S3 method for printing objects of class "exametrika". This function formats and displays appropriate summary information based on the specific subclass of the exametrika object. Different types of analysis results (IRT, LCA, network models, etc.) are presented with customized formatting to highlight the most relevant information.
Usage
# S3 method for class 'exametrika'
print(x, digits = 3, ...)Value
Prints a formatted summary of the exametrika object to the console, with content varying by object subclass:
- TestStatistics
Basic descriptive statistics of the test
- Dimensionality
Eigenvalue analysis results with scree plot
- ItemStatistics
Item-level statistics and psychometric properties
- QitemStatistics
Item statistics for polytomous items
- exametrikaData
Data structure details including response patterns and weights
- IIAnalysis
Item-item relationship measures (tetrachoric correlations, etc.)
- CTT
Classical Test Theory reliability measures
- IRT/GRM
Item parameters, ability estimates, and fit indices
- LCA/LRA
Class/Rank profiles, distribution information, and model fit statistics
- Biclustering/Biclustering_IRM
Cluster profiles, field distributions, and model diagnostics
- LDLRA/LDB/BINET
Network visualizations, parameter estimates, and conditional probabilities
Details
The function identifies the specific subclass of the exametrika object and tailors the output accordingly. For most analysis types, the function displays:
Basic model description and parameters
Estimation results (e.g., item parameters, latent class profiles)
Model fit statistics and diagnostics
Visual representations where appropriate (e.g., graphs for network models, scree plots for dimensionality analysis)
When printing network-based models (LDLRA, LDB, BINET), this function visualizes the network structure using graphs, which can help in interpreting complex relationships between items or latent variables.
Examples
# \donttest{
# Print IRT analysis results with 4 decimal places
result <- IRT(J15S500)
#> No ID column detected. All columns treated as response data. Sequential IDs (Student1, Student2, ...) were generated. Use id= parameter to specify the ID column explicitly.
#> No ID column detected. All columns treated as response data. Sequential IDs (Student1, Student2, ...) were generated. Use id= parameter to specify the ID column explicitly.
#>
iter 1 LogLik -3915.61
#>
iter 2 LogLik -3901.1
#>
iter 3 LogLik -3896.89
#>
iter 4 LogLik -3894.98
#>
iter 5 LogLik -3894.02
#>
iter 6 LogLik -3893.53
#>
iter 7 LogLik -3893.28
#>
iter 8 LogLik -3893.15
#>
iter 9 LogLik -3893.08
#>
iter 10 LogLik -3893.04
#>
iter 11 LogLik -3893.03
print(result, digits = 4)
#> Item Parameters
#> slope location PSD(slope) PSD(location)
#> Item01 0.6983 -1.6830 0.10932 0.2658
#> Item02 0.8104 -1.5525 0.11663 0.2209
#> Item03 0.5591 -1.8382 0.09877 0.3382
#> Item04 1.4160 -1.1781 0.15686 0.1134
#> Item05 0.6808 -2.2416 0.11517 0.3599
#> Item06 0.9967 -2.1624 0.14990 0.2733
#> Item07 1.0843 -1.0393 0.12808 0.1303
#> Item08 0.6938 -0.5575 0.10022 0.1528
#> Item09 0.3472 1.6298 0.07659 0.4273
#> Item10 0.4918 -1.4207 0.09065 0.3058
#> Item11 1.1221 1.0203 0.13140 0.1245
#> Item12 1.2159 1.0312 0.13848 0.1172
#> Item13 0.8752 -0.7197 0.11113 0.1332
#> Item14 1.1996 -1.2316 0.14069 0.1338
