Person-Fit statistics

Source: R/Pfit.R

Compute several person fit statistic for the 1-PL, 2-PL, 3-PL, 4-PL and PCM.

Pfit(respm, pp, fitindices, SE = FALSE)

# S3 method for gpcm
Pfit(respm, pp, fitindices = c("infit", "outfit"), SE = FALSE)

Arguments

respm

numeric response matrix
pp

object of the class fourpl with estimated person parameter
fitindices

character vector of desired person fit statistics c("lz","lzstar","infit","outfit")
SE

logical: if true standard errors are computed using jackknife method

Value

list of person-fits for each person-fit statistic

  • the list of person-fits contains the calculated person-fit (like lz, lzstar) and also additional information like p-value or standard error if desired.

  • the additional information is provided after the short form of the personfit

  • lz (lz)

  • lzstar (lzs)

  • infit the mean-square statistic (in)

  • outfit the mean-square statistic (ou)

  • _unst: unstandardised

  • _se: standard error

  • _t: t-value

  • _chisq: $chi^2$-value

  • _df: defrees of freedom

  • _pv: p-value

Details

Please note that currently only the likelihood based LZ-Index (Drasgow, Levine, and Williams, 1985) and LZ*-Index (Snijders, 2001) are implemented. Also the INFIT-OUTIFT (Wright and Masters, 1982, 1990) statistic as well as the polytomouse version of INFIT-OUTFIT are supported. Other person fit statistics will be added soon.

The calculation of the person fit statistics requires the numeric response-matrix as well as an object of the fourpl-class. So first you should estimate the person parameter and afterwards calculate the person fit statistics. You could also use our PPass-function to estimate the person parameter and calculate the desired person fit simultaneously. It is possible to calculate several person fit statistics at once, you only have to specify them in a vector.

For the Partial Credit model we currently support the infit-outfit statistic. Please submit also the numeric response-matrix as well as the estimated person parameter with an gpcm-class.

References

Armstrong, R. D., Stoumbos, Z. G., Kung, M. T. & Shi, M. (2007). On the performance of the lz person-fit statistic. Practical Assessment, Research & Evaluation, 12(16). Chicago

De La Torre, J., & Deng, W. (2008). Improving Person-Fit Assessment by Correcting the Ability Estimate and Its Reference Distribution. Journal of Educational Measurement, 45(2), 159-177.

Drasgow, F., Levine, M. V. & Williams, E. A. (1985) Appropriateness measurement with polychotomous item response models and standardized indices. British Journal of Mathematical and Statistical Psychology, 38(1), 67--86.

Efron, B., & Stein, C. (1981). The jackknife estimate of variance. The Annals of Statistics, 9(3), 586-596.

Karabatsos, G. (2003) Comparing the Aberrant Response Detection Performance of Thirty-Six Person-Fit Statistics. Applied Measurement In Education, 16(4), 277--298.

Magis, D., Raiche, G. & Beland, S. (2012) A didactic presentation of Snijders's l[sub]z[/sub] index of person fit with emphasis on response model selection and ability estimation. Journal of Educational and Behavioral Statistics, 37(1), 57--81.

Meijer, R. R. & Sijtsma, K. (2001) Methodology review: Evaluating person fit. Applied Psychological Measurement, 25(2), 107--135.

Molenaar, I. W. & Hoijtink, H. (1990) The many null distributions of person fit indices. Psychometrika, 55(1), 75--106.

Mousavi, A. & Cui, Y. Evaluate the performance of and of person fit: A simulation study.

Reise, S. P. (1990). A comparison of item-and person-fit methods of assessing model-data fit in IRT. Applied Psychological Measurement, 14(2), 127-137.

Snijders, T. B. (2001) Asymptotic null distribution of person fit statistics with estimated person parameter. Psychometrika, 66(3), 331--342.

Wright, B. D. & Masters, G. N. (1990). Computation of OUTFIT and INFIT Statistics. Rasch Measurement Transactions, 3:4, 84-85.

Wright, B. D., & Masters, G. N. (1982). Rating Scale Analysis. Rasch Measurement. MESA Press, 5835 S. Kimbark Avenue, Chicago, IL 60637.

See also

PPall, PP_4pl, PPass

Author

Jan Steinfeld

Examples

################# Pfit ###################################################


### data creation ##########

set.seed(1337)


# intercepts
diffpar <- seq(-3,3,length=15)
# slope parameters
sl     <- round(runif(15,0.5,1.5),2)
la     <- round(runif(15,0,0.25),2)
ua     <- round(runif(15,0.8,1),2)

# response matrix
awm <- matrix(sample(0:1,100*15,replace=TRUE),ncol=15)

# ------------------------------------------------------------------------
## 1PL model ##### 
# ------------------------------------------------------------------------
# MLE
res1plmle <- PP_4pl(respm = awm,thres = diffpar,type = "mle")

#> Estimating:  1pl model ... 
#> type = mle 
#> Estimation finished!
# WLE
res1plwle <- PP_4pl(respm = awm,thres = diffpar,type = "wle")

#> Estimating:  1pl model ... 
#> type = wle 
#> Estimation finished!
# MAP estimation
res1plmap <- PP_4pl(respm = awm,thres = diffpar,type = "map")

#> Warning: all mu's are set to 0! 
#> Warning: all sigma2's are set to 1! 
#> Estimating:  1pl model ... 
#> type = map 
#> Estimation finished!
# ------------------------------------------------------------------------
## LZ*-Index ##### 
Pfit(respm=awm,pp=res1plwle,fitindices="lzstar")

#> $lzstar
#>           lzstar
#>   [1,] -3.086058
#>   [2,] -5.790820
#>   [3,] -5.953959
#>   [4,] -7.131986
#>   [5,] -2.759479
#>   [6,] -6.831113
#>   [7,] -2.669941
#>   [8,] -2.877999
#>   [9,] -8.081369
#>  [10,] -0.542024
#>  [11,] -2.253823
#>  [12,] -2.343201
#>  [13,] -3.192791
#>  [14,] -6.591151
#>  [15,] -4.539780
#>  [16,] -3.351074
#>  [17,] -4.750527
#>  [18,] -3.424715
#>  [19,] -2.343201
#>  [20,] -1.325978
#>  [21,] -5.198679
#>  [22,] -3.294116
#>  [23,] -5.104368
#>  [24,] -4.334410
#>  [25,] -4.755188
#>  [26,] -3.918292
#>  [27,] -4.679573
#>  [28,] -6.591151
#>  [29,] -4.042381
#>  [30,] -4.089951
#>  [31,] -7.015946
#>  [32,] -4.126351
#>  [33,] -4.539780
#>  [34,] -3.086058
#>  [35,] -3.617586
#>  [36,] -5.253004
#>  [37,] -3.868206
#>  [38,] -2.669941
#>  [39,] -7.630418
#>  [40,] -5.316766
#>  [41,] -0.589354
#>  [42,] -5.998879
#>  [43,] -5.316766
#>  [44,] -2.253823
#>  [45,] -0.029786
#>  [46,] -1.211402
#>  [47,] -5.790820
#>  [48,] -0.763770
#>  [49,] -6.414996
#>  [50,] -4.777522
#>  [51,] -4.755188
#>  [52,] -1.629834
#>  [53,] -3.502175
#>  [54,] -3.086058
#>  [55,] -4.228982
#>  [56,] -2.461882
#>  [57,] -4.679573
#>  [58,] -2.981224
#>  [59,] -6.441711
#>  [60,] -1.837706
#>  [61,] -4.064298
#>  [62,] -4.089951
#>  [63,] -5.104368
#>  [64,] -2.253823
#>  [65,] -6.203970
#>  [66,] -2.980393
#>  [67,] -1.207261
#>  [68,] -1.918406
#>  [69,] -2.537733
#>  [70,] -3.588815
#>  [71,] -1.421589
#>  [72,] -3.086058
#>  [73,] -4.228982
#>  [74,] -5.582761
#>  [75,] -3.351074
#>  [76,] -3.502175
#>  [77,] -4.334410
#>  [78,] -7.440741
#>  [79,] -5.642169
#>  [80,] -7.653139
#>  [81,] -5.104368
#>  [82,] -3.202970
#>  [83,] -4.542468
#>  [84,] -5.316766
#>  [85,] -5.490746
#>  [86,] -2.767996
#>  [87,] -3.710234
#>  [88,] -3.710234
#>  [89,] -0.735919
#>  [90,] -6.414996
#>  [91,] -4.679573
#>  [92,] -2.400109
#>  [93,] -5.790820
#>  [94,] -2.555598
#>  [95,] -4.334410
#>  [96,] -3.710234
#>  [97,] -4.958586
#>  [98,] -5.166644
#>  [99,] -3.918292
#> [100,] -6.803549
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
Pfit(respm=awm,pp=res1plmle,fitindices="lzstar")

#> $lzstar
#>           lzstar
#>   [1,] -3.086326
#>   [2,] -5.791328
#>   [3,] -5.959100
#>   [4,] -7.256911
#>   [5,] -2.767271
#>   [6,] -6.831714
#>   [7,] -2.670172
#>   [8,] -2.878249
#>   [9,] -8.104604
#>  [10,] -0.543383
#>  [11,] -2.254017
#>  [12,] -2.345187
#>  [13,] -3.195520
#>  [14,] -6.596850
#>  [15,] -4.572940
#>  [16,] -3.375397
#>  [17,] -4.750943
#>  [18,] -3.434438
#>  [19,] -2.345187
#>  [20,] -1.347989
#>  [21,] -5.213549
#>  [22,] -3.294403
#>  [23,] -5.108768
#>  [24,] -4.334788
#>  [25,] -4.768771
#>  [26,] -3.918634
#>  [27,] -4.683602
#>  [28,] -6.596850
#>  [29,] -4.045852
#>  [30,] -4.101604
#>  [31,] -7.022016
#>  [32,] -4.126711
#>  [33,] -4.572940
#>  [34,] -3.086326
#>  [35,] -3.620686
#>  [36,] -5.291466
#>  [37,] -3.879216
#>  [38,] -2.670172
#>  [39,] -7.686552
#>  [40,] -5.321351
#>  [41,] -0.589401
#>  [42,] -5.999405
#>  [43,] -5.321351
#>  [44,] -2.254017
#>  [45,]       NaN
#>  [46,] -1.219819
#>  [47,] -5.791328
#>  [48,] -0.765772
#>  [49,] -6.415559
#>  [50,] -4.812449
#>  [51,] -4.768771
#>  [52,] -1.697211
#>  [53,] -3.502480
#>  [54,] -3.086326
#>  [55,] -4.302450
#>  [56,] -2.462094
#>  [57,] -4.683602
#>  [58,] -2.989660
#>  [59,] -6.489009
#>  [60,] -1.837863
#>  [61,] -4.093923
#>  [62,] -4.101604
#>  [63,] -5.108768
#>  [64,] -2.254017
#>  [65,] -6.249500
#>  [66,] -2.982937
#>  [67,] -1.210549
#>  [68,] -1.920021
#>  [69,] -2.544882
#>  [70,] -3.614905
#>  [71,] -1.421709
#>  [72,] -3.086326
#>  [73,] -4.302450
#>  [74,] -5.583251
#>  [75,] -3.375397
#>  [76,] -3.502480
#>  [77,] -4.334788
#>  [78,] -7.447182
#>  [79,] -5.658326
#>  [80,] -7.659765
#>  [81,] -5.108768
#>  [82,] -3.212049
#>  [83,] -4.542865
#>  [84,] -5.321351
#>  [85,] -5.530975
#>  [86,] -2.770354
#>  [87,] -3.710557
#>  [88,] -3.710557
#>  [89,] -0.740802
#>  [90,] -6.415559
#>  [91,] -4.683602
#>  [92,] -2.417362
#>  [93,] -5.791328
#>  [94,] -2.557770
#>  [95,] -4.334788
#>  [96,] -3.710557
#>  [97,] -4.959020
#>  [98,] -5.167097
#>  [99,] -3.918634
#> [100,] -6.809433
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
Pfit(respm=awm,pp=res1plmap,fitindices="lzstar")

