Validate triangle structure before building a development

Check that each (group_var, cohort_var) cohort has a consecutive dev_var sequence within its observed range. Non-consecutive cohorts produce non-consecutive age-to-age links downstream (e.g., 14 -> 17 instead of 14 -> 15), which breaks summary.ATA() / summary.ED() key uniqueness and causes cartesian joins in fit_lr().

This function inspects the raw data without modifying it. Use it before build_triangle() to decide whether to fix the data source, drop offending cohorts, or pass fill_gaps = TRUE to build_triangle().

Usage

validate_triangle(df, group_var, cohort_var = "uym", dev_var = "elap_m")

Arguments

df

A data.frame.

group_var

Grouping variable(s).

cohort_var

A single cohort variable. Default "uym".

dev_var

A single development variable. Default "elap_m".

Value

A data.table of class "TriangleValidation" with one row per cohort containing gaps. Columns:

group_var(s), cohort_var

Cohort identifier.

n_observed

Number of distinct observed dev_var values.

n_expected

max(elap_m) - min(elap_m) + 1 for that cohort.

missing

List column of missing dev_var values.

Returns a zero-row data.table when no gaps are found.

See also

build_triangle()