Longitudinal continuous-exposure data with a time-2 support gap

A simulated wide-format longitudinal dataset with one row per subject over three time points. It carries a known support gap in the time-2 exposure, so the sequential and continuous-exposure diagnostics can be exercised against ground truth. It serves check_hdr(), check_hdr_seq(), and check_port_seq().

Usage

pos_violations_long

Format

A tibble with 500 rows and 7 columns:

id

Integer subject identifier, 1 to 500.

l0

Numeric baseline covariate.

a1

Numeric time-1 exposure.

l1

Numeric time-1 covariate.

a2

Numeric time-2 exposure, carrying the support gap.

l2

Numeric time-2 covariate.

a3

Numeric time-3 exposure.

Source

Simulated by data-raw/make-datasets.R in the package source repository, https://github.com/r-causal/positively.

Details

The exposures a1, a2, and a3 are continuous, each taking hundreds of distinct values. The planted violation lives at time 2: a2 is drawn on [0, 2] or [4, 6], so it never falls in the open interval (2, 4). The time-1 and time-3 exposures are unconstrained and place values throughout that interval, which makes the gap specific to time 2 rather than a feature of the exposure distribution as a whole. The covariates and exposures follow a simple time-ordered process in which each variable depends on the one before it.