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PowRPriori 0.2.0

This is a large update, including complete refactoring of underlying mechanisms related to how the package handles experimental designs and the addition of mean centering as well as p-value adjustment. The way study designs are defined has been changed in a major way, which also led to changes of other functions in the package to work correctly with the new functionality. As a result, the process of doing power analyses should now be more streamlined and intuitive as well as support a larger variety of research designs. The vignette has also been adapted to reflect these changes.

Breaking Changes

• The id parameter in define_design has been removed. Grouping variables and hierarchical structures are now defined entirely by a new sample_size parameter, providing a much cleaner and more intuitive syntax
• The between parameter in define_design() has been changed. Now, the predictor can directly reference the desired analysis unit defined in sample_size
• Some functions were changed in order to support the new mean centering functionality:
  • fixed_effects_from_average_outcome now has a new function parameter center. It defaults to TRUE and controls how the centering of predictors is handled. See function documentation for more details
  • The n_is_total in power_sim has been removed. This choice has been made obsolete by the new sample_size parameter in define_design
  • The plot_sim_model function now also has a center parameter that works identically to the one added in power_sim
• In cases where users supply the fixed effects manually to power_sim (i.e. do not use fixed_effects_from_average_outcome) and an interaction effect is present in the formula, power_sim now stops execution and alerts the user to explicitly choose if centering should be applied

New Features & Parameters

• Added automated p-value adjustment (Benjamini-Hochberg by default) in power_sim when testing multiple effects for power simultaneously. See function documentation for details
• Added a mean centering functionality for predictors. Users can now configure if the predictors should be centered in the power_sim function PowRPriori automatically chooses either grand-mean centering or within-cluster centering (between-subject predictors are centered on the grand mean, within-subject predictors are centered on the cluster mean). See function documentation and vignette for details
• power_sim has new parameters: adjust_p_value, along and center
  • adjust_p_value controls the method of p-value adjustment when multiple parameters are specified in test_parameter
  • along defines which variable specified in the sample_size parameter in define_design should be incremented after each simulation course (defaults to NULL, in which case the function increments the lowest analysis unit)
  • center controls if mean centering should be applied to the predictors. Defaults to auto, in which case the simulation engine tries to detect the appropriate centering method
• Improved the underlying data simulation engine (updated .create_design_matrix and implemented a new function .center_predictors) to automatically and robustly handle complex hierarchical and crossed designs, as well as automatic mean-centering
• Added a new parameter n to plot_sim_model, which allows the configuration of the sample size to be simulated for the plot and defaults to the sample size of the lowest analysis level otherwise
• Added a new internal function .center_predictors which handles the predictor mean centering

General improvements / changes

• The internal .create_design_matrix was updated to correctly handle the new objects created by define_design
• Updated the vignette to reflect and incorporate all changes and new features

Bug fixes

• Fixed an issue where plot_sim_model calculated the wrong average sample value for the data when using type="data"

PowRPriori 0.1.2

This a smaller update, improving the vignette and fixing a critical bug in the functions providing code snippets for model specification as well as some smaller bugs.

Bug fixes

Critical bug fix

• Fixed incorrect handling of certain nested designs in get_fixed_effects_structrue and get_random_effects_structure so that the functions now consistently produce the correct code snippets

Miscellaneous smaller bugs

• Fixed plotting of sample data where the incorrect regression lines were drawn when using plot_sim_model with type = "data"
• Corrected the possible values of the type parameter in the plot_sim_model function when using it with an lme4-style formula object
• Fixed incorrect console output when the data simulation with power_sim reached or exceeded max_simulation_steps
• Fixed a bug where the simulation stop condition did not work correctly in some cases where more than the defined n_issue_stop_prop proportion of models had fitting issues

General improvements

• Vastly extended the vignette to provide more background information on the functions, include more additional use-cases and further improve the overall readability
• Improved the output of power_sim to include more information when simulating a nested design
• Improved the output of power_sim to incorporate the percentage of model fits with issues in relation to the total number of models fit

PowRPriori 0.1.1

• Initial CRAN submission

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.