#> $lzstar
#>           lzstar
#>   [1,] -3.084090
#>   [2,] -5.785297
#>   [3,] -5.901261
#>   [4,] -6.407385
#>   [5,] -2.717059
#>   [6,] -6.824222
#>   [7,] -2.668520
#>   [8,] -2.876305
#>   [9,] -7.855528
#>  [10,] -0.576030
#>  [11,] -2.252950
#>  [12,] -2.333898
#>  [13,] -3.173277
#>  [14,] -6.530796
#>  [15,] -4.321656
#>  [16,] -3.217364
#>  [17,] -4.746371
#>  [18,] -3.359368
#>  [19,] -2.333898
#>  [20,] -1.333638
#>  [21,] -5.072191
#>  [22,] -3.291875
#>  [23,] -5.061881
#>  [24,] -4.330801
#>  [25,] -4.643985
#>  [26,] -3.915231
#>  [27,] -4.642192
#>  [28,] -6.530796
#>  [29,] -4.012657
#>  [30,] -4.001676
#>  [31,] -6.950485
#>  [32,] -4.123016
#>  [33,] -4.321656
#>  [34,] -3.084090
#>  [35,] -3.592967
#>  [36,] -4.984232
#>  [37,] -3.787573
#>  [38,] -2.668520
#>  [39,] -7.192817
#>  [40,] -5.271726
#>  [41,] -0.590669
#>  [42,] -5.993082
#>  [43,] -5.271726
#>  [44,] -2.252950
#>  [45,] -0.549952
#>  [46,] -1.229637
#>  [47,] -5.785297
#>  [48,] -0.790133
#>  [49,] -6.408652
#>  [50,] -4.542515
#>  [51,] -4.643985
#>  [52,] -1.557042
#>  [53,] -3.499660
#>  [54,] -3.084090
#>  [55,] -3.870511
#>  [56,] -2.460735
#>  [57,] -4.642192
#>  [58,] -2.931162
#>  [59,] -6.088525
#>  [60,] -1.837380
#>  [61,] -3.879939
#>  [62,] -4.001676
#>  [63,] -5.061881
#>  [64,] -2.252950
#>  [65,] -5.867666
#>  [66,] -2.963433
#>  [67,] -1.218339
#>  [68,] -1.914208
#>  [69,] -2.502956
#>  [70,] -3.438222
#>  [71,] -1.421810
#>  [72,] -3.084090
#>  [73,] -3.870511
#>  [74,] -5.577511
#>  [75,] -3.217364
#>  [76,] -3.499660
#>  [77,] -4.330801
#>  [78,] -7.370175
#>  [79,] -5.500396
#>  [80,] -7.580020
#>  [81,] -5.061881
#>  [82,] -3.145265
#>  [83,] -4.538586
#>  [84,] -5.271726
#>  [85,] -5.205091
#>  [86,] -2.753588
#>  [87,] -3.707446
#>  [88,] -3.707446
#>  [89,] -0.787920
#>  [90,] -6.408652
#>  [91,] -4.642192
#>  [92,] -2.333930
#>  [93,] -5.785297
#>  [94,] -2.543743
#>  [95,] -4.330801
#>  [96,] -3.707446
#>  [97,] -4.954156
#>  [98,] -5.161941
#>  [99,] -3.915231
#> [100,] -6.740640
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
## LZ*-Index combined with Infit-Outfit ##### 
Pfit(respm=awm,pp=res1plwle,fitindices=c("lzstar","infit","outfit"))

#> $lzstar
#>           lzstar
#>   [1,] -3.086058
#>   [2,] -5.790820
#>   [3,] -5.953959
#>   [4,] -7.131986
#>   [5,] -2.759479
#>   [6,] -6.831113
#>   [7,] -2.669941
#>   [8,] -2.877999
#>   [9,] -8.081369
#>  [10,] -0.542024
#>  [11,] -2.253823
#>  [12,] -2.343201
#>  [13,] -3.192791
#>  [14,] -6.591151
#>  [15,] -4.539780
#>  [16,] -3.351074
#>  [17,] -4.750527
#>  [18,] -3.424715
#>  [19,] -2.343201
#>  [20,] -1.325978
#>  [21,] -5.198679
#>  [22,] -3.294116
#>  [23,] -5.104368
#>  [24,] -4.334410
#>  [25,] -4.755188
#>  [26,] -3.918292
#>  [27,] -4.679573
#>  [28,] -6.591151
#>  [29,] -4.042381
#>  [30,] -4.089951
#>  [31,] -7.015946
#>  [32,] -4.126351
#>  [33,] -4.539780
#>  [34,] -3.086058
#>  [35,] -3.617586
#>  [36,] -5.253004
#>  [37,] -3.868206
#>  [38,] -2.669941
#>  [39,] -7.630418
#>  [40,] -5.316766
#>  [41,] -0.589354
#>  [42,] -5.998879
#>  [43,] -5.316766
#>  [44,] -2.253823
#>  [45,] -0.029786
#>  [46,] -1.211402
#>  [47,] -5.790820
#>  [48,] -0.763770
#>  [49,] -6.414996
#>  [50,] -4.777522
#>  [51,] -4.755188
#>  [52,] -1.629834
#>  [53,] -3.502175
#>  [54,] -3.086058
#>  [55,] -4.228982
#>  [56,] -2.461882
#>  [57,] -4.679573
#>  [58,] -2.981224
#>  [59,] -6.441711
#>  [60,] -1.837706
#>  [61,] -4.064298
#>  [62,] -4.089951
#>  [63,] -5.104368
#>  [64,] -2.253823
#>  [65,] -6.203970
#>  [66,] -2.980393
#>  [67,] -1.207261
#>  [68,] -1.918406
#>  [69,] -2.537733
#>  [70,] -3.588815
#>  [71,] -1.421589
#>  [72,] -3.086058
#>  [73,] -4.228982
#>  [74,] -5.582761
#>  [75,] -3.351074
#>  [76,] -3.502175
#>  [77,] -4.334410
#>  [78,] -7.440741
#>  [79,] -5.642169
#>  [80,] -7.653139
#>  [81,] -5.104368
#>  [82,] -3.202970
#>  [83,] -4.542468
#>  [84,] -5.316766
#>  [85,] -5.490746
#>  [86,] -2.767996
#>  [87,] -3.710234
#>  [88,] -3.710234
#>  [89,] -0.735919
#>  [90,] -6.414996
#>  [91,] -4.679573
#>  [92,] -2.400109
#>  [93,] -5.790820
#>  [94,] -2.555598
#>  [95,] -4.334410
#>  [96,] -3.710234
#>  [97,] -4.958586
#>  [98,] -5.166644
#>  [99,] -3.918292
#> [100,] -6.803549
#> 
#> $infit
#>           infit      in_t in_chisq in_df in_pv
#>   [1,] 2.020659  2.305126   47.064    14 0.000
#>   [2,] 2.966794  3.732302   70.659    14 0.000
#>   [3,] 2.886533  3.635684   80.964    14 0.000
#>   [4,] 2.506919  2.974178  227.247    14 0.000
#>   [5,] 1.761801  1.850323   60.601    14 0.000
#>   [6,] 3.295854  4.156107   85.261    14 0.000
#>   [7,] 2.017459  2.299606   35.447    14 0.001
#>   [8,] 1.887782  2.070857   45.947    14 0.000
#>   [9,] 3.174749  4.031736  167.034    14 0.000
#>  [10,] 1.208592  0.662944   16.421    14 0.288
#>  [11,] 1.861658  2.023515   30.353    14 0.007
#>  [12,] 1.901466  2.101764   29.745    14 0.008
#>  [13,] 2.288035  2.755571   32.436    14 0.003
#>  [14,] 3.059913  3.867534  102.519    14 0.000
#>  [15,] 2.449373  3.004670   59.661    14 0.000
#>  [16,] 2.128161  2.493259   46.873    14 0.000
#>  [17,] 2.474620  3.035443   70.990    14 0.000
#>  [18,] 2.104515  2.464248   61.720    14 0.000
#>  [19,] 1.782467  1.882789   42.533    14 0.000
#>  [20,] 1.463658  1.201692   18.876    14 0.170
#>  [21,] 2.677261  3.355794   70.887    14 0.000
#>  [22,] 2.213871  2.628103   39.953    14 0.000
#>  [23,] 2.597235  3.227214   88.204    14 0.000
#>  [24,] 2.327296  2.809024   67.607    14 0.000
#>  [25,] 2.358202  2.876889   83.651    14 0.000
#>  [26,] 2.212375  2.625676   63.196    14 0.000
#>  [27,] 2.584669  3.208799   62.591    14 0.000
#>  [28,] 3.273153  4.140974   79.194    14 0.000
#>  [29,] 2.375792  2.893543   56.285    14 0.000
#>  [30,] 2.390856  2.927816   49.220    14 0.000
#>  [31,] 3.275458  4.143864   98.688    14 0.000
#>  [32,] 2.385012  2.898833   55.368    14 0.000
#>  [33,] 2.515469  3.104269   67.902    14 0.000
#>  [34,] 2.131184  2.492278   38.576    14 0.000
#>  [35,] 2.130238  2.498328   57.193    14 0.000
#>  [36,] 2.554012  3.161545  103.305    14 0.000
#>  [37,] 2.472168  3.052646   38.807    14 0.000
#>  [38,] 1.931290  2.148741   39.368    14 0.000
#>  [39,] 2.983273  3.763656  193.050    14 0.000
#>  [40,] 2.725644  3.412079   73.191    14 0.000
#>  [41,] 1.222556  0.693588   19.161    14 0.159
#>  [42,] 2.942133  3.699302   80.483    14 0.000
#>  [43,] 2.648552  3.301809   85.964    14 0.000
#>  [44,] 1.820512  1.948046   31.715    14 0.004
#>  [45,] 0.091543 -0.758691    0.458    14 1.000
#>  [46,] 1.488959  1.296680   18.408    14 0.189
#>  [47,] 2.903129  3.646731   76.867    14 0.000
#>  [48,] 1.316595  0.919670   16.939    14 0.259
#>  [49,] 3.195533  4.030013   79.418    14 0.000
#>  [50,] 2.243042  2.681728  106.092    14 0.000
#>  [51,] 2.283534  2.758644  119.986    14 0.000
#>  [52,] 1.277631  0.729392   41.305    14 0.000
#>  [53,] 2.147637  2.519581   53.909    14 0.000
#>  [54,] 2.048700  2.353241   43.681    14 0.000
#>  [55,] 2.047067  2.269113  189.850    14 0.000
#>  [56,] 2.003421  2.275325   29.723    14 0.008
#>  [57,] 2.512691  3.102148   64.890    14 0.000
#>  [58,] 1.998524  2.281999   46.135    14 0.000
#>  [59,] 2.502710  3.085179  187.024    14 0.000
#>  [60,] 1.638480  1.599779   29.639    14 0.009
#>  [61,] 1.992678  2.262062  106.235    14 0.000
#>  [62,] 2.129671  2.506601   95.764    14 0.000
#>  [63,] 2.683172  3.351589   77.052    14 0.000
#>  [64,] 1.889188  2.073391   29.601    14 0.009
#>  [65,] 2.329974  2.820109  208.084    14 0.000
#>  [66,] 2.053357  2.368375   40.832    14 0.000
#>  [67,] 1.316019  0.918338   35.482    14 0.001
#>  [68,] 1.748405  1.818322   26.832    14 0.020
#>  [69,] 1.796120  1.915211   44.287    14 0.000
#>  [70,] 2.201970  2.615103   49.276    14 0.000
#>  [71,] 1.509358  1.336771   28.305    14 0.013
#>  [72,] 1.943360  2.170139   54.943    14 0.000
#>  [73,] 2.216822  2.540629   71.943    14 0.000
#>  [74,] 2.964230  3.728880   65.451    14 0.000
#>  [75,] 1.931905  2.154942   84.846    14 0.000
#>  [76,] 2.351182  2.846370   38.539    14 0.000
#>  [77,] 2.467699  3.025010   56.744    14 0.000
#>  [78,] 3.369545  4.260685  109.328    14 0.000
#>  [79,] 2.772456  3.491251  101.142    14 0.000
#>  [80,] 3.670377  4.620216   86.591    14 0.000
#>  [81,] 2.838659  3.570036   56.833    14 0.000
#>  [82,] 2.038866  2.352107   44.237    14 0.000
#>  [83,] 2.499000  3.072038   66.273    14 0.000
#>  [84,] 2.618063  3.257608   92.149    14 0.000
#>  [85,] 2.533005  3.130399  109.572    14 0.000
#>  [86,] 1.868779  2.042548   48.700    14 0.000
#>  [87,] 2.170816  2.557810   59.654    14 0.000
#>  [88,] 2.322359  2.801273   46.836    14 0.000
#>  [89,] 1.159862  0.540649   24.781    14 0.037
#>  [90,] 3.062416  3.858556   87.250    14 0.000
#>  [91,] 2.738066  3.429653   50.978    14 0.000
#>  [92,] 1.791322  1.898236   34.739    14 0.002
#>  [93,] 2.858379  3.585831   79.335    14 0.000
#>  [94,] 1.966949  2.218385   31.968    14 0.004
#>  [95,] 2.436439  2.977645   62.990    14 0.000
#>  [96,] 2.248116  2.683365   51.775    14 0.000
#>  [97,] 2.603982  3.226958   70.858    14 0.000
#>  [98,] 2.652925  3.297761   74.300    14 0.000
#>  [99,] 2.341664  2.831519   58.196    14 0.000
#> [100,] 3.229711  4.086252   85.631    14 0.000
#> 
#> $outfit
#>           outfit     ou_t ou_chisq ou_df ou_pv
#>   [1,]  3.137599 2.433293   47.064    14 0.000
#>   [2,]  4.710578 3.451529   70.659    14 0.000
#>   [3,]  5.397579 3.423889   80.964    14 0.000
#>   [4,] 15.149777 3.515269  227.247    14 0.000
#>   [5,]  4.040039 2.329902   60.601    14 0.000
#>   [6,]  5.684096 3.970965   85.261    14 0.000
#>   [7,]  2.363110 1.800351   35.447    14 0.001
#>   [8,]  3.063145 2.377323   45.947    14 0.000
#>   [9,] 11.135629 4.533375  167.034    14 0.000
#>  [10,]  1.094765 0.396058   16.421    14 0.288
#>  [11,]  2.023523 1.478466   30.353    14 0.007
#>  [12,]  1.982991 1.319986   29.745    14 0.008
#>  [13,]  2.162386 1.475503   32.436    14 0.003
#>  [14,]  6.834587 4.030767  102.519    14 0.000
#>  [15,]  3.977385 1.945130   59.661    14 0.000
#>  [16,]  3.124851 1.615458   46.873    14 0.000
#>  [17,]  4.732637 3.464056   70.990    14 0.000
#>  [18,]  4.114657 2.363435   61.720    14 0.000
#>  [19,]  2.835529 1.992212   42.533    14 0.000
#>  [20,]  1.258374 0.650388   18.876    14 0.170
#>  [21,]  4.725785 2.623903   70.887    14 0.000
#>  [22,]  2.663502 2.060255   39.953    14 0.000
#>  [23,]  5.880292 3.638613   88.204    14 0.000
#>  [24,]  4.507138 3.334107   67.607    14 0.000
#>  [25,]  5.576707 2.951518   83.651    14 0.000
#>  [26,]  4.213050 3.157947   63.196    14 0.000
#>  [27,]  4.172740 2.814377   62.591    14 0.000
#>  [28,]  5.279577 3.369465   79.194    14 0.000
#>  [29,]  3.752362 2.577754   56.285    14 0.000
#>  [30,]  3.281351 1.962827   49.220    14 0.000
#>  [31,]  6.579206 3.929599   98.688    14 0.000
#>  [32,]  3.691183 2.823989   55.368    14 0.000
#>  [33,]  4.526793 2.133181   67.902    14 0.000
#>  [34,]  2.571743 1.983034   38.576    14 0.000
#>  [35,]  3.812844 2.612851   57.193    14 0.000
#>  [36,]  6.887006 2.801983  103.305    14 0.000
#>  [37,]  2.587115 1.573326   38.807    14 0.000
#>  [38,]  2.624566 2.027708   39.368    14 0.000
#>  [39,] 12.869975 3.989288  193.050    14 0.000
#>  [40,]  4.879368 3.178609   73.191    14 0.000
#>  [41,]  1.277386 0.616257   19.161    14 0.159
#>  [42,]  5.365518 3.808008   80.483    14 0.000
#>  [43,]  5.730959 3.573483   85.964    14 0.000
#>  [44,]  2.114342 1.567875   31.715    14 0.004
#>  [45,]  0.030523 1.520954    0.458    14 1.000
#>  [46,]  1.227211 0.561542   18.408    14 0.189
#>  [47,]  5.124439 3.680352   76.867    14 0.000
#>  [48,]  1.129287 0.432948   16.939    14 0.259
#>  [49,]  5.294548 3.770832   79.418    14 0.000
#>  [50,]  7.072832 2.847656  106.092    14 0.000
#>  [51,]  7.999069 3.731125  119.986    14 0.000
#>  [52,]  2.753675 1.268627   41.305    14 0.000
#>  [53,]  3.593936 2.758332   53.909    14 0.000
#>  [54,]  2.912046 2.260883   43.681    14 0.000
#>  [55,] 12.656652 3.219606  189.850    14 0.000
#>  [56,]  1.981524 1.436212   29.723    14 0.008
#>  [57,]  4.326002 2.896681   64.890    14 0.000
#>  [58,]  3.075644 1.853670   46.135    14 0.000
#>  [59,] 12.468247 3.922928  187.024    14 0.000
#>  [60,]  1.975907 1.430516   29.639    14 0.009
#>  [61,]  7.082345 2.849973  106.235    14 0.000
#>  [62,]  6.384238 3.232861   95.764    14 0.000
#>  [63,]  5.136770 3.302505   77.052    14 0.000
#>  [64,]  1.973376 1.427945   29.601    14 0.009
#>  [65,] 13.872267 4.149038  208.084    14 0.000
#>  [66,]  2.722127 1.911379   40.832    14 0.000
#>  [67,]  2.365457 1.434381   35.482    14 0.001
#>  [68,]  1.788810 1.140684   26.832    14 0.020
#>  [69,]  2.952493 1.785974   44.287    14 0.000
#>  [70,]  3.285079 1.681632   49.276    14 0.000
#>  [71,]  1.887005 1.338888   28.305    14 0.013
#>  [72,]  3.662847 2.804978   54.943    14 0.000
#>  [73,]  4.796168 1.896757   71.943    14 0.000
#>  [74,]  4.363403 3.248998   65.451    14 0.000
#>  [75,]  5.656383 2.476576   84.846    14 0.000
#>  [76,]  2.569281 1.980937   38.539    14 0.000
#>  [77,]  3.782942 2.884892   56.744    14 0.000
#>  [78,]  7.288506 4.204536  109.328    14 0.000
#>  [79,]  6.742809 3.350191  101.142    14 0.000
#>  [80,]  5.772735 3.591816   86.591    14 0.000
#>  [81,]  3.788895 2.598998   56.833    14 0.000
#>  [82,]  2.949129 1.784098   44.237    14 0.000
#>  [83,]  4.418168 3.281643   66.273    14 0.000
#>  [84,]  6.143251 3.750669   92.149    14 0.000
#>  [85,]  7.304815 2.903566  109.572    14 0.000
#>  [86,]  3.246678 2.268389   48.700    14 0.000
#>  [87,]  3.976940 3.010485   59.654    14 0.000
#>  [88,]  3.122385 2.421929   46.836    14 0.000
#>  [89,]  1.652048 0.861875   24.781    14 0.037
#>  [90,]  5.816651 4.036975   87.250    14 0.000
#>  [91,]  3.398520 2.364467   50.978    14 0.000
#>  [92,]  2.315906 1.241284   34.739    14 0.002
#>  [93,]  5.289005 3.767915   79.335    14 0.000
#>  [94,]  2.131182 1.449087   31.968    14 0.004
#>  [95,]  4.199301 3.149514   62.990    14 0.000
#>  [96,]  3.451691 2.660124   51.775    14 0.000
#>  [97,]  4.723895 3.459096   70.858    14 0.000
#>  [98,]  4.953334 3.587299   74.300    14 0.000
#>  [99,]  3.879744 2.948085   58.196    14 0.000
#> [100,]  5.708703 3.563680   85.631    14 0.000
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
# ------------------------------------------------------------------------

