -0.9719057
## 369 0.6576242 -1.1519759
## 371 0.6065230 -0.9305953
## 373 0.3612124 -1.3926697
## 374 0.6968196 -0.6503183
## 375 0.3612124 -1.3926697
## 376 0.3612124 -1.3926697
## 378 0.3844617 -0.9719057
## 380 0.1568586 -0.5789409
## 382 0.3612124 -1.3926697
## 386 0.3844617 -0.9719057
## 388 0.3612124 -1.3926697
## 394 0.6968196 -0.6503183
## 396 0.3612124 -1.3926697
## 398 0.1568586 -0.5789409
## 400 0.3844617 -0.9719057
## 401 1.0267316 0.2663208
## 402 0.1568586 -0.5789409
## 403 0.3844617 -0.9719057
## 404 0.3612124 -1.3926697
## 405 0.1568586 -0.5789409
## 406 0.3844617 -0.9719057
## 407 0.1568586 -0.5789409
## 408 0.3844617 -0.9719057
## 409 0.3612124 -1.3926697
## 414 0.1568586 -0.5789409
## 415 0.1568586 -0.5789409
## 416 0.3612124 -1.3926697
## 419 0.1568586 -0.5789409
## 420 0.1568586 -0.5789409
## 421 0.3844617 -0.9719057
## 422 0.1568586 -0.5789409
## 424 0.3844617 -0.9719057
## 425 0.3612124 -1.3926697subset(p_table, p_table$Infit>1.4)## TestTakerID P_PropCorrect Theta SE Outfit Outfit_t Infit
## 3 3 40.00% -2.5351672 0.7587624 4.215192 2.8424540 2.081836
## 12 12 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 20 20 90.00% 0.7916991 1.1106587 1.345403 0.7837426 1.549879
## 21 21 40.00% -2.5351672 0.7587624 1.471466 0.8180884 1.696670
## 43 43 80.00% -0.1632616 0.9172265 1.340105 0.6469852 1.871783
## 46 46 60.00% -1.4663564 0.7688974 2.339554 1.8161267 1.892995
## 60 60 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 63 63 80.00% -0.1632616 0.9172265 1.340105 0.6469852 1.871783
## 68 68 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 74 74 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 76 76 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 77 77 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 78 78 80.00% -0.1632616 0.9172265 1.121249 0.4749542 1.409198
## 89 89 80.00% -0.1632616 0.9172265 1.849681 0.9850767 2.037078
## 90 90 80.00% -0.1632616 0.9172265 1.849681 0.9850767 2.037078
## 94 94 80.00% -0.1632616 0.9172265 3.149713 1.6295306 2.200562
## 102 102 60.00% -1.4663564 0.7688974 1.640166 1.0861630 1.718998
## 103 103 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 107 107 70.00% -0.8772971 0.8190927 2.067709 1.3305471 1.647437
## 116 116 90.00% 0.7916991 1.1106587 1.802020 0.9675062 1.602602
## 119 119 80.00% -0.1632616 0.9172265 1.340105 0.6469852 1.871783
## 123 123 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 140 140 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 142 142 80.00% -0.1632616 0.9172265 1.340105 0.6469852 1.871783
## 143 143 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 150 150 90.00% 0.7916991 1.1106587 1.345403 0.7837426 1.549879
## 155 155 80.00% -0.1632616 0.9172265 1.340105 0.6469852 1.871783
## 158 158 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 198 198 40.00% -2.5351672 0.7587624 1.778577 1.1322964 1.891745
## 208 208 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 209 209 60.00% -1.4663564 0.7688974 1.886952 1.3639660 1.482815
## 211 211 80.00% -0.1632616 0.9172265 1.121249 0.4749542 1.409198
## 217 217 90.00% 0.7916991 1.1106587 1.345403 0.7837426 1.549879
## 222 222 90.00% 0.7916991 1.1106587 1.345403 0.7837426 1.549879
## 228 228 80.00% -0.1632616 0.9172265 1.315506 0.6286307 1.858899
## 257 257 80.00% -0.1632616 0.9172265 1.340105 0.6469852 1.871783
## 264 264 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 280 280 70.00% -0.8772971 0.8190927 1.852449 1.1505426 1.581965
## 291 291 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 299 299 70.00% -0.8772971 0.8190927 1.583239 0.9045869 1.819347
## 306 306 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 333 333 80.00% -0.1632616 0.9172265 1.340105 0.6469852 1.871783
## 343 343 80.00% -0.1632616 0.9172265 1.121249 0.4749542 1.409198
## 347 347 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 354 354 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 356 356 80.00% -0.1632616 0.9172265 1.397948 0.6892760 1.906609
## 360 360 30.00% -3.0995710 0.7930736 1.422658 0.7114111 1.533438
## 361 361 50.00% -2.0031302 0.7508743 2.589188 2.0763806 1.973656
## 372 372 70.00% -0.8772971 0.8190927 1.834491 1.1349078 1.996782
## 381 381 70.00% -0.8772971 0.8190927 1.357836 0.6761808 1.436849
## 384 384 90.00% 0.7916991 1.1106587 2.290376 1.1322744 1.647064
## 385 385 30.00% -3.0995710 0.7930736 1.980269 1.1303443 2.138197
## 387 387 80.00% -0.1632616 0.9172265 1.340105 0.6469852 1.871783
## 392 392 40.00% -2.5351672 0.7587624 1.471466 0.8180884 1.696670
## 397 397 90.00% 0.7916991 1.1106587 1.202165 0.7175924 1.524509
## 399 399 90.00% 0.7916991 1.1106587 1.406800 0.8106603 1.559081
## 417 417 90.00% 0.7916991 1.1106587 2.292715 1.1330042 1.701384
## 418 418 80.00% -0.1632616 0.9172265 1.397948 0.6892760 1.906609
## 423 423 20.00% -3.7363473 0.8659962 2.048229 1.0534841 1.808065
## Infit_t
## 3 2.5767465
## 12 0.8908693
## 20 0.9187783
## 21 1.8275475
## 43 1.4547206
## 46 2.2068066
## 60 0.9288260
## 63 1.4547206
## 68 0.8908693
## 74 0.9288260
## 76 0.8908693
## 77 0.8908693
## 78 0.8457137
## 89 1.6477059
## 90 1.6477059
## 94 1.8285632
## 102 1.8612377
## 103 0.9288260
## 107 1.4091239
## 116 0.9758182
## 119 1.4547206
## 123 0.8908693
## 140 0.8908693
## 142 1.4547206
## 143 0.8908693
## 150 0.9187783
## 155 1.4547206
## 158 0.8908693
## 198 2.2197350
## 208 0.8908693
## 209 1.3526161
## 211 0.8457137
## 217 0.9187783
## 222 0.9187783
## 228 1.4392082
## 257 1.4547206
## 264 0.9288260
## 280 1.2982300
## 291 0.8908693
## 299 1.6869571
## 306 0.8908693
## 333 1.4547206
## 343 0.8457137
## 347 0.8908693
## 354 0.9288260
## 356 1.4962982
## 360 1.3263844
## 361 2.5537001
## 372 1.9559238
## 381 1.0410444
## 384 1.0229552
## 385 2.3839739
## 387 1.4547206
## 392 1.8275475
## 397 0.8908693
## 399 0.9288260
## 417 1.0794071
## 418 1.4962982
## 423 1.6196842subset(p_table, p_table$Infit<0.6)## TestTakerID P_PropCorrect Theta SE Outfit Outfit_t
## 1 1 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 4 4 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 6 6 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 7 7 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 9 9 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 