Small datasets for use in rstap examples and vignettes.



Simulated data for the longitudinal simulation

  • subj_ID: The subject unique identifier

  • measure_ID: The measurement unique identifier

  • bef_ID The Built Environment Unique identifier

  • measure_date The date at which the subject was measured

  • date_open: The date at which the business opened

  • date_close: The date at which the business may have closed; NA if the business is still open

  • date: The date at which the subject first moved to the location associated with the distance and time with the built environment feature

  • class: The kind of built environment feature. Only one is in the simulated dataset - "Coffee Shop"

  • dist: The distance between the subject and BEF at the date to be associated with the measure ID

  • time: The time for which the subject was "exposed" to the BEF at corresponding distance

Source: Vignette

  • subj_ID: The subject unique identifier

  • Income: Simulated continuous covariate

  • measure_date: The simulated date the subject was measured

  • ran_int: Random intercept generated for the longitudinal I simulation

  • y: Continuous outcome simulated for longitudinal I simulation - meant to be akin to BMI

  • y_bern: Bernoulli outcome simulated

  • sex: Discrete 1-0 covariate simulated to be akin to sex

  • Coffee_Shop: The "true" Coffee Shop Exposure covariate

  • centered_income: scaled and centered version of Income covariate

  • centered_age: scaled and centered version of Age covariate

Source: \href Vignette

  • subj_idThe subject unique identifier

  • yContinuous simulated outcome, meant to be BMI

  • sexdiscrete factor coded "M" for male, "F" for females

Source: Vignette

  • subj_id: The subject unique identifier

  • BEF Built Environment Feature class identifier - only one included in this dataset "Fast_Food"

  • Distance: The euclidean distance between the row's subject and Fast Food restaurant locations'

Source: Vignette