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.
Arguments
- x
An object of class "exametrika" with various possible subclasses
- digits
Integer indicating the number of decimal places to display. Default is 3.
- fit_indices
For models that can carry both: which set of fit indices to show. "both" (default), "pattern" for the response-pattern based ones, or "margin" for the margin-based ones from
add_M2. The two are built from chi-squares that live in different worlds and are never combined into a single set.- ...
Additional arguments passed to print methods (not currently used)
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)
print(result, digits = 4)
#> Item Parameters
#> slope location PSD(slope) PSD(location)
#> Item01 0.6982 -1.6838 0.10931 0.2659
#> Item02 0.8104 -1.5531 0.11662 0.2209
#> Item03 0.5591 -1.8388 0.09876 0.3382
#> Item04 1.4162 -1.1787 0.15687 0.1134
#> Item05 0.6808 -2.2423 0.11517 0.3599
#> Item06 0.9966 -2.1632 0.14989 0.2733
#> Item07 1.0843 -1.0400 0.12808 0.1303
#> Item08 0.6938 -0.5582 0.10021 0.1528
#> Item09 0.3472 1.6292 0.07659 0.4273
#> Item10 0.4918 -1.4214 0.09065 0.3058
#> Item11 1.1222 1.0197 0.13139 0.1245
#> Item12 1.2161 1.0305 0.13849 0.1171
#> Item13 0.8751 -0.7204 0.11112 0.1332
#> Item14 1.1995 -1.2322 0.14069 0.1338
#> Item15 0.8227 -1.2036 0.11274 0.1798
#>
#> Item Fit Indices
#> model_log_like bench_log_like null_log_like model_Chi_sq null_Chi_sq
#> Item01 -263.5262 -240.1896 -283.3432 46.6731 86.3072
#> Item02 -252.9125 -235.4364 -278.9486 34.9522 87.0245
#> Item03 -281.0828 -260.9064 -293.5981 40.3527 65.3834
#> Item04 -205.8387 -192.0718 -265.9618 27.5338 147.7800
#> Item05 -232.0733 -206.5372 -247.4032 51.0722 81.7320
#> Item06 -173.9331 -153.9397 -198.8174 39.9867 89.7553
#> Item07 -252.0373 -228.3788 -298.3455 47.3171 139.9335
#> Item08 -313.7555 -293.2252 -338.7888 41.0607 91.1272
#> Item09 -325.6907 -300.4923 -327.8422 50.3966 54.6997
#> Item10 -309.4496 -288.1984 -319.8497 42.5026 63.3026
#> Item11 -250.8297 -224.0855 -299.2653 53.4885 150.3596
#> Item12 -240.2314 -214.7967 -293.5981 50.8694 157.6029
#> Item13 -291.8217 -262.0307 -328.3959 59.5819 132.7304
#> Item14 -224.3306 -204.9528 -273.2123 38.7556 136.5190
#> Item15 -273.1223 -254.7637 -302.8469 36.7173 96.1665
#> model_df null_df NFI RFI IFI TLI CFI RMSEA AIC
#> Item01 12 13 0.4592 0.4142 0.5334 0.4876 0.5270 0.0761 22.6731
#> Item02 12 13 0.5984 0.5649 0.6941 0.6641 0.6899 0.0619 10.9522
#> Item03 12 13 0.3828 0.3314 0.4689 0.4136 0.4587 0.0688 16.3527
#> Item04 12 13 0.8137 0.7982 0.8856 0.8751 0.8847 0.0509 3.5338
#> Item05 12 13 0.3751 0.3231 0.4397 0.3842 0.4315 0.0808 27.0722
#> Item06 12 13 0.5545 0.5174 0.6401 0.6050 0.6354 0.0684 15.9867
#> Item07 12 13 0.6619 0.6337 0.7239 0.6986 0.7218 0.0768 23.3171
#> Item08 12 13 0.5494 0.5119 0.6327 0.5970 0.6280 0.0697 17.0607
#> Item09 12 13 0.0787 0.0019 0.1008 0.0025 0.0792 0.0801 26.3966
#> Item10 12 13 0.3286 0.2726 0.4054 0.3431 0.3936 0.0714 18.5026
#> Item11 12 13 0.6443 0.6146 0.7001 0.6728 0.6980 0.0832 29.4885
#> Item12 12 13 0.6772 0.6503 0.7330 0.7088 0.7312 0.0806 26.8694
#> Item13 12 13 0.5511 0.5137 0.6059 0.5695 0.6026 0.0891 35.5819
#> Item14 12 13 0.7161 0.6925 0.7851 0.7653 0.7834 0.0668 14.7556
#> Item15 12 13 0.6182 0.5864 0.7063 0.6780 0.7028 0.0642 12.7173
#> CAIC BIC
#> Item01 -39.9021 -27.9021
#> Item02 -51.6231 -39.6231
#> Item03 -46.2226 -34.2226
#> Item04 -59.0415 -47.0415
#> Item05 -35.5031 -23.5031
#> Item06 -46.5886 -34.5886
#> Item07 -39.2582 -27.2582
#> Item08 -45.5146 -33.5146
#> Item09 -36.1787 -24.1787
#> Item10 -44.0727 -32.0727
#> Item11 -33.0868 -21.0868
#> Item12 -35.7059 -23.7059
#> Item13 -26.9934 -14.9934
#> Item14 -47.8197 -35.8197
