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Validate model and data inputs before running diagnostics (deprecated)

Source: R/validation.R ยท Help: ?validateTestInputs

Deprecated since 0.14.0: an internal helper of the package, which leaves the exports in 1.0.0. It still works and warns.

Usage

validateTestInputs(model, data, test_name, min_obs = 10)

Arguments

model
A fitted model created by stats::lm() or stats::glm().
data
A data.frame containing the variables used to fit model.
test_name
Character scalar naming the diagnostic that is about to run; included in error messages for clarity.
min_obs
Non-negative integer giving the minimum sample size accepted by the diagnostic. Defaults to 10.

Value

Invisibly returns TRUE when validation succeeds. Execution stops with an informative error when any check fails.

Details

Provides the compatibility wrapper used by the public testing interface to ensure that fitted-model objects and their associated data satisfy minimal quality requirements. The helper guards against the most common issues that invalidate heteroscedasticity tests and produces actionable error messages that reference the calling diagnostic.

The routine performs four layers of validation:

  1. confirm that model inherits from lm or glm