Sliding-window module

Welcome to the section on the sliding-window! You will find a detailed description of the rationale behind this method and detailed instructions on how you can use it on your own.

Warning

Note that sihnpy provides data to practice using the sliding-window module. While the PREVENT-AD participants are used, the data available for this method is simulated data (i.e., the numbers observed are fake; they were randomly generated to fit the purpose of the tutorials). As a general rule for sihnpy, and especially for this module, only use the data provided to help you practice using the module, not to conduct or publish actual research.

• Introduction to sliding-window analysis
  • Rationale
  • Definitions
  • Data type
  • Use cases and limitations
  • Logistical considerations
  • Future considerations
  • References
• Sliding-window analysis
  • Practice data
  • Deriving the sliding windows
    • 1. Preparing the data
    • 2. Calculating the number of windows
    • 3. Building the windows
    • 4. Reconstructing data in each window
    • 5. Summary statistics for each window
    • 6. Exporting data
  • tl;dr
  • References