10 10 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 11 11 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 13 13 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 14 14 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 15 15 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 16 16 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 22 22 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 23 23 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 24 24 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 25 25 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 26 26 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 30 30 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 33 33 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 34 34 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 37 37 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 41 41 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 50 50 50.00% -2.0031302 0.7508743 0.45714814 -0.9568200
## 56 56 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 57 57 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 59 59 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 61 61 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 66 66 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 67 67 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 69 69 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 73 73 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 79 79 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 80 80 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 81 81 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 82 82 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 83 83 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 86 86 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 87 87 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 88 88 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 96 96 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 97 97 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 109 109 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 117 117 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 120 120 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 121 121 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 128 128 50.00% -2.0031302 0.7508743 0.45714814 -0.9568200
## 129 129 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 131 131 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 133 133 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 138 138 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 139 139 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 144 144 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 146 146 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 147 147 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 149 149 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 154 154 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 156 156 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 157 157 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 161 161 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 163 163 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 164 164 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 165 165 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 166 166 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 167 167 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 169 169 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 171 171 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 173 173 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 174 174 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 175 175 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 176 176 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 178 178 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 181 181 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 183 183 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 187 187 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 188 188 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 190 190 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 191 191 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 192 192 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 193 193 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 194 194 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 195 195 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 197 197 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 199 199 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 201 201 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 202 202 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 203 203 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 204 204 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 205 205 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 213 213 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 214 214 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 215 215 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 219 219 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 220 220 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 221 221 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 223 223 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 224 224 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 227 227 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 229 229 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 230 230 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 232 232 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 233 233 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 237 237 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 238 238 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 239 239 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 240 240 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 243 243 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 244 244 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 