Presents either the fitting of model heuristics or the evolution of parameters on a graph
# S3 method for auto_edina autoplot( object, type = c("selection", "guessing", "slipping", "evolution"), ... )
Arguments
| object | An |
|---|---|
| type | Kind of graph to display. Valid types: |
| ... | Not used. |
Value
A ggplot2 object.
See also
Examples
if(requireNamespace("simcdm", quietly = TRUE)) { # Set a seed for reproducibility set.seed(1512) # Setup data simulation parameters N = 2 # Number of Examinees / Subjects J = 10 # Number of Items K = 2 # Number of Skills / Attributes # Note: # Sample size and attributes have been reduced to create a minimally # viable example that can be run during CRAN's automatic check. # Please make sure to have a larger sample size... # Assign slipping and guessing values for each item ss = gs = rep(.2, J) # Simulate an identifiable Q matrix Q = simcdm::sim_q_matrix(J, K) # Simulate subject attributes subject_alphas = simcdm::sim_subject_attributes(N, K) # Simulate items under the DINA model items_dina = simcdm::sim_dina_items(subject_alphas, Q, ss, gs) # \donttest{ # Requires at least 15 seconds of execution time. # Three EDINA models will be fit with increasing number of attributes. model_set_edina = auto_edina(items_dina, k = 2:4) # Visualize results results autoplot(model_set_edina, type = "selection") # Equivalent to: model_selection_graph(model_set_edina) # View model parameters autoplot(model_set_edina, type = "guessing") # Or directly call with: parameter_evolution_graph(model_set_edina, type = "guessing") # } }#>#>#>#>#>#>#>