##########################################################################

# ------------------------------------------------------------------------
## 2PL model ##### 
# ------------------------------------------------------------------------
# MLE
res2plmle <- PP_4pl(respm = awm,thres = diffpar, slopes = sl,type = "mle")

#> Estimating:  2pl model ... 
#> type = mle 
#> Estimation finished!
# WLE
res2plwle <- PP_4pl(respm = awm,thres = diffpar, slopes = sl,type = "wle")

#> Estimating:  2pl model ... 
#> type = wle 
#> Estimation finished!
# ------------------------------------------------------------------------
## LZ*-Index ##### 
Pfit(respm=awm,pp=res2plwle,fitindices="lzstar")

#> $lzstar
#>           lzstar
#>   [1,] -3.974933
#>   [2,] -6.019367
#>   [3,] -6.667613
#>   [4,] -7.916054
#>   [5,] -2.385695
#>   [6,] -7.008073
#>   [7,] -3.048935
#>   [8,] -2.526643
#>   [9,] -9.799558
#>  [10,] -0.424418
#>  [11,] -2.296407
#>  [12,] -2.669438
#>  [13,] -3.345911
#>  [14,] -7.655669
#>  [15,] -5.121245
#>  [16,] -4.622091
#>  [17,] -5.743787
#>  [18,] -3.147805
#>  [19,] -2.220005
#>  [20,] -1.358712
#>  [21,] -4.386445
#>  [22,] -3.766286
#>  [23,] -6.134711
#>  [24,] -6.142708
#>  [25,] -5.460107
#>  [26,] -5.222215
#>  [27,] -5.125238
#>  [28,] -6.718402
#>  [29,] -5.002245
#>  [30,] -4.526645
#>  [31,] -8.389500
#>  [32,] -5.261207
#>  [33,] -5.301615
#>  [34,] -2.898337
#>  [35,] -4.621964
#>  [36,] -5.473613
#>  [37,] -4.434842
#>  [38,] -2.946523
#>  [39,] -7.429822
#>  [40,] -6.219071
#>  [41,] -0.691651
#>  [42,] -7.255903
#>  [43,] -6.517754
#>  [44,] -2.121095
#>  [45,]  0.104615
#>  [46,] -2.059338
#>  [47,] -6.589660
#>  [48,] -0.892366
#>  [49,] -7.395970
#>  [50,] -4.479684
#>  [51,] -5.423881
#>  [52,] -1.439764
#>  [53,] -4.096481
#>  [54,] -3.252465
#>  [55,] -5.648627
#>  [56,] -3.202158
#>  [57,] -5.266054
#>  [58,] -4.054353
#>  [59,] -6.066516
#>  [60,] -2.029725
#>  [61,] -4.758977
#>  [62,] -4.383625
#>  [63,] -6.221403
#>  [64,] -2.307409
#>  [65,] -7.498190
#>  [66,] -3.423138
#>  [67,] -1.650437
#>  [68,] -2.485756
#>  [69,] -3.623541
#>  [70,] -4.111450
#>  [71,] -2.095327
#>  [72,] -4.547844
#>  [73,] -5.049396
#>  [74,] -6.529758
#>  [75,] -3.735708
#>  [76,] -4.021221
#>  [77,] -5.177085
#>  [78,] -8.539754
#>  [79,] -6.922836
#>  [80,] -8.230902
#>  [81,] -5.667608
#>  [82,] -3.243733
#>  [83,] -5.063878
#>  [84,] -6.555584
#>  [85,] -5.539382
#>  [86,] -3.267342
#>  [87,] -4.623911
#>  [88,] -3.900786
#>  [89,] -1.265263
#>  [90,] -7.185481
#>  [91,] -4.791026
#>  [92,] -2.907868
#>  [93,] -6.289528
#>  [94,] -3.155976
#>  [95,] -5.846669
#>  [96,] -4.505930
#>  [97,] -5.414642
#>  [98,] -6.273773
#>  [99,] -5.126250
#> [100,] -7.054260
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
Pfit(respm=awm,pp=res2plmle,fitindices="lzstar")

#> $lzstar
#>           lzstar
#>   [1,] -3.967574
#>   [2,] -6.022430
#>   [3,] -6.680146
#>   [4,] -7.977989
#>   [5,] -2.400112
#>   [6,] -7.018118
#>   [7,] -3.054756
#>   [8,] -2.524814
#>   [9,] -9.825110
#>  [10,] -0.425902
#>  [11,] -2.294772
#>  [12,] -2.678850
#>  [13,] -3.338710
#>  [14,] -7.669941
#>  [15,] -5.153314
#>  [16,] -4.644879
#>  [17,] -5.731443
#>  [18,] -3.164369
#>  [19,] -2.227131
#>  [20,] -1.369931
#>  [21,] -4.394859
#>  [22,] -3.772711
#>  [23,] -6.119172
#>  [24,] -6.149360
#>  [25,] -5.461427
#>  [26,] -5.228888
#>  [27,] -5.143377
#>  [28,] -6.738029
#>  [29,] -4.993828
#>  [30,] -4.514836
#>  [31,] -8.402919
#>  [32,] -5.246774
#>  [33,] -5.327064
#>  [34,] -2.895277
#>  [35,] -4.614703
#>  [36,] -5.495869
#>  [37,] -4.450102
#>  [38,] -2.954217
#>  [39,] -7.499787
#>  [40,] -6.236290
#>  [41,] -0.692980
#>  [42,] -7.264533
#>  [43,] -6.523645
#>  [44,] -2.122036
#>  [45,]       NaN
#>  [46,] -2.066638
#>  [47,] -6.571694
#>  [48,] -0.896501
#>  [49,] -7.385601
#>  [50,] -4.496727
#>  [51,] -5.445174
#>  [52,] -1.529933
#>  [53,] -4.095259
#>  [54,] -3.258374
#>  [55,] -5.684942
#>  [56,] -3.208004
#>  [57,] -5.277074
#>  [58,] -4.069232
#>  [59,] -6.099754
#>  [60,] -2.033122
#>  [61,] -4.778529
#>  [62,] -4.371492
#>  [63,] -6.224981
#>  [64,] -2.305175
#>  [65,] -7.529510
#>  [66,] -3.433010
#>  [67,] -1.656041
#>  [68,] -2.478732
#>  [69,] -3.639696
#>  [70,] -4.121313
#>  [71,] -2.098434
#>  [72,] -4.556194
#>  [73,] -5.103196
#>  [74,] -6.521623
#>  [75,] -3.743023
#>  [76,] -4.018687
#>  [77,] -5.174004
#>  [78,] -8.560909
#>  [79,] -6.940208
#>  [80,] -8.219471
#>  [81,] -5.678610
#>  [82,] -3.241763
#>  [83,] -5.073638
#>  [84,] -6.540553
#>  [85,] -5.560246
#>  [86,] -3.258200
#>  [87,] -4.616881
#>  [88,] -3.890710
#>  [89,] -1.270418
#>  [90,] -7.185600
#>  [91,] -4.781941
#>  [92,] -2.922645
#>  [93,] -6.290221
#>  [94,] -3.162061
#>  [95,] -5.836846
#>  [96,] -4.500268
#>  [97,] -5.423948
#>  [98,] -6.285953
#>  [99,] -5.135941
#> [100,] -7.044245
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
## LZ*-Index combined with Infit-Outfit ##### 
Pfit(respm=awm,pp=res2plwle,fitindices=c("lzstar","infit","outfit"))

