print.exametrika {exametrika} | R Documentation |
Print Method for Exametrika Objects
Description
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, ...)
Arguments
x |
An object of class "exametrika" with various possible subclasses |
digits |
Integer indicating the number of decimal places to display. Default is 3. |
... |
Additional arguments passed to print methods (not currently used) |
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.
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/IRM
Cluster profiles, field distributions, and model diagnostics
- LDLRA/LDB/BINET
Network visualizations, parameter estimates, and conditional probabilities
Examples
# Print IRT analysis results with 4 decimal places
result <- IRT(J15S500)
print(result, digits = 4)
# Print Latent Class Analysis results
result_lca <- LCA(J15S500, ncls = 3)
print(result_lca)