#> Item15 0.8228 -1.2030 0.11274 0.1798
#>
#> Item Fit Indices
#> model_log_like bench_log_like null_log_like model_Chi_sq null_Chi_sq
#> Item01 -263.5243 -240.1896 -283.3432 46.6693 86.3072
#> Item02 -252.9135 -235.4364 -278.9486 34.9543 87.0245
#> Item03 -281.0830 -260.9064 -293.5981 40.3532 65.3834
#> Item04 -205.8510 -192.0718 -265.9618 27.5585 147.7800
#> Item05 -232.0722 -206.5372 -247.4032 51.0699 81.7320
#> Item06 -173.9301 -153.9397 -198.8174 39.9807 89.7553
#> Item07 -252.0388 -228.3788 -298.3455 47.3201 139.9335
#> Item08 -313.7538 -293.2252 -338.7888 41.0573 91.1272
#> Item09 -325.6916 -300.4923 -327.8422 50.3986 54.6997
#> Item10 -309.4483 -288.1984 -319.8497 42.4998 63.3026
#> Item11 -250.8358 -224.0855 -299.2653 53.5007 150.3596
#> Item12 -240.2466 -214.7967 -293.5981 50.8999 157.6029
#> Item13 -291.8161 -262.0307 -328.3959 59.5709 132.7304
#> Item14 -224.3296 -204.9528 -273.2123 38.7536 136.5190
#> Item15 -273.1202 -254.7637 -302.8469 36.7131 96.1665
#> model_df null_df NFI RFI IFI TLI CFI RMSEA AIC
#> Item01 12 13 0.4593 0.4142 0.5334 0.4877 0.5271 0.0761 22.6693
#> Item02 12 13 0.5983 0.5649 0.6940 0.6641 0.6899 0.0619 10.9543
#> Item03 12 13 0.3828 0.3314 0.4689 0.4136 0.4587 0.0688 16.3532
#> Item04 12 13 0.8135 0.7980 0.8854 0.8749 0.8846 0.0510 3.5585
#> Item05 12 13 0.3752 0.3231 0.4397 0.3842 0.4316 0.0808 27.0699
#> Item06 12 13 0.5546 0.5174 0.6401 0.6051 0.6355 0.0684 15.9807
#> Item07 12 13 0.6618 0.6337 0.7239 0.6986 0.7217 0.0768 23.3201
#> Item08 12 13 0.5495 0.5119 0.6328 0.5971 0.6281 0.0697 17.0573
#> Item09 12 13 0.0786 0.0019 0.1007 0.0024 0.0792 0.0801 26.3986
#> Item10 12 13 0.3286 0.2727 0.4055 0.3431 0.3937 0.0714 18.4998
#> Item11 12 13 0.6442 0.6145 0.7001 0.6727 0.6979 0.0833 29.5007
#> Item12 12 13 0.6770 0.6501 0.7328 0.7086 0.7310 0.0806 26.8999
#> Item13 12 13 0.5512 0.5138 0.6060 0.5696 0.6027 0.0891 35.5709
#> Item14 12 13 0.7161 0.6925 0.7851 0.7654 0.7834 0.0668 14.7536
#> Item15 12 13 0.6182 0.5864 0.7064 0.6781 0.7028 0.0642 12.7131
#> CAIC BIC
#> Item01 -39.9060 -27.9060
#> Item02 -51.6210 -39.6210
#> Item03 -46.2221 -34.2221
#> Item04 -59.0168 -47.0168
#> Item05 -35.5054 -23.5054
#> Item06 -46.5946 -34.5946
#> Item07 -39.2552 -27.2552
#> Item08 -45.5180 -33.5180
#> Item09 -36.1767 -24.1767
#> Item10 -44.0755 -32.0755
#> Item11 -33.0746 -21.0746
#> Item12 -35.6754 -23.6754
#> Item13 -27.0044 -15.0044
#> Item14 -47.8217 -35.8217
#> Item15 -49.8622 -37.8622
#>
#> Model Fit Indices
#> value
#> model_log_like -3890.6551
#> bench_log_like -3560.0051
#> null_log_like -4350.2170
#> model_Chi_sq 661.2999
#> null_Chi_sq 1580.4238
#> model_df 180.0000
#> null_df 195.0000
#> NFI 0.5816
#> RFI 0.5467
#> IFI 0.6563
#> TLI 0.6236
#> CFI 0.6526
#> RMSEA 0.0732
#> AIC 301.2999
#> CAIC -637.3296
#> BIC -457.3296
# Print Latent Class Analysis results
result_lca <- LCA(J15S500, ncls = 3)
#>
iter 1 log_lik -3955.4
#>
iter 2 log_lik -3904.63
#>
iter 3 log_lik -3890.82
#>
iter 4 log_lik -3880
#>
iter 5 log_lik -3870.82
#>
iter 6 log_lik -3863.52
#>
iter 7 log_lik -3857.89
#>
iter 8 log_lik -3853.58
#>
iter 9 log_lik -3850.31
#>
iter 10 log_lik -3847.86
#>
iter 11 log_lik -3846.05
#>
iter 12 log_lik -3844.72
#>
iter 13 log_lik -3843.74
#>
iter 14 log_lik -3843.02
#>
iter 15 log_lik -3842.48
#>
iter 16 log_lik -3842.07
#>
