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Generate mock data with the native backend

Source: R/mock_spec_native.R
generate_mock_data_native.Rd

generate_mock_data_native() consumes a validated mock_spec and generates baseline valid values using MockData's native R backend. This milestone does not yet apply missing-code injection, garbage values, diagnostics, or optional simstudy features.

Usage

generate_mock_data_native(spec, n, seed = NULL)

Arguments

spec

A mock_spec object.

n

Non-negative whole number of rows to generate.

seed

Optional whole-number seed. Generation uses an isolated L'Ecuyer-CMRG sub-stream and restores the caller's RNG state and kind on exit, so output is reproducible for a given seed and package version without perturbing the caller's RNG.

Value

A data frame with n rows and one column per non-derived mock_spec variable (type = "survival" and type = "formula" variables are appended afterwards by generate_survival_dates() and evaluate_mock_formulas()).

Details

The native backend is the default MIT-licensed baseline engine. It currently supports uniform continuous variables, truncated-normal continuous variables, truncated-exponential continuous variables, categorical variables, and uniform calendar dates. Missing codes, garbage values, and diagnostics are intentionally handled by postprocess_mock_data() so that all backends share the same audit trail.

type = "formula" variables are skipped by this backend — they carry no distribution to sample from. They are computed post-baseline by evaluate_mock_formulas(), which evaluates each formula over this function's output columns in dependency order. A spec containing only formula variables still returns an n-row, zero-column data frame here (see evaluate_mock_formulas() for how columns are appended afterwards). A stray formula field on a variable of some other type remains an unsupported/fallback trigger.

See also

mock_spec(), mock_continuous(), mock_spec_from_recodeflow(), postprocess_mock_data(), generate_mock_data_simstudy(), evaluate_mock_formulas()

Other mock generation APIs: create_mock_data(), generate_mock_data_simstudy(), generate_survival_dates(), postprocess_mock_data()

Examples

spec <- mock_spec(
  mock_spec_continuous("age", range = c(18, 85), rtype = "integer"),
  mock_spec_categorical(
    "smoking",
    levels = c("never", "former", "current"),
    proportions = c(0.5, 0.3, 0.2)
  )
)
data <- generate_mock_data_native(spec, n = 10, seed = 1)
head(data)
#>   age smoking
#> 1  63 current
#> 2  47 current
#> 3  79   never
#> 4  82  former
#> 5  74   never
#> 6  41   never