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 class 'gpcm'
Pfit(respm, pp, fitindices = c("infit", "outfit"), SE = FALSE)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
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.
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.
################# 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"
# ------------------------------------------------------------------------
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