#> $lzstar
#>           lzstar
#>   [1,] -3.974933
#>   [2,] -6.019367
#>   [3,] -6.667613
#>   [4,] -7.916054
#>   [5,] -2.385695
#>   [6,] -7.008073
#>   [7,] -3.048935
#>   [8,] -2.526643
#>   [9,] -9.799558
#>  [10,] -0.424418
#>  [11,] -2.296407
#>  [12,] -2.669438
#>  [13,] -3.345911
#>  [14,] -7.655669
#>  [15,] -5.121245
#>  [16,] -4.622091
#>  [17,] -5.743787
#>  [18,] -3.147805
#>  [19,] -2.220005
#>  [20,] -1.358712
#>  [21,] -4.386445
#>  [22,] -3.766286
#>  [23,] -6.134711
#>  [24,] -6.142708
#>  [25,] -5.460107
#>  [26,] -5.222215
#>  [27,] -5.125238
#>  [28,] -6.718402
#>  [29,] -5.002245
#>  [30,] -4.526645
#>  [31,] -8.389500
#>  [32,] -5.261207
#>  [33,] -5.301615
#>  [34,] -2.898337
#>  [35,] -4.621964
#>  [36,] -5.473613
#>  [37,] -4.434842
#>  [38,] -2.946523
#>  [39,] -7.429822
#>  [40,] -6.219071
#>  [41,] -0.691651
#>  [42,] -7.255903
#>  [43,] -6.517754
#>  [44,] -2.121095
#>  [45,]  0.104615
#>  [46,] -2.059338
#>  [47,] -6.589660
#>  [48,] -0.892366
#>  [49,] -7.395970
#>  [50,] -4.479684
#>  [51,] -5.423881
#>  [52,] -1.439764
#>  [53,] -4.096481
#>  [54,] -3.252465
#>  [55,] -5.648627
#>  [56,] -3.202158
#>  [57,] -5.266054
#>  [58,] -4.054353
#>  [59,] -6.066516
#>  [60,] -2.029725
#>  [61,] -4.758977
#>  [62,] -4.383625
#>  [63,] -6.221403
#>  [64,] -2.307409
#>  [65,] -7.498190
#>  [66,] -3.423138
#>  [67,] -1.650437
#>  [68,] -2.485756
#>  [69,] -3.623541
#>  [70,] -4.111450
#>  [71,] -2.095327
#>  [72,] -4.547844
#>  [73,] -5.049396
#>  [74,] -6.529758
#>  [75,] -3.735708
#>  [76,] -4.021221
#>  [77,] -5.177085
#>  [78,] -8.539754
#>  [79,] -6.922836
#>  [80,] -8.230902
#>  [81,] -5.667608
#>  [82,] -3.243733
#>  [83,] -5.063878
#>  [84,] -6.555584
#>  [85,] -5.539382
#>  [86,] -3.267342
#>  [87,] -4.623911
#>  [88,] -3.900786
#>  [89,] -1.265263
#>  [90,] -7.185481
#>  [91,] -4.791026
#>  [92,] -2.907868
#>  [93,] -6.289528
#>  [94,] -3.155976
#>  [95,] -5.846669
#>  [96,] -4.505930
#>  [97,] -5.414642
#>  [98,] -6.273773
#>  [99,] -5.126250
#> [100,] -7.054260
#> 
#> $infit
#>           infit      in_t in_chisq in_df in_pv
#>   [1,] 2.161824  2.523330   77.569    14 0.000
#>   [2,] 2.958347  3.678557   94.702    14 0.000
#>   [3,] 3.228325  4.232896   80.025    14 0.000
#>   [4,] 2.532425  3.306195  307.789    14 0.000
#>   [5,] 1.682130  1.808835   31.743    14 0.004
#>   [6,] 3.289628  4.161404   96.972    14 0.000
#>   [7,] 1.996639  2.393581   45.918    14 0.000
#>   [8,] 1.809348  1.901760   48.077    14 0.000
#>   [9,] 3.426238  4.915757  260.784    14 0.000
#>  [10,] 1.172871  0.606356   12.789    14 0.543
#>  [11,] 1.934144  2.124673   27.168    14 0.018
#>  [12,] 1.918375  2.430263   28.333    14 0.013
#>  [13,] 2.346949  2.826230   33.227    14 0.003
#>  [14,] 3.415408  4.454160  128.412    14 0.000
#>  [15,] 2.278543  2.944060  154.647    14 0.000
#>  [16,] 2.198370  2.873011  110.951    14 0.000
#>  [17,] 2.785887  3.472482  113.288    14 0.000
#>  [18,] 2.012154  2.501965   33.297    14 0.003
#>  [19,] 1.754981  2.088038   26.609    14 0.022
#>  [20,] 1.442388  1.244094   18.573    14 0.182
#>  [21,] 2.343521  2.936626   60.076    14 0.000
#>  [22,] 2.293075  2.805419   49.308    14 0.000
#>  [23,] 2.739050  3.433565  141.570    14 0.000
#>  [24,] 2.776783  3.456282  118.421    14 0.000
#>  [25,] 2.367442  3.008595  131.415    14 0.000
#>  [26,] 2.342429  2.830839  121.812    14 0.000
#>  [27,] 2.438133  3.425868   76.087    14 0.000
#>  [28,] 3.048461  4.414601   82.141    14 0.000
#>  [29,] 2.508051  3.220898   98.570    14 0.000
#>  [30,] 2.454648  3.089493   57.493    14 0.000
#>  [31,] 3.452995  4.389584  148.866    14 0.000
#>  [32,] 2.563849  3.209421   93.137    14 0.000
#>  [33,] 2.452072  3.329613  117.020    14 0.000
#>  [34,] 2.197940  2.568388   29.955    14 0.008
#>  [35,] 2.238164  2.785376  105.135    14 0.000
#>  [36,] 2.512078  3.119125  150.656    14 0.000
#>  [37,] 2.359270  3.288460   50.691    14 0.000
#>  [38,] 1.957752  2.489207   36.867    14 0.001
#>  [39,] 2.754780  3.216760  359.345    14 0.000
#>  [40,] 2.754245  3.937235   89.869    14 0.000
#>  [41,] 1.212720  0.716123   20.426    14 0.117
#>  [42,] 3.125100  3.927618  135.962    14 0.000
#>  [43,] 3.039566  3.799468  104.021    14 0.000
#>  [44,] 1.801783  1.894241   28.853    14 0.011
#>  [45,] 0.125091 -0.924794    0.625    14 1.000
#>  [46,] 1.598524  1.541206   40.611    14 0.000
#>  [47,] 2.943377  3.778386  138.186    14 0.000
#>  [48,] 1.329933  1.016601   15.389    14 0.352
#>  [49,] 3.183200  3.969831  144.262    14 0.000
#>  [50,] 2.034829  2.357907  146.718    14 0.000
#>  [51,] 2.158717  2.885836  382.971    14 0.000
#>  [52,] 1.289233  0.672552   31.154    14 0.005
#>  [53,] 2.199397  2.568529   80.075    14 0.000
#>  [54,] 1.944865  2.240982   57.783    14 0.000
#>  [55,] 2.070641  2.568284  916.250    14 0.000
#>  [56,] 2.181663  2.662492   37.420    14 0.001
#>  [57,] 2.623873  3.571467   65.397    14 0.000
#>  [58,] 2.046619  2.685372   79.499    14 0.000
#>  [59,] 2.344240  2.801568  282.907    14 0.000
#>  [60,] 1.700888  1.755376   30.773    14 0.006
#>  [61,] 2.094673  2.450593  161.493    14 0.000
#>  [62,] 2.194743  2.645114  117.298    14 0.000
#>  [63,] 2.995470  3.729196   97.540    14 0.000
#>  [64,] 1.951052  2.155047   26.788    14 0.021
#>  [65,] 2.468552  3.424016  492.020    14 0.000
#>  [66,] 1.933398  2.458645   61.008    14 0.000
#>  [67,] 1.290249  0.954852   60.414    14 0.000
#>  [68,] 1.870533  2.062342   37.094    14 0.001
#>  [69,] 1.822533  2.171473  101.824    14 0.000
#>  [70,] 2.190474  2.669192   66.506    14 0.000
#>  [71,] 1.557702  1.448880   68.176    14 0.000
#>  [72,] 2.177927  2.651641  116.800    14 0.000
#>  [73,] 2.075446  2.474736  290.276    14 0.000
#>  [74,] 3.186747  3.970253   93.121    14 0.000
#>  [75,] 1.831201  2.035029  129.121    14 0.000
#>  [76,] 2.638777  3.231233   37.129    14 0.001
#>  [77,] 2.535504  3.082536   90.118    14 0.000
#>  [78,] 3.269969  4.667381  220.373    14 0.000
#>  [79,] 2.791685  3.956925  204.155    14 0.000
#>  [80,] 3.565312  4.694603  127.883    14 0.000
#>  [81,] 2.802444  3.729350   73.870    14 0.000
#>  [82,] 1.971738  2.325344   48.310    14 0.000
#>  [83,] 2.448244  3.169523  121.182    14 0.000
#>  [84,] 2.705059  3.493375  187.616    14 0.000
#>  [85,] 2.456057  3.047175  151.620    14 0.000
#>  [86,] 1.925608  2.166263   72.219    14 0.000
#>  [87,] 2.232255  2.630497  108.218    14 0.000
#>  [88,] 2.324300  2.815041   51.771    14 0.000
#>  [89,] 1.181057  0.638609   46.064    14 0.000
#>  [90,] 3.083952  3.832705  137.448    14 0.000
#>  [91,] 2.711585  3.523430   49.211    14 0.000
#>  [92,] 1.722612  1.927259   64.136    14 0.000
#>  [93,] 2.922083  3.622759  114.277    14 0.000
#>  [94,] 2.139205  2.666054   36.424    14 0.001
#>  [95,] 2.774851  3.439008  113.410    14 0.000
#>  [96,] 2.298047  2.730030   84.390    14 0.000
#>  [97,] 2.709056  3.438123   98.944    14 0.000
#>  [98,] 2.760331  3.663219  139.170    14 0.000
#>  [99,] 2.455431  3.139932  123.095    14 0.000
#> [100,] 3.138580  4.142237  121.834    14 0.000
#> 
#> $outfit
#>           outfit     ou_t ou_chisq ou_df ou_pv
#>   [1,]  5.171274 2.952726   77.569    14 0.000
#>   [2,]  6.313446 3.540528   94.702    14 0.000
#>   [3,]  5.335033 2.999243   80.025    14 0.000
#>   [4,] 20.519263 2.822230  307.789    14 0.000
#>   [5,]  2.116212 1.143880   31.743    14 0.004
#>   [6,]  6.464781 3.575817   96.972    14 0.000
#>   [7,]  3.061197 1.871265   45.918    14 0.000
#>   [8,]  3.205156 2.070691   48.077    14 0.000
#>   [9,] 17.385610 4.832384  260.784    14 0.000
#>  [10,]  0.852606 0.629717   12.789    14 0.543
#>  [11,]  1.811179 1.097383   27.168    14 0.018
#>  [12,]  1.888894 0.973090   28.333    14 0.013
#>  [13,]  2.215133 1.377159   33.227    14 0.003
#>  [14,]  8.560792 4.114875  128.412    14 0.000
#>  [15,] 10.309808 2.288368  154.647    14 0.000
#>  [16,]  7.396721 2.116377  110.951    14 0.000
#>  [17,]  7.552518 3.753477  113.288    14 0.000
#>  [18,]  2.219779 1.131823   33.297    14 0.003
#>  [19,]  1.773938 0.920643   26.609    14 0.022
#>  [20,]  1.238191 1.064760   18.573    14 0.182
#>  [21,]  4.005094 1.839503   60.076    14 0.000
#>  [22,]  3.287179 2.099884   49.308    14 0.000
#>  [23,]  9.437978 4.193646  141.570    14 0.000
#>  [24,]  7.894766 4.092351  118.421    14 0.000
#>  [25,]  8.760995 3.178259  131.415    14 0.000
#>  [26,]  8.120823 4.154228  121.812    14 0.000
#>  [27,]  5.072479 2.039390   76.087    14 0.000
#>  [28,]  5.476084 2.373559   82.141    14 0.000
#>  [29,]  6.571346 2.911727   98.570    14 0.000
#>  [30,]  3.832834 2.121436   57.493    14 0.000
#>  [31,]  9.924371 4.645983  148.866    14 0.000
#>  [32,]  6.209126 3.140088   93.137    14 0.000
#>  [33,]  7.801346 2.196731  117.020    14 0.000
#>  [34,]  1.996986 1.244710   29.955    14 0.008
#>  [35,]  7.009013 3.013787  105.135    14 0.000
#>  [36,] 10.043734 3.006649  150.656    14 0.000
#>  [37,]  3.379425 1.575404   50.691    14 0.000
#>  [38,]  2.457793 1.331493   36.867    14 0.001
#>  [39,] 23.956303 4.015078  359.345    14 0.000
#>  [40,]  5.991282 2.576539   89.869    14 0.000
#>  [41,]  1.361736 0.659559   20.426    14 0.117
#>  [42,]  9.064115 4.450855  135.962    14 0.000
#>  [43,]  6.934743 3.770169  104.021    14 0.000
#>  [44,]  1.923510 1.201535   28.853    14 0.011
#>  [45,]  0.041678 1.620172    0.625    14 1.000
#>  [46,]  2.707404 1.330003   40.611    14 0.000
#>  [47,]  9.212429 3.920390  138.186    14 0.000
#>  [48,]  1.025937 0.715506   15.389    14 0.352
#>  [49,]  9.617443 4.467762  144.262    14 0.000
#>  [50,]  9.781201 2.986771  146.718    14 0.000
#>  [51,] 25.531424 4.343386  382.971    14 0.000
#>  [52,]  2.076905 1.200306   31.154    14 0.005
#>  [53,]  5.338338 3.128377   80.075    14 0.000
#>  [54,]  3.852223 2.363440   57.783    14 0.000
#>  [55,] 61.083324 4.495719  916.250    14 0.000
#>  [56,]  2.494657 1.581263   37.420    14 0.001
#>  [57,]  4.359789 2.352684   65.397    14 0.000
#>  [58,]  5.299936 2.041931   79.499    14 0.000
#>  [59,] 18.860479 3.946008  282.907    14 0.000
#>  [60,]  2.051505 1.282579   30.773    14 0.006
#>  [61,] 10.766202 3.113440  161.493    14 0.000
#>  [62,]  7.819839 3.573024  117.298    14 0.000
#>  [63,]  6.502639 3.612312   97.540    14 0.000
#>  [64,]  1.785885 1.072591   26.788    14 0.021
#>  [65,] 32.801303 4.625340  492.020    14 0.000
#>  [66,]  4.067202 1.939332   61.008    14 0.000
#>  [67,]  4.027595 1.746452   60.414    14 0.000
#>  [68,]  2.472966 1.495264   37.094    14 0.001
#>  [69,]  6.788243 2.101705  101.824    14 0.000
#>  [70,]  4.433734 1.946938   66.506    14 0.000
#>  [71,]  4.545068 2.766759   68.176    14 0.000
#>  [72,]  7.786680 3.891079  116.800    14 0.000
#>  [73,] 19.351743 2.628585  290.276    14 0.000
#>  [74,]  6.208082 3.406781   93.121    14 0.000
#>  [75,]  8.608079 2.955109  129.121    14 0.000
#>  [76,]  2.475263 1.606843   37.129    14 0.001
#>  [77,]  6.007847 3.380091   90.118    14 0.000
#>  [78,] 14.691517 4.548516  220.373    14 0.000
#>  [79,] 13.610302 4.337049  204.155    14 0.000
#>  [80,]  8.525519 3.360462  127.883    14 0.000
#>  [81,]  4.924671 2.733183   73.870    14 0.000
#>  [82,]  3.220634 1.677949   48.310    14 0.000
#>  [83,]  8.078774 3.824067  121.182    14 0.000
#>  [84,] 12.507722 4.410536  187.616    14 0.000
#>  [85,] 10.108026 3.043820  151.620    14 0.000
#>  [86,]  4.814625 2.629340   72.219    14 0.000
#>  [87,]  7.214552 3.736311  108.218    14 0.000
#>  [88,]  3.451368 2.079769   51.771    14 0.000
#>  [89,]  3.070902 1.357496   46.064    14 0.000
#>  [90,]  9.163211 4.466006  137.448    14 0.000
#>  [91,]  3.280732 1.787778   49.211    14 0.000
#>  [92,]  4.275741 1.598317   64.136    14 0.000
#>  [93,]  7.618447 3.989872  114.277    14 0.000
#>  [94,]  2.428261 1.481315   36.424    14 0.001
#>  [95,]  7.560657 3.826587  113.410    14 0.000
#>  [96,]  5.625983 3.187131   84.390    14 0.000
#>  [97,]  6.596260 3.567218   98.944    14 0.000
#>  [98,]  9.277967 4.135589  139.170    14 0.000
#>  [99,]  8.206313 3.948298  123.095    14 0.000
#> [100,]  8.122280 3.270797  121.834    14 0.000
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
# ------------------------------------------------------------------------