246 246 50.00% -2.0031302 0.7508743 0.45714814 -0.9568200
## 248 248 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 251 251 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 252 252 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 254 254 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 262 262 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 266 266 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 267 267 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 268 268 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 270 270 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 272 272 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 273 273 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 277 277 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 278 278 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 279 279 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 281 281 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 282 282 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 283 283 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 286 286 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 287 287 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 289 289 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 290 290 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 292 292 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 294 294 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 295 295 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 296 296 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 298 298 20.00% -3.7363473 0.8659962 0.30371981 -0.3044809
## 300 300 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 304 304 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 305 305 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 307 307 70.00% -0.8772971 0.8190927 0.41582276 -0.7412154
## 308 308 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 309 309 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 317 317 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 318 318 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 319 319 50.00% -2.0031302 0.7508743 0.45714814 -0.9568200
## 321 321 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 322 322 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 328 328 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 329 329 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 330 330 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 335 335 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 336 336 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 337 337 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 346 346 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 349 349 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 358 358 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 363 363 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 366 366 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 368 368 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 373 373 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 375 375 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 376 376 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 378 378 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 380 380 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 382 382 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 386 386 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 388 388 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 396 396 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 398 398 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 400 400 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 402 402 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 403 403 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 404 404 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 405 405 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 406 406 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 407 407 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 408 408 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 409 409 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 414 414 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 415 415 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 416 416 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## 419 419 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 420 420 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 421 421 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 422 422 100.00% 2.3471248 1.7628252 0.04344653 0.8893776
## 424 424 90.00% 0.7916991 1.1106587 0.16335767 -0.1230185
## 425 425 80.00% -0.1632616 0.9172265 0.23116561 -0.6747306
## Infit Infit_t
## 1 0.3612124 -1.3926697
## 4 0.1568586 -0.5789409
## 6 0.3844617 -0.9719057
## 7 0.1568586 -0.5789409
## 9 0.3844617 -0.9719057
## 10 0.5673364 -1.0609277
## 11 0.3844617 -0.9719057
## 13 0.1568586 -0.5789409
## 14 0.1568586 -0.5789409
## 15 0.3612124 -1.3926697
## 16 0.3612124 -1.3926697
## 22 0.3612124 -1.3926697
## 23 0.3612124 -1.3926697
## 24 0.3844617 -0.9719057
## 25 0.3844617 -0.9719057
## 26 0.3612124 -1.3926697
## 30 0.3844617 -0.9719057
## 33 0.3612124 -1.3926697
## 34 0.3612124 -1.3926697
## 37 0.3612124 -1.3926697
## 41 0.3844617 -0.9719057
## 50 0.5789023 -1.5004777
## 56 0.3612124 -1.3926697
## 57 0.5673364 -1.0609277
## 59 0.3612124 -1.3926697
## 61 0.5673364 -1.0609277
## 66 0.3612124 -1.3926697
## 67 0.1568586 -0.5789409
## 69 0.3612124 -1.3926697
## 73 0.1568586 -0.5789409
## 79 0.1568586 -0.5789409
## 80 0.1568586 -0.5789409
## 81 0.5673364 -1.0609277
## 82 0.3612124 -1.3926697
## 83 0.3844617 -0.9719057
## 86 0.3844617 -0.9719057
## 87 0.3844617 -0.9719057
## 88 0.1568586 -0.5789409
## 96 0.5673364 -1.0609277
## 97 0.3612124 -1.3926697
## 109 0.3844617 -0.9719057
## 117 0.1568586 -0.5789409
## 120 0.3844617 -0.9719057
## 121 0.3612124 -1.3926697
## 128 0.5789023 -1.5004777
## 129 0.3612124 -1.3926697
## 131 0.1568586 -0.5789409
## 133 0.3612124 -1.3926697
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