iter 17 log_lik -3841.76
print(result_lca)
#>
#> Item Reference Profile
#> IRP1 IRP2 IRP3
#> Item01 0.5972 0.749 0.885
#> Item02 0.5604 0.809 0.882
#> Item03 0.5914 0.778 0.800
#> Item04 0.4927 0.851 0.968
#> Item05 0.6757 0.853 0.876
#> Item06 0.6872 0.961 0.933
#> Item07 0.4314 0.807 0.894
#> Item08 0.3586 0.680 0.712
#> Item09 0.3403 0.261 0.493
#> Item10 0.5154 0.749 0.712
#> Item11 0.0867 0.159 0.606
#> Item12 0.0732 0.171 0.570
#> Item13 0.3298 0.826 0.727
#> Item14 0.5176 0.788 0.974
#> Item15 0.4533 0.821 0.829
#>
#> Test Profile
#> Class 1 Class 2 Class 3
#> Test Reference Profile 6.711 10.263 11.861
#> Latent Class Ditribution 156.000 178.000 166.000
#> Class Membership Distribution 159.885 172.129 167.987
#>
#> Item Fit Indices
#> model_log_like bench_log_like null_log_like model_Chi_sq null_Chi_sq
#> Item01 -264.789 -240.190 -283.343 49.199 86.307
#> Item02 -254.621 -235.436 -278.949 38.370 87.025
#> Item03 -283.141 -260.906 -293.598 44.469 65.383
#> Item04 -206.729 -192.072 -265.962 29.314 147.780
#> Item05 -235.601 -206.537 -247.403 58.127 81.732
#> Item06 -169.064 -153.940 -198.817 30.248 89.755
#> Item07 -250.627 -228.379 -298.345 44.496 139.933
#> Item08 -313.073 -293.225 -338.789 39.697 91.127
#> Item09 -317.685 -300.492 -327.842 34.384 54.700
#> Item10 -308.505 -288.198 -319.850 40.614 63.303
#> Item11 -235.022 -224.085 -299.265 21.873 150.360
#> Item12 -235.443 -214.797 -293.598 41.293 157.603
#> Item13 -279.431 -262.031 -328.396 34.800 132.730
#> Item14 -220.099 -204.953 -273.212 30.292 136.519
#> Item15 -267.926 -254.764 -302.847 26.324 96.166
#> model_df null_df NFI RFI IFI TLI CFI RMSEA AIC CAIC
#> Item01 11 13 0.430 0.326 0.493 0.384 0.479 0.083 27.199 -30.162
#> Item02 11 13 0.559 0.479 0.640 0.563 0.630 0.071 16.370 -40.991
#> Item03 11 13 0.320 0.196 0.385 0.245 0.361 0.078 22.469 -34.892
#> Item04 11 13 0.802 0.766 0.866 0.839 0.864 0.058 7.314 -50.046
#> Item05 11 13 0.289 0.160 0.334 0.190 0.314 0.093 36.127 -21.234
#> Item06 11 13 0.663 0.602 0.756 0.704 0.749 0.059 8.248 -49.112
#> Item07 11 13 0.682 0.624 0.740 0.688 0.736 0.078 22.496 -34.864
#> Item08 11 13 0.564 0.485 0.642 0.566 0.633 0.072 17.697 -39.664
#> Item09 11 13 0.371 0.257 0.465 0.337 0.439 0.065 12.384 -44.976
#> Item10 11 13 0.358 0.242 0.434 0.304 0.411 0.073 18.614 -38.746
#> Item11 11 13 0.855 0.828 0.922 0.906 0.921 0.045 -0.127 -57.487
#> Item12 11 13 0.738 0.690 0.793 0.752 0.791 0.074 19.293 -38.068
#> Item13 11 13 0.738 0.690 0.804 0.765 0.801 0.066 12.800 -44.561
#> Item14 11 13 0.778 0.738 0.846 0.815 0.844 0.059 8.292 -49.069
#> Item15 11 13 0.726 0.676 0.820 0.782 0.816 0.053 4.324 -53.037
#> BIC
#> Item01 -19.162
#> Item02 -29.991
#> Item03 -23.892
#> Item04 -39.046
#> Item05 -10.234
#> Item06 -38.112
#> Item07 -23.864
#> Item08 -28.664
#> Item09 -33.976
#> Item10 -27.746
#> Item11 -46.487
#> Item12 -27.068
#> Item13 -33.561
#> Item14 -38.069
#> Item15 -42.037
#>
#> Model Fit Indices
#> Number of Latent class: 3
#> Number of EM cycle: 17
#> value
#> model_log_like -3841.755
#> bench_log_like -3560.005
#> null_log_like -4350.217
#> model_Chi_sq 563.500
#> null_Chi_sq 1580.424
#> model_df 165.000
#> null_df 195.000
#> NFI 0.643
#> RFI 0.579
#> IFI 0.718
#> TLI 0.660
#> CFI 0.712
#> RMSEA 0.070
#> AIC 233.500
#> CAIC -626.910
#> BIC -461.910
# }