##########################################################################

# ------------------------------------------------------------------------
## 3PL model ##### 
# ------------------------------------------------------------------------
# MLE
res3plmle <- PP_4pl(respm = awm,thres = diffpar,
                    slopes = sl,lowerA = la,type = "mle")

#> Estimating:  3pl model ... 
#> type = mle 
#> Estimation finished!
# WLE
res3plwle <- PP_4pl(respm = awm,thres = diffpar,
                    slopes = sl,lowerA = la,type = "wle")

#> Estimating:  3pl model ... 
#> type = wle 
#> Estimation finished!
# ------------------------------------------------------------------------
## LZ*-Index ##### 
Pfit(respm=awm,pp=res3plwle,fitindices="lzstar")

#> $lzstar
#>           lzstar
#>   [1,] -1.167107
#>   [2,] -2.972067
#>   [3,] -1.996280
#>   [4,] -1.490544
#>   [5,] -0.472727
#>   [6,] -4.464340
#>   [7,] -1.456728
#>   [8,] -1.971257
#>   [9,] -2.456835
#>  [10,]  0.641880
#>  [11,] -0.874465
#>  [12,] -1.666685
#>  [13,] -1.474259
#>  [14,] -1.899192
#>  [15,] -1.737668
#>  [16,] -1.310770
#>  [17,] -1.888048
#>  [18,] -1.815926
#>  [19,] -1.104945
#>  [20,] -0.334555
#>  [21,] -3.623541
#>  [22,] -2.095393
#>  [23,] -3.742391
#>  [24,] -1.091194
#>  [25,] -3.467305
#>  [26,] -2.131304
#>  [27,] -1.823812
#>  [28,] -3.779213
#>  [29,] -1.897761
#>  [30,] -2.282876
#>  [31,] -3.449781
#>  [32,] -2.183877
#>  [33,] -2.021747
#>  [34,] -1.368433
#>  [35,] -2.198432
#>  [36,] -3.579926
#>  [37,] -1.914916
#>  [38,] -2.431900
#>  [39,] -5.256489
#>  [40,] -1.530712
#>  [41,] -0.515219
#>  [42,] -2.603174
#>  [43,] -1.816981
#>  [44,] -0.956564
#>  [45,]  0.100935
#>  [46,] -0.741626
#>  [47,] -3.533871
#>  [48,]  0.097852
#>  [49,] -3.937868
#>  [50,] -4.583706
#>  [51,] -1.612839
#>  [52,] -1.459997
#>  [53,] -1.685451
#>  [54,] -2.209994
#>  [55,] -0.893440
#>  [56,] -0.944765
#>  [57,] -1.661964
#>  [58,] -1.481694
#>  [59,] -5.175055
#>  [60,] -0.456816
#>  [61,] -3.363411
#>  [62,] -3.350986
#>  [63,] -1.617741
#>  [64,] -0.911813
#>  [65,] -0.921325
#>  [66,] -1.528381
#>  [67,]  0.605448
#>  [68,] -0.053901
#>  [69,] -1.096144
#>  [70,] -2.326477
#>  [71,]  0.245738
#>  [72,] -1.093606
#>  [73,] -1.132133
#>  [74,] -2.441409
#>  [75,] -3.339224
#>  [76,] -1.199777
#>  [77,] -2.126979
#>  [78,] -3.364901
#>  [79,] -2.118311
#>  [80,] -4.022433
#>  [81,] -2.509393
#>  [82,] -1.929477
#>  [83,] -2.476316
#>  [84,] -3.549654
#>  [85,] -4.183185
#>  [86,] -2.099526
#>  [87,] -2.266947
#>  [88,] -1.698536
#>  [89,]  0.840654
#>  [90,] -3.871117
#>  [91,] -1.882091
#>  [92,] -0.637227
#>  [93,] -3.303697
#>  [94,] -0.706953
#>  [95,] -1.718385
#>  [96,] -1.955281
#>  [97,] -1.594137
#>  [98,] -2.046207
#>  [99,] -1.409845
#> [100,] -4.424265
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
Pfit(respm=awm,pp=res3plmle,fitindices="lzstar")

#> $lzstar
#>           lzstar
#>   [1,] -1.166144
#>   [2,] -2.971172
#>   [3,] -2.039880
#>   [4,]       NaN
#>   [5,] -0.474762
#>   [6,] -4.464310
#>   [7,] -1.455807
#>   [8,] -1.977924
#>   [9,]       NaN
#>  [10,]  0.642192
#>  [11,] -0.875154
#>  [12,] -1.666647
#>  [13,] -1.475248
#>  [14,] -1.896018
#>  [15,] -1.776836
#>  [16,] -1.301064
#>  [17,] -1.891169
#>  [18,] -1.816158
#>  [19,] -1.104504
#>  [20,] -0.330200
#>  [21,] -3.752019
#>  [22,] -2.094663
#>  [23,] -3.740895
#>  [24,] -1.094117
#>  [25,] -3.488856
#>  [26,] -2.134370
#>  [27,] -1.836525
#>  [28,] -4.011259
#>  [29,] -1.896650
#>  [30,] -2.287365
#>  [31,] -3.539395
#>  [32,] -2.182573
#>  [33,] -2.133323
#>  [34,] -1.369139
#>  [35,] -2.202429
#>  [36,] -3.616914
#>  [37,] -2.037835
#>  [38,] -2.431541
#>  [39,] -5.426791
#>  [40,] -1.527736
#>  [41,] -0.514472
#>  [42,] -2.616449
#>  [43,] -1.824058
#>  [44,] -0.958932
#>  [45,]       NaN
#>  [46,] -0.751293
#>  [47,] -3.531651
#>  [48,]  0.097980
#>  [49,] -3.955371
#>  [50,] -4.608018
#>  [51,] -1.611616
#>  [52,] -1.558881
#>  [53,] -1.684145
#>  [54,] -2.210858
#>  [55,] -0.973382
#>  [56,] -0.944428
#>  [57,] -1.681191
#>  [58,] -1.491758
#>  [59,] -5.202127
#>  [60,] -0.456799
#>  [61,] -3.391269
#>  [62,] -3.366784
#>  [63,] -1.628370
#>  [64,] -0.912803
#>  [65,] -0.868447
#>  [66,] -1.527862
#>  [67,]  0.605705
#>  [68,] -0.054611
#>  [69,] -1.095275
#>  [70,] -2.351613
#>  [71,]  0.246470
#>  [72,] -1.093285
#>  [73,] -1.237523
#>  [74,] -2.441210
#>  [75,] -3.379080
#>  [76,] -1.199402
#>  [77,] -2.125510
#>  [78,] -3.381910
#>  [79,] -2.265319
#>  [80,] -4.022198
#>  [81,] -2.542263
#>  [82,] -1.938870
#>  [83,] -2.475176
#>  [84,] -3.547105
#>  [85,] -4.221018
#>  [86,] -2.109868
#>  [87,] -2.265790
#>  [88,] -1.700476
#>  [89,]  0.843086
#>  [90,] -3.878891
#>  [91,] -1.881595
#>  [92,] -0.634077
#>  [93,] -3.302340
#>  [94,] -0.706649
#>  [95,] -1.718035
#>  [96,] -1.953573
#>  [97,] -1.593705
#>  [98,] -2.045958
#>  [99,] -1.409866
#> [100,] -4.422669
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
## LZ*-Index combined with Infit-Outfit ##### 
Pfit(respm=awm,pp=res3plwle,fitindices=c("lzstar","infit","outfit"))