#> Item15 -49.8580 -37.8580
#>
#> Model Fit Indices
#> value
#> model_log_like -3890.6353
#> bench_log_like -3560.0051
#> null_log_like -4350.2170
#> model_Chi_sq 661.2604
#> null_Chi_sq 1580.4238
#> model_df 180.0000
#> null_df 195.0000
#> NFI 0.5816
#> RFI 0.5467
#> IFI 0.6563
#> TLI 0.6237
#> CFI 0.6526
#> RMSEA 0.0732
#> AIC 301.2604
#> CAIC -637.3691
#> BIC -457.3691
# Print Latent Class Analysis results
result_lca <- LCA(J15S500, ncls = 3)
print(result_lca)
#>
#> Item Reference Profile
#> IRP1 IRP2 IRP3
#> Item01 0.5952 0.761 0.877
#> Item02 0.5597 0.820 0.875
#> Item03 0.5922 0.782 0.799
#> Item04 0.5027 0.838 0.979
#> Item05 0.6764 0.859 0.872
#> Item06 0.6864 0.972 0.927
#> Item07 0.4390 0.807 0.893
#> Item08 0.3602 0.690 0.705
#> Item09 0.3441 0.242 0.509
#> Item10 0.5138 0.766 0.699
#> Item11 0.0831 0.190 0.582
#> Item12 0.0749 0.156 0.589
#> Item13 0.3351 0.826 0.728
#> Item14 0.5155 0.799 0.970
#> Item15 0.4587 0.820 0.830
#>
#> Test Profile
#> Class 1 Class 2 Class 3
#> Test Reference Profile 6.737 10.329 11.833
#> Latent Class Ditribution 157.000 171.000 172.000
#> Class Membership Distribution 162.321 171.048 166.631
#>
#> Item Fit Indices
#> model_log_like bench_log_like null_log_like model_Chi_sq null_Chi_sq
#> Item01 -265.586 -240.190 -283.343 50.792 86.307
#> Item02 -254.618 -235.436 -278.949 38.363 87.025
#> Item03 -283.074 -260.906 -293.598 44.336 65.383
#> Item04 -205.405 -192.072 -265.962 26.667 147.780
#> Item05 -235.564 -206.537 -247.403 58.053 81.732
#> Item06 -166.780 -153.940 -198.817 25.680 89.755
#> Item07 -252.085 -228.379 -298.345 47.412 139.933
#> Item08 -313.021 -293.225 -338.789 39.591 91.127
#> Item09 -314.543 -300.492 -327.842 28.101 54.700
#> Item10 -307.337 -288.198 -319.850 38.278 63.303
#> Item11 -242.986 -224.085 -299.265 37.802 150.360
#> Item12 -230.028 -214.797 -293.598 30.462 157.603
#> Item13 -280.068 -262.031 -328.396 36.074 132.730
#> Item14 -220.731 -204.953 -273.212 31.556 136.519
#> Item15 -268.593 -254.764 -302.847 27.658 96.166
#> model_df null_df NFI RFI IFI TLI CFI RMSEA AIC CAIC
#> Item01 11 13 0.411 0.304 0.472 0.358 0.457 0.085 28.792 -28.569
#> Item02 11 13 0.559 0.479 0.640 0.563 0.630 0.071 16.363 -40.998
#> Item03 11 13 0.322 0.199 0.387 0.248 0.364 0.078 22.336 -35.025
#> Item04 11 13 0.820 0.787 0.885 0.863 0.884 0.053 4.667 -52.694
#> Item05 11 13 0.290 0.161 0.335 0.191 0.315 0.093 36.053 -21.308
#> Item06 11 13 0.714 0.662 0.814 0.774 0.809 0.052 3.680 -53.681
#> Item07 11 13 0.661 0.600 0.718 0.661 0.713 0.081 25.412 -31.948
#> Item08 11 13 0.566 0.487 0.643 0.568 0.634 0.072 17.591 -39.770
#> Item09 11 13 0.486 0.393 0.609 0.515 0.590 0.056 6.101 -51.259
#> Item10 11 13 0.395 0.285 0.478 0.359 0.458 0.070 16.278 -41.083
#> Item11 11 13 0.749 0.703 0.808 0.769 0.805 0.070 15.802 -41.559
#> Item12 11 13 0.807 0.772 0.867 0.841 0.865 0.060 8.462 -48.899
#> Item13 11 13 0.728 0.679 0.794 0.753 0.791 0.068 14.074 -43.287
#> Item14 11 13 0.769 0.727 0.836 0.803 0.834 0.061 9.556 -47.805
#> Item15 11 13 0.712 0.660 0.804 0.763 0.800 0.055 5.658 -51.703
#> BIC
#> Item01 -17.569
#> Item02 -29.998
#> Item03 -24.025
#> Item04 -41.694
#> Item05 -10.308
#> Item06 -42.681
#> Item07 -20.948
#> Item08 -28.770
#> Item09 -40.259
#> Item10 -30.083
#> Item11 -30.559
#> Item12 -37.899
#> Item13 -32.287
#> Item14 -36.805
#> Item15 -40.703
#>
#> Model Fit Indices
#> Number of Latent class: 3
#> Number of EM cycle: 95
#> value
#> model_log_like -3840.417
#> bench_log_like -3560.005
#> null_log_like -4350.217
#> model_Chi_sq 560.824
#> null_Chi_sq 1580.424
#> model_df 165.000
#> null_df 195.000
#> NFI 0.645
#> RFI 0.581
#> IFI 0.720
#> TLI 0.662
#> CFI 0.714
#> RMSEA 0.069
#> AIC 230.824
#> CAIC -629.587
#> BIC -464.587
# }