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.
# S3 method for exametrika
print(x, digits = 3, fit_indices = c("both", "pattern", "margin"), ...)Prints a formatted summary of the exametrika object to the console, with content varying by object subclass:
Basic descriptive statistics of the test
Eigenvalue analysis results with scree plot
Item-level statistics and psychometric properties
Item statistics for polytomous items
Data structure details including response patterns and weights
Item-item relationship measures (tetrachoric correlations, etc.)
Classical Test Theory reliability measures
Item parameters, ability estimates, and fit indices
Class/Rank profiles, distribution information, and model fit statistics
Cluster profiles, field distributions, and model diagnostics
Network visualizations, parameter estimates, and conditional probabilities
An object of class "exametrika" with various possible subclasses
Integer indicating the number of decimal places to display. Default is 3.
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)
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.
# \donttest{
# 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)
# }
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