#> $lzstar
#>           lzstar
#>   [1,] -1.167107
#>   [2,] -2.972067
#>   [3,] -1.996280
#>   [4,] -1.490544
#>   [5,] -0.472727
#>   [6,] -4.464340
#>   [7,] -1.456728
#>   [8,] -1.971257
#>   [9,] -2.456835
#>  [10,]  0.641880
#>  [11,] -0.874465
#>  [12,] -1.666685
#>  [13,] -1.474259
#>  [14,] -1.899192
#>  [15,] -1.737668
#>  [16,] -1.310770
#>  [17,] -1.888048
#>  [18,] -1.815926
#>  [19,] -1.104945
#>  [20,] -0.334555
#>  [21,] -3.623541
#>  [22,] -2.095393
#>  [23,] -3.742391
#>  [24,] -1.091194
#>  [25,] -3.467305
#>  [26,] -2.131304
#>  [27,] -1.823812
#>  [28,] -3.779213
#>  [29,] -1.897761
#>  [30,] -2.282876
#>  [31,] -3.449781
#>  [32,] -2.183877
#>  [33,] -2.021747
#>  [34,] -1.368433
#>  [35,] -2.198432
#>  [36,] -3.579926
#>  [37,] -1.914916
#>  [38,] -2.431900
#>  [39,] -5.256489
#>  [40,] -1.530712
#>  [41,] -0.515219
#>  [42,] -2.603174
#>  [43,] -1.816981
#>  [44,] -0.956564
#>  [45,]  0.100935
#>  [46,] -0.741626
#>  [47,] -3.533871
#>  [48,]  0.097852
#>  [49,] -3.937868
#>  [50,] -4.583706
#>  [51,] -1.612839
#>  [52,] -1.459997
#>  [53,] -1.685451
#>  [54,] -2.209994
#>  [55,] -0.893440
#>  [56,] -0.944765
#>  [57,] -1.661964
#>  [58,] -1.481694
#>  [59,] -5.175055
#>  [60,] -0.456816
#>  [61,] -3.363411
#>  [62,] -3.350986
#>  [63,] -1.617741
#>  [64,] -0.911813
#>  [65,] -0.921325
#>  [66,] -1.528381
#>  [67,]  0.605448
#>  [68,] -0.053901
#>  [69,] -1.096144
#>  [70,] -2.326477
#>  [71,]  0.245738
#>  [72,] -1.093606
#>  [73,] -1.132133
#>  [74,] -2.441409
#>  [75,] -3.339224
#>  [76,] -1.199777
#>  [77,] -2.126979
#>  [78,] -3.364901
#>  [79,] -2.118311
#>  [80,] -4.022433
#>  [81,] -2.509393
#>  [82,] -1.929477
#>  [83,] -2.476316
#>  [84,] -3.549654
#>  [85,] -4.183185
#>  [86,] -2.099526
#>  [87,] -2.266947
#>  [88,] -1.698536
#>  [89,]  0.840654
#>  [90,] -3.871117
#>  [91,] -1.882091
#>  [92,] -0.637227
#>  [93,] -3.303697
#>  [94,] -0.706953
#>  [95,] -1.718385
#>  [96,] -1.955281
#>  [97,] -1.594137
#>  [98,] -2.046207
#>  [99,] -1.409845
#> [100,] -4.424265
#> 
#> $infit
#>           infit      in_t in_chisq in_df in_pv
#>   [1,] 1.290404  1.118365   20.726    14 0.109
#>   [2,] 1.783697  2.512664   28.176    14 0.013
#>   [3,] 1.524228  1.634530   23.514    14 0.052
#>   [4,] 1.167012  0.519078   16.252    14 0.298
#>   [5,] 1.106763  0.469071   17.625    14 0.224
#>   [6,] 2.161029  3.394447   35.617    14 0.001
#>   [7,] 1.386128  1.403349   21.352    14 0.093
#>   [8,] 1.398292  1.444900   29.815    14 0.008
#>   [9,] 1.612827  1.577519   24.590    14 0.039
#>  [10,] 0.836210 -0.563551   11.859    14 0.618
#>  [11,] 1.255814  1.010023   17.516    14 0.230
#>  [12,] 1.447081  1.577185   22.094    14 0.077
#>  [13,] 1.405211  1.492812   20.223    14 0.123
#>  [14,] 1.507969  1.579250   22.697    14 0.065
#>  [15,] 1.396337  1.261899   24.819    14 0.036
#>  [16,] 1.227912  0.777948   25.558    14 0.029
#>  [17,] 1.526581  1.786683   22.420    14 0.070
#>  [18,] 1.474452  1.637013   23.554    14 0.052
#>  [19,] 1.275681  1.053576   20.464    14 0.116
#>  [20,] 1.030595  0.201436   18.507    14 0.185
#>  [21,] 1.953242  2.789249   30.729    14 0.006
#>  [22,] 1.542962  1.887731   24.331    14 0.042
#>  [23,] 1.969506  2.988701   31.934    14 0.004
#>  [24,] 1.312629  1.137809   18.846    14 0.171
#>  [25,] 1.829699  2.660018   34.414    14 0.002
#>  [26,] 1.577239  1.930330   24.009    14 0.046
#>  [27,] 1.422432  1.442175   25.551    14 0.030
#>  [28,] 1.917504  2.347566   35.927    14 0.001
#>  [29,] 1.474714  1.699416   24.120    14 0.044
#>  [30,] 1.625805  2.129784   22.944    14 0.061
#>  [31,] 1.861267  2.284299   32.909    14 0.003
#>  [32,] 1.583342  1.989495   24.217    14 0.043
#>  [33,] 1.411531  1.145206   27.067    14 0.019
#>  [34,] 1.367090  1.373168   20.121    14 0.126
#>  [35,] 1.522478  1.847599   26.862    14 0.020
#>  [36,] 1.819599  2.529551   41.007    14 0.000
#>  [37,] 1.506043  1.514411   22.204    14 0.075
#>  [38,] 1.557846  1.948287   29.480    14 0.009
#>  [39,] 2.229711  3.556466   48.644    14 0.000
#>  [40,] 1.381003  1.290455   22.252    14 0.074
#>  [41,] 1.053674  0.290191   21.826    14 0.082
#>  [42,] 1.748046  2.315368   24.559    14 0.039
#>  [43,] 1.502368  1.644835   21.943    14 0.080
#>  [44,] 1.256170  1.003968   18.149    14 0.200
#>  [45,] 0.119985 -0.918067    0.611    14 1.000
#>  [46,] 1.207648  0.781393   16.860    14 0.264
#>  [47,] 1.933013  2.913652   30.366    14 0.007
#>  [48,] 0.966655 -0.038282   14.820    14 0.391
#>  [49,] 2.022457  2.991833   34.064    14 0.002
#>  [50,] 1.866781  2.464739   99.173    14 0.000
#>  [51,] 1.399052  1.403743   23.446    14 0.053
#>  [52,] 1.210738  0.555658   30.001    14 0.008
#>  [53,] 1.443411  1.593777   22.327    14 0.072
#>  [54,] 1.523700  1.851930   27.438    14 0.017
#>  [55,] 1.133848  0.476329   17.477    14 0.232
#>  [56,] 1.258089  1.001317   18.719    14 0.176
#>  [57,] 1.443466  1.491025   22.179    14 0.075
#>  [58,] 1.366943  1.282165   22.713    14 0.065
#>  [59,] 2.012206  2.857684   97.720    14 0.000
#>  [60,] 1.120062  0.536798   16.540    14 0.282
#>  [61,] 1.674697  2.104802   51.437    14 0.000
#>  [62,] 1.776217  2.531046   36.037    14 0.001
#>  [63,] 1.456508  1.456419   20.371    14 0.119
#>  [64,] 1.264355  1.037694   17.562    14 0.227
#>  [65,] 1.208720  0.723934   18.391    14 0.190
#>  [66,] 1.389903  1.413745   22.143    14 0.076
#>  [67,] 0.850007 -0.506975   11.957    14 0.610
#>  [68,] 1.017708  0.154206   14.533    14 0.411
#>  [69,] 1.290797  1.077717   19.924    14 0.133
#>  [70,] 1.625727  2.019904   23.605    14 0.051
#>  [71,] 0.957952 -0.081231   12.901    14 0.534
#>  [72,] 1.291342  1.098813   19.708    14 0.140
#>  [73,] 1.141112  0.494194   22.951    14 0.061
#>  [74,] 1.620103  2.053505   26.841    14 0.020
#>  [75,] 1.671573  2.071114   61.694    14 0.000
#>  [76,] 1.318408  1.185826   20.171    14 0.125
#>  [77,] 1.566502  1.941182   24.044    14 0.045
#>  [78,] 1.841098  2.460893   32.857    14 0.003
#>  [79,] 1.549253  1.509627   22.498    14 0.069
#>  [80,] 2.058196  3.156006   33.142    14 0.003
#>  [81,] 1.625070  1.969867   27.992    14 0.014
#>  [82,] 1.496191  1.646491   23.098    14 0.059
#>  [83,] 1.638026  2.134644   26.390    14 0.023
#>  [84,] 1.889203  2.826398   32.257    14 0.004
#>  [85,] 1.956714  2.793510   50.594    14 0.000
#>  [86,] 1.452017  1.615653   29.240    14 0.010
#>  [87,] 1.572920  1.988183   26.068    14 0.025
#>  [88,] 1.473404  1.701496   20.751    14 0.108
#>  [89,] 0.783196 -0.778732   10.777    14 0.703
#>  [90,] 2.016235  3.026578   32.841    14 0.003
#>  [91,] 1.502602  1.784175   22.500    14 0.069
#>  [92,] 1.151454  0.617605   18.441    14 0.187
#>  [93,] 1.848490  2.700870   30.256    14 0.007
#>  [94,] 1.192826  0.774304   17.916    14 0.211
#>  [95,] 1.443410  1.564440   22.886    14 0.062
#>  [96,] 1.521763  1.827638   23.269    14 0.056
#>  [97,] 1.408676  1.461519   22.551    14 0.068
#>  [98,] 1.514633  1.753931   25.209    14 0.033
#>  [99,] 1.370478  1.336144   21.294    14 0.094
#> [100,] 2.140027  3.457471   34.770    14 0.002
#> 
#> $outfit
#>          outfit      ou_t ou_chisq ou_df ou_pv
#>   [1,] 1.381753  1.189548   20.726    14 0.109
#>   [2,] 1.878382  2.459135   28.176    14 0.013
#>   [3,] 1.567626  1.378204   23.514    14 0.052
#>   [4,] 1.083448  0.342190   16.252    14 0.298
#>   [5,] 1.174970  0.612143   17.625    14 0.224
#>   [6,] 2.374447  3.437252   35.617    14 0.001
#>   [7,] 1.423463  1.356612   21.352    14 0.093
#>   [8,] 1.987692  1.957350   29.815    14 0.008
#>   [9,] 1.639308  1.252888   24.590    14 0.039
#>  [10,] 0.790590 -0.648875   11.859    14 0.618
#>  [11,] 1.167731  0.578271   17.516    14 0.230
#>  [12,] 1.472953  1.488564   22.094    14 0.077
#>  [13,] 1.348220  1.008111   20.223    14 0.123
#>  [14,] 1.513145  1.258415   22.697    14 0.065
#>  [15,] 1.654600  1.482838   24.819    14 0.036
#>  [16,] 1.703898  1.507266   25.558    14 0.029
#>  [17,] 1.494646  1.504159   22.420    14 0.070
#>  [18,] 1.570293  1.685782   23.554    14 0.052
#>  [19,] 1.364295  1.198994   20.464    14 0.116
#>  [20,] 1.233828  0.727346   18.507    14 0.185
#>  [21,] 2.048594  1.547996   30.729    14 0.006
#>  [22,] 1.622043  1.811877   24.331    14 0.042
#>  [23,] 2.128956  2.982404   31.934    14 0.004
#>  [24,] 1.256399  0.836257   18.846    14 0.171
#>  [25,] 2.294247  2.446556   34.414    14 0.002
#>  [26,] 1.600574  1.769941   24.009    14 0.046
#>  [27,] 1.703367  1.804892   25.551    14 0.030
#>  [28,] 2.395130  2.441270   35.927    14 0.001
#>  [29,] 1.608008  1.708945   24.120    14 0.044
#>  [30,] 1.529578  1.290129   22.944    14 0.061
#>  [31,] 2.193965  2.237537   32.909    14 0.003
#>  [32,] 1.614494  1.831145   24.217    14 0.043
#>  [33,] 1.804499  1.483088   27.067    14 0.019
#>  [34,] 1.341400  1.002506   20.121    14 0.126
#>  [35,] 1.790786  1.914525   26.862    14 0.020
#>  [36,] 2.733791  2.476111   41.007    14 0.000
#>  [37,] 1.480263  1.139997   22.204    14 0.075
#>  [38,] 1.965350  2.418232   29.480    14 0.009
#>  [39,] 3.242959  3.292520   48.644    14 0.000
#>  [40,] 1.483473  1.269288   22.252    14 0.074
#>  [41,] 1.455069  1.136022   21.826    14 0.082
#>  [42,] 1.637243  1.681189   24.559    14 0.039
#>  [43,] 1.462875  1.254995   21.943    14 0.080
#>  [44,] 1.209920  0.630637   18.149    14 0.200
#>  [45,] 0.040718  1.536469    0.611    14 1.000
#>  [46,] 1.123978  0.409346   16.860    14 0.264
#>  [47,] 2.024406  2.744200   30.366    14 0.007
#>  [48,] 0.988000  0.060023   14.820    14 0.391
#>  [49,] 2.270902  2.942509   34.064    14 0.002
#>  [50,] 6.611521  2.983203   99.173    14 0.000
#>  [51,] 1.563081  1.613408   23.446    14 0.053
#>  [52,] 2.000093  1.114669   30.001    14 0.008
#>  [53,] 1.488480  1.486626   22.327    14 0.072
#>  [54,] 1.829178  2.068517   27.438    14 0.017
#>  [55,] 1.165133  0.479490   17.477    14 0.232
#>  [56,] 1.247943  0.865875   18.719    14 0.176
#>  [57,] 1.478613  1.303983   22.179    14 0.075
#>  [58,] 1.514180  1.406448   22.713    14 0.065
#>  [59,] 6.514691  3.456467   97.720    14 0.000
#>  [60,] 1.102673  0.398871   16.540    14 0.282
#>  [61,] 3.429143  2.629649   51.437    14 0.000
#>  [62,] 2.402464  2.657488   36.037    14 0.001
#>  [63,] 1.358033  0.957429   20.371    14 0.119
#>  [64,] 1.170805  0.566900   17.562    14 0.227
#>  [65,] 1.226049  0.635303   18.391    14 0.190
#>  [66,] 1.476200  1.495993   22.143    14 0.076
#>  [67,] 0.797132 -0.627950   11.957    14 0.610
#>  [68,] 0.968894  0.047667   14.533    14 0.411
#>  [69,] 1.328257  1.047255   19.924    14 0.133
#>  [70,] 1.573692  1.088397   23.605    14 0.051
#>  [71,] 0.860053 -0.290960   12.901    14 0.534
#>  [72,] 1.313871  1.059817   19.708    14 0.140
#>  [73,] 1.530086  1.074148   22.951    14 0.061
#>  [74,] 1.789381  2.225124   26.841    14 0.020
#>  [75,] 4.112946  2.647890   61.694    14 0.000
#>  [76,] 1.344758  1.146239   20.171    14 0.125
#>  [77,] 1.602965  1.804360   24.044    14 0.045
#>  [78,] 2.190451  2.544925   32.857    14 0.003
#>  [79,] 1.499834  1.091617   22.498    14 0.069
#>  [80,] 2.209490  3.113712   33.142    14 0.003
#>  [81,] 1.866165  2.063412   27.992    14 0.014
#>  [82,] 1.539886  0.983225   23.098    14 0.059
#>  [83,] 1.759361  2.184112   26.390    14 0.023
#>  [84,] 2.150439  2.923946   32.257    14 0.004
#>  [85,] 3.372932  2.668068   50.594    14 0.000
#>  [86,] 1.949341  1.956624   29.240    14 0.010
#>  [87,] 1.737848  1.994598   26.068    14 0.025
#>  [88,] 1.383407  1.081136   20.751    14 0.108
#>  [89,] 0.718476 -0.878508   10.777    14 0.703
#>  [90,] 2.189428  2.968376   32.841    14 0.003
#>  [91,] 1.500014  1.447735   22.500    14 0.069
#>  [92,] 1.229419  0.748337   18.441    14 0.187
#>  [93,] 2.017042  2.730635   30.256    14 0.007
#>  [94,] 1.194431  0.713186   17.916    14 0.211
#>  [95,] 1.525729  1.622340   22.886    14 0.062
#>  [96,] 1.551242  1.636959   23.269    14 0.056
#>  [97,] 1.503431  1.566339   22.551    14 0.068
#>  [98,] 1.680588  1.953820   25.209    14 0.033
#>  [99,] 1.419570  1.330433   21.294    14 0.094
#> [100,] 2.317969  3.109782   34.770    14 0.002
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
# ------------------------------------------------------------------------

##########################################################################

# ------------------------------------------------------------------------
## 4PL model ##### 
# ------------------------------------------------------------------------
# MLE
res4plmle <- PP_4pl(respm = awm,thres = diffpar,
                    slopes = sl,lowerA = la,upperA=ua,type = "mle")

#> Estimating:  4pl model ... 
#> type = mle 
#> Estimation finished!
# WLE
res4plwle <- PP_4pl(respm = awm,thres = diffpar,
                    slopes = sl,lowerA = la,upperA=ua,type = "wle")

#> Estimating:  4pl model ... 
#> type = wle 
#> Estimation finished!
# ------------------------------------------------------------------------
## LZ*-Index ##### 
Pfit(respm=awm,pp=res4plwle,fitindices="lzstar")

#> $lzstar
#>               lzstar
#>   [1,] -7.110660e-01
#>   [2,] -2.690420e+00
#>   [3,] -2.204481e+00
#>   [4,] -1.386212e+00
#>   [5,] -6.009980e-01
#>   [6,] -4.491770e+00
#>   [7,] -1.519750e+00
#>   [8,] -8.663040e-01
#>   [9,] -2.465895e+00
#>  [10,]  8.444670e-01
#>  [11,] -7.434170e-01
#>  [12,] -1.170209e+00
#>  [13,] -1.575307e+00
#>  [14,] -1.776909e+00
#>  [15,] -1.820740e+00
#>  [16,] -1.227086e+00
#>  [17,] -2.002713e+00
#>  [18,] -1.627960e+00
#>  [19,] -4.936800e-01
#>  [20,] -8.522700e-02
#>  [21,] -2.908888e+00
#>  [22,] -1.699497e+00
#>  [23,] -3.701174e+00
#>  [24,] -1.191120e+00
#>  [25,] -2.248607e+00
#>  [26,] -1.882028e+00
#>  [27,] -2.060016e+00
#>  [28,] -3.779713e+00
#>  [29,] -1.456385e+00
#>  [30,] -2.199146e+00
#>  [31,] -3.366498e+00
#>  [32,] -1.567555e+00
#>  [33,] -1.997269e+00
#>  [34,] -1.322156e+00
#>  [35,] -1.164016e+00
#>  [36,] -2.615033e+00
#>  [37,] -1.817373e+00
#>  [38,] -1.659983e+00
#>  [39,] -4.538809e+00
#>  [40,] -1.451968e+00
#>  [41,]  3.181090e-01
#>  [42,] -2.581170e+00
#>  [43,] -1.571539e+00
#>  [44,] -6.329640e-01
#>  [45,] -1.748010e+09
#>  [46,] -5.459280e-01
#>  [47,] -3.535106e+00
#>  [48,]  4.623000e-02
#>  [49,] -3.838048e+00
#>  [50,] -2.183106e+00
#>  [51,] -1.324876e+00
#>  [52,] -2.473310e-01
#>  [53,] -1.144863e+00
#>  [54,] -1.961395e+00
#>  [55,] -8.021640e-01
#>  [56,] -9.432020e-01
#>  [57,] -1.756644e+00
#>  [58,] -1.367314e+00
#>  [59,] -3.692281e+00
#>  [60,] -2.439740e-01
#>  [61,] -1.534674e+00
#>  [62,] -3.281694e+00
#>  [63,] -1.450629e+00
#>  [64,] -8.991050e-01
#>  [65,] -8.123390e-01
#>  [66,] -1.708696e+00
#>  [67,]  6.816150e-01
#>  [68,] -3.355500e-02
#>  [69,] -9.534480e-01
#>  [70,] -1.948360e+00
#>  [71,]  9.026600e-02
#>  [72,] -1.099325e+00
#>  [73,] -1.124776e+00
#>  [74,] -2.356520e+00
#>  [75,] -1.780751e+00
#>  [76,] -1.363065e+00
#>  [77,] -1.690221e+00
#>  [78,] -3.384571e+00
#>  [79,] -2.158426e+00
#>  [80,] -3.951233e+00
#>  [81,] -2.631824e+00
#>  [82,] -1.245720e+00
#>  [83,] -1.984919e+00
#>  [84,] -3.196848e+00
#>  [85,] -2.786786e+00
#>  [86,] -8.376070e-01
#>  [87,] -2.179747e+00
#>  [88,] -1.494271e+00
#>  [89,]  8.820530e-01
#>  [90,] -3.638853e+00
#>  [91,] -1.990396e+00
#>  [92,] -4.305340e-01
#>  [93,] -3.380538e+00
#>  [94,] -7.007880e-01
#>  [95,] -1.985082e+00
#>  [96,] -1.330435e+00
#>  [97,] -1.596787e+00
#>  [98,] -1.813374e+00
#>  [99,] -1.713560e+00
#> [100,] -3.853027e+00
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
Pfit(respm=awm,pp=res4plmle,fitindices="lzstar")

#> $lzstar
#>           lzstar
#>   [1,] -0.713023
#>   [2,] -2.687144
#>   [3,] -2.245646
#>   [4,]       NaN
#>   [5,] -0.601554
#>   [6,] -4.481686
#>   [7,] -1.516264
#>   [8,] -0.866281
#>   [9,]       NaN
#>  [10,]  0.844055
#>  [11,] -0.743631
#>  [12,] -1.171394
#>  [13,] -1.575111
#>  [14,] -1.773307
#>  [15,] -1.861968
#>  [16,] -1.227223
#>  [17,] -2.004971
#>  [18,] -1.625619
#>  [19,] -0.494098
#>  [20,] -0.088990
#>  [21,] -2.914413
#>  [22,] -1.699248
#>  [23,] -3.699015
#>  [24,] -1.191428
#>  [25,] -2.273114
#>  [26,] -1.877573
#>  [27,] -2.069997
#>  [28,] -4.016476
#>  [29,] -1.465962
#>  [30,] -2.199400
#>  [31,] -3.449152
#>  [32,] -1.598968
#>  [33,] -2.128040
#>  [34,] -1.322060
#>  [35,] -1.170205
#>  [36,] -2.719470
#>  [37,] -1.941199
#>  [38,] -1.655938
#>  [39,] -4.534012
#>  [40,] -1.448411
#>  [41,]  0.323934
#>  [42,] -2.587144
#>  [43,] -1.580397
#>  [44,] -0.632835
#>  [45,]       NaN
#>  [46,] -0.546103
#>  [47,] -3.531229
#>  [48,]  0.047050
#>  [49,] -3.844465
#>  [50,] -2.158645
#>  [51,] -1.322357
#>  [52,] -0.118183
#>  [53,] -1.147444
#>  [54,] -1.962945
#>  [55,] -0.882883
#>  [56,] -0.940966
#>  [57,] -1.770082
#>  [58,] -1.377803
#>  [59,] -3.753507
#>  [60,] -0.243818
#>  [61,] -1.653490
#>  [62,] -3.290089
#>  [63,] -1.468131
#>  [64,] -0.899181
#>  [65,] -0.765914
#>  [66,] -1.704864
#>  [67,]  0.682766
#>  [68,] -0.033415
#>  [69,] -0.951412
#>  [70,] -1.952425
#>  [71,]  0.090078
#>  [72,] -1.096963
#>  [73,] -1.257690
#>  [74,] -2.352601
#>  [75,] -1.803355
#>  [76,] -1.359686
#>  [77,] -1.693056
#>  [78,] -3.392359
#>  [79,] -2.323726
#>  [80,] -3.943727
#>  [81,] -2.659244
#>  [82,] -1.245989
#>  [83,] -1.985922
#>  [84,] -3.246883
#>  [85,] -2.837909
#>  [86,] -0.841309
#>  [87,] -2.184985
#>  [88,] -1.496010
#>  [89,]  0.885379
#>  [90,] -3.635986
#>  [91,] -1.988156
#>  [92,] -0.428660
#>  [93,] -3.374808
#>  [94,] -0.698454
#>  [95,] -1.981752
#>  [96,] -1.340700
#>  [97,] -1.593286
#>  [98,] -1.810378
#>  [99,] -1.711108
#> [100,] -3.931094
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
## LZ*-Index combined with Infit-Outfit ##### 
Pfit(respm=awm,pp=res4plwle,fitindices=c("lzstar","infit","outfit"))

#> $lzstar
#>               lzstar
#>   [1,] -7.110660e-01
#>   [2,] -2.690420e+00
#>   [3,] -2.204481e+00
#>   [4,] -1.386212e+00
#>   [5,] -6.009980e-01
#>   [6,] -4.491770e+00
#>   [7,] -1.519750e+00
#>   [8,] -8.663040e-01
#>   [9,] -2.465895e+00
#>  [10,]  8.444670e-01
#>  [11,] -7.434170e-01
#>  [12,] -1.170209e+00
#>  [13,] -1.575307e+00
#>  [14,] -1.776909e+00
#>  [15,] -1.820740e+00
#>  [16,] -1.227086e+00
#>  [17,] -2.002713e+00
#>  [18,] -1.627960e+00
#>  [19,] -4.936800e-01
#>  [20,] -8.522700e-02
#>  [21,] -2.908888e+00
#>  [22,] -1.699497e+00
#>  [23,] -3.701174e+00
#>  [24,] -1.191120e+00
#>  [25,] -2.248607e+00
#>  [26,] -1.882028e+00
#>  [27,] -2.060016e+00
#>  [28,] -3.779713e+00
#>  [29,] -1.456385e+00
#>  [30,] -2.199146e+00
#>  [31,] -3.366498e+00
#>  [32,] -1.567555e+00
#>  [33,] -1.997269e+00
#>  [34,] -1.322156e+00
#>  [35,] -1.164016e+00
#>  [36,] -2.615033e+00
#>  [37,] -1.817373e+00
#>  [38,] -1.659983e+00
#>  [39,] -4.538809e+00
#>  [40,] -1.451968e+00
#>  [41,]  3.181090e-01
#>  [42,] -2.581170e+00
#>  [43,] -1.571539e+00
#>  [44,] -6.329640e-01
#>  [45,] -1.748010e+09
#>  [46,] -5.459280e-01
#>  [47,] -3.535106e+00
#>  [48,]  4.623000e-02
#>  [49,] -3.838048e+00
#>  [50,] -2.183106e+00
#>  [51,] -1.324876e+00
#>  [52,] -2.473310e-01
#>  [53,] -1.144863e+00
#>  [54,] -1.961395e+00
#>  [55,] -8.021640e-01
#>  [56,] -9.432020e-01
#>  [57,] -1.756644e+00
#>  [58,] -1.367314e+00
#>  [59,] -3.692281e+00
#>  [60,] -2.439740e-01
#>  [61,] -1.534674e+00
#>  [62,] -3.281694e+00
#>  [63,] -1.450629e+00
#>  [64,] -8.991050e-01
#>  [65,] -8.123390e-01
#>  [66,] -1.708696e+00
#>  [67,]  6.816150e-01
#>  [68,] -3.355500e-02
#>  [69,] -9.534480e-01
#>  [70,] -1.948360e+00
#>  [71,]  9.026600e-02
#>  [72,] -1.099325e+00
#>  [73,] -1.124776e+00
#>  [74,] -2.356520e+00
#>  [75,] -1.780751e+00
#>  [76,] -1.363065e+00
#>  [77,] -1.690221e+00
#>  [78,] -3.384571e+00
#>  [79,] -2.158426e+00
#>  [80,] -3.951233e+00
#>  [81,] -2.631824e+00
#>  [82,] -1.245720e+00
#>  [83,] -1.984919e+00
#>  [84,] -3.196848e+00
#>  [85,] -2.786786e+00
#>  [86,] -8.376070e-01
#>  [87,] -2.179747e+00
#>  [88,] -1.494271e+00
#>  [89,]  8.820530e-01
#>  [90,] -3.638853e+00
#>  [91,] -1.990396e+00
#>  [92,] -4.305340e-01
#>  [93,] -3.380538e+00
#>  [94,] -7.007880e-01
#>  [95,] -1.985082e+00
#>  [96,] -1.330435e+00
#>  [97,] -1.596787e+00
#>  [98,] -1.813374e+00
#>  [99,] -1.713560e+00
#> [100,] -3.853027e+00
#> 
#> $infit
#>           infit      in_t in_chisq in_df in_pv
#>   [1,] 1.176911  0.775134   17.334    14 0.239
#>   [2,] 1.660846  2.403197   25.246    14 0.032
#>   [3,] 1.580204  1.729440   24.205    14 0.043
#>   [4,] 1.162143  0.508745   16.254    14 0.298
#>   [5,] 1.135457  0.567278   18.117    14 0.202
#>   [6,] 2.068485  3.459307   34.600    14 0.002
#>   [7,] 1.337814  1.358153   22.815    14 0.063
#>   [8,] 1.220986  0.883252   18.334    14 0.192
#>   [9,] 1.620560  1.582067   24.874    14 0.036
#>  [10,] 0.796241 -0.789675   11.128    14 0.676
#>  [11,] 1.196398  0.855660   17.197    14 0.246
#>  [12,] 1.204918  0.885212   24.348    14 0.042
#>  [13,] 1.389768  1.543304   20.818    14 0.106
#>  [14,] 1.468609  1.488749   22.301    14 0.073
#>  [15,] 1.408817  1.285584   25.689    14 0.028
#>  [16,] 1.219226  0.759261   24.502    14 0.040
#>  [17,] 1.527074  1.844782   22.642    14 0.066
#>  [18,] 1.391249  1.468833   22.380    14 0.071
#>  [19,] 1.121532  0.570010   16.705    14 0.272
#>  [20,] 0.981966  0.025429   16.597    14 0.278
#>  [21,] 1.709419  1.959729   32.556    14 0.003
#>  [22,] 1.428696  1.673463   20.935    14 0.103
#>  [23,] 1.844843  2.938746   32.756    14 0.003
#>  [24,] 1.331031  1.201668   19.065    14 0.162
#>  [25,] 1.578990  1.769872   26.265    14 0.024
#>  [26,] 1.421633  1.642534   24.406    14 0.041
#>  [27,] 1.468576  1.555915   26.866    14 0.020
#>  [28,] 1.916471  2.348913   36.326    14 0.001
#>  [29,] 1.360912  1.400637   20.174    14 0.125
#>  [30,] 1.563777  2.049979   22.448    14 0.070
#>  [31,] 1.842328  2.273654   32.527    14 0.003
#>  [32,] 1.397094  1.500457   20.422    14 0.117
#>  [33,] 1.409529  1.139362   27.456    14 0.017
#>  [34,] 1.322709  1.312587   19.976    14 0.131
#>  [35,] 1.341019  1.232990   18.325    14 0.192
#>  [36,] 1.700708  1.989948   27.789    14 0.015
#>  [37,] 1.486110  1.487698   21.754    14 0.084
#>  [38,] 1.268319  1.063235   32.672    14 0.003
#>  [39,] 2.132797  2.059090   73.897    14 0.000
#>  [40,] 1.349625  1.208351   22.030    14 0.078
#>  [41,] 0.911694 -0.277668   13.443    14 0.492
#>  [42,] 1.717827  2.280571   24.356    14 0.041
#>  [43,] 1.426474  1.471830   20.843    14 0.106
#>  [44,] 1.163283  0.710137   16.775    14 0.268
#>  [45,] 0.138095 -1.744378    1.450    14 1.000
#>  [46,] 1.110653  0.496968   18.752    14 0.175
#>  [47,] 1.810369  2.841391   31.670    14 0.004
#>  [48,] 0.970039 -0.033815   15.356    14 0.354
#>  [49,] 1.972067  3.029858   31.317    14 0.005
#>  [50,] 1.515897  1.318001   30.581    14 0.006
#>  [51,] 1.317985  1.201181   21.323    14 0.094
#>  [52,] 0.916642 -0.037691   14.141    14 0.439
#>  [53,] 1.283396  1.160023   19.005    14 0.165
#>  [54,] 1.374388  1.463097   29.406    14 0.009
#>  [55,] 1.123010  0.450045   17.102    14 0.251
#>  [56,] 1.235113  0.980114   18.572    14 0.182
#>  [57,] 1.464665  1.541830   22.336    14 0.072
#>  [58,] 1.342464  1.240680   21.216    14 0.096
#>  [59,] 1.839892  1.858819   66.211    14 0.000
#>  [60,] 1.058104  0.318429   15.805    14 0.325
#>  [61,] 1.440902  1.191732   20.187    14 0.124
#>  [62,] 1.550162  1.839547   60.720    14 0.000
#>  [63,] 1.405243  1.339987   19.832    14 0.136
#>  [64,] 1.236708  1.003788   17.722    14 0.220
#>  [65,] 1.179982  0.644914   18.069    14 0.204
#>  [66,] 1.401063  1.522782   22.769    14 0.064
#>  [67,] 0.833876 -0.607047   11.835    14 0.620
#>  [68,] 1.002898  0.091826   15.069    14 0.373
#>  [69,] 1.241948  0.952895   18.925    14 0.168
#>  [70,] 1.459134  1.605095   27.480    14 0.017
#>  [71,] 0.990543  0.039241   14.086    14 0.443
#>  [72,] 1.262305  1.055717   19.828    14 0.136
#>  [73,] 1.140859  0.492363   23.433    14 0.054
#>  [74,] 1.564827  2.014618   25.484    14 0.030
#>  [75,] 1.273513  0.875734   46.842    14 0.000
#>  [76,] 1.333965  1.283852   20.788    14 0.107
#>  [77,] 1.425132  1.642142   20.855    14 0.105
#>  [78,] 1.839111  2.468969   32.344    14 0.004
#>  [79,] 1.560853  1.522697   22.911    14 0.062
#>  [80,] 1.979050  3.184620   31.084    14 0.005
#>  [81,] 1.651428  2.026465   28.166    14 0.014
#>  [82,] 1.323059  1.200651   20.292    14 0.121
#>  [83,] 1.495546  1.883768   22.120    14 0.076
#>  [84,] 1.663714  2.256829   40.266    14 0.000
#>  [85,] 1.721138  1.958328   30.519    14 0.006
#>  [86,] 1.244847  0.925934   17.094    14 0.251
#>  [87,] 1.452404  1.733160   28.781    14 0.011
#>  [88,] 1.382203  1.493697   19.932    14 0.132
#>  [89,] 0.778498 -0.814241   10.693    14 0.710
#>  [90,] 1.817286  2.861117   32.988    14 0.003
#>  [91,] 1.483835  1.847946   22.802    14 0.064
#>  [92,] 1.094036  0.430093   17.452    14 0.233
#>  [93,] 1.780427  2.721770   30.539    14 0.006
#>  [94,] 1.172348  0.732421   17.910    14 0.211
#>  [95,] 1.479536  1.732606   23.830    14 0.048
#>  [96,] 1.337122  1.295937   19.598    14 0.143
#>  [97,] 1.381828  1.445824   22.154    14 0.076
#>  [98,] 1.428079  1.591875   23.363    14 0.055
#>  [99,] 1.423357  1.529070   22.590    14 0.067
#> [100,] 2.044137  2.931911   32.164    14 0.004
#> 
#> $outfit
#>          outfit      ou_t ou_chisq ou_df ou_pv
#>   [1,] 1.155569  0.568184   17.334    14 0.239
#>   [2,] 1.683063  2.039055   25.246    14 0.032
#>   [3,] 1.613676  1.418938   24.205    14 0.043
#>   [4,] 1.083619  0.341057   16.254    14 0.298
#>   [5,] 1.207773  0.694974   18.117    14 0.202
#>   [6,] 2.306655  3.488695   34.600    14 0.002
#>   [7,] 1.520988  1.657844   22.815    14 0.063
#>   [8,] 1.222290  0.616362   18.334    14 0.192
#>   [9,] 1.658298  1.273223   24.874    14 0.036
#>  [10,] 0.741886 -0.890418   11.128    14 0.676
#>  [11,] 1.146483  0.559758   17.197    14 0.246
#>  [12,] 1.623177  1.797621   24.348    14 0.042
#>  [13,] 1.387835  1.252818   20.818    14 0.106
#>  [14,] 1.486713  1.224189   22.301    14 0.073
#>  [15,] 1.712575  1.573990   25.689    14 0.028
#>  [16,] 1.633439  1.407599   24.502    14 0.040
#>  [17,] 1.509479  1.573290   22.642    14 0.066
#>  [18,] 1.491973  1.576974   22.380    14 0.071
#>  [19,] 1.113686  0.458839   16.705    14 0.272
#>  [20,] 1.106448  0.416060   16.597    14 0.278
#>  [21,] 2.170397  1.541556   32.556    14 0.003
#>  [22,] 1.395660  1.271721   20.935    14 0.103
#>  [23,] 2.183762  3.073228   32.756    14 0.003
#>  [24,] 1.270978  0.871994   19.065    14 0.162
#>  [25,] 1.751004  1.197132   26.265    14 0.024
#>  [26,] 1.627060  1.926412   24.406    14 0.041
#>  [27,] 1.791041  1.931899   26.866    14 0.020
#>  [28,] 2.421714  2.484903   36.326    14 0.001
#>  [29,] 1.344944  1.023265   20.174    14 0.125
#>  [30,] 1.496533  1.389644   22.448    14 0.070
#>  [31,] 2.168438  2.243419   32.527    14 0.003
#>  [32,] 1.361484  1.028282   20.422    14 0.117
#>  [33,] 1.830393  1.519544   27.456    14 0.017
#>  [34,] 1.331761  1.097175   19.976    14 0.131
#>  [35,] 1.221681  0.561992   18.325    14 0.192
#>  [36,] 1.852580  1.267571   27.789    14 0.015
#>  [37,] 1.450268  1.106647   21.754    14 0.084
#>  [38,] 2.178156  2.507924   32.672    14 0.003
#>  [39,] 4.926468  2.627749   73.897    14 0.000
#>  [40,] 1.468650  1.255503   22.030    14 0.078
#>  [41,] 0.896232 -0.169578   13.443    14 0.492
#>  [42,] 1.623759  1.706882   24.356    14 0.041
#>  [43,] 1.389513  1.147050   20.843    14 0.106
#>  [44,] 1.118338  0.444263   16.775    14 0.268
#>  [45,] 0.096678 -1.031798    1.450    14 1.000
#>  [46,] 1.250110  0.647056   18.752    14 0.175
#>  [47,] 2.111341  2.972135   31.670    14 0.004
#>  [48,] 1.023700  0.177921   15.356    14 0.354
#>  [49,] 2.087807  2.878724   31.317    14 0.005
#>  [50,] 2.038709  1.286874   30.581    14 0.006
#>  [51,] 1.421503  1.338620   21.323    14 0.094
#>  [52,] 0.942754  0.238367   14.141    14 0.439
#>  [53,] 1.267020  0.873093   19.005    14 0.165
#>  [54,] 1.960400  2.404383   29.406    14 0.009
#>  [55,] 1.140121  0.435494   17.102    14 0.251
#>  [56,] 1.238151  0.874280   18.572    14 0.182
#>  [57,] 1.489079  1.313666   22.336    14 0.072
#>  [58,] 1.414373  1.241061   21.216    14 0.096
#>  [59,] 4.414047  2.630437   66.211    14 0.000
#>  [60,] 1.053643  0.273362   15.805    14 0.325
#>  [61,] 1.345806  0.663449   20.187    14 0.124
#>  [62,] 4.047990  3.453405   60.720    14 0.000
#>  [63,] 1.322100  0.902949   19.832    14 0.136
#>  [64,] 1.181462  0.661622   17.722    14 0.220
#>  [65,] 1.204571  0.593722   18.069    14 0.204
#>  [66,] 1.517938  1.665494   22.769    14 0.064
#>  [67,] 0.789010 -0.680598   11.835    14 0.620
#>  [68,] 1.004614  0.118983   15.069    14 0.373
#>  [69,] 1.261671  0.902935   18.925    14 0.168
#>  [70,] 1.832032  1.457854   27.480    14 0.017
#>  [71,] 0.939038 -0.109014   14.086    14 0.443
#>  [72,] 1.321892  1.117963   19.828    14 0.136
#>  [73,] 1.562187  1.119564   23.433    14 0.054
#>  [74,] 1.698942  2.116275   25.484    14 0.030
#>  [75,] 3.122808  2.221781   46.842    14 0.000
#>  [76,] 1.385875  1.288869   20.788    14 0.107
#>  [77,] 1.390320  1.193525   20.855    14 0.105
#>  [78,] 2.156245  2.522313   32.344    14 0.004
#>  [79,] 1.527388  1.129103   22.911    14 0.062
#>  [80,] 2.072282  2.991858   31.084    14 0.005
#>  [81,] 1.877748  2.066965   28.166    14 0.014
#>  [82,] 1.352768  0.793004   20.292    14 0.121
#>  [83,] 1.474673  1.439440   22.120    14 0.076
#>  [84,] 2.684413  3.117598   40.266    14 0.000
#>  [85,] 2.034615  1.403353   30.519    14 0.006
#>  [86,] 1.139611  0.425807   17.094    14 0.251
#>  [87,] 1.918723  2.387667   28.781    14 0.011
#>  [88,] 1.328808  1.024887   19.932    14 0.132
#>  [89,] 0.712881 -0.903856   10.693    14 0.710
#>  [90,] 2.199223  3.101305   32.988    14 0.003
#>  [91,] 1.520110  1.647505   22.802    14 0.064
#>  [92,] 1.163441  0.590459   17.452    14 0.233
#>  [93,] 2.035942  2.905827   30.539    14 0.006
#>  [94,] 1.193993  0.728749   17.910    14 0.211
#>  [95,] 1.588692  1.813962   23.830    14 0.048
#>  [96,] 1.306556  0.883348   19.598    14 0.143
#>  [97,] 1.476940  1.544335   22.154    14 0.076
#>  [98,] 1.557524  1.755593   23.363    14 0.055
#>  [99,] 1.506004  1.553722   22.590    14 0.067
#> [100,] 2.144268  1.658801   32.164    14 0.004
#> 
#> attr(,"class")
#> [1] "list"  "PPfit"
# ------------------------------------------------------------------------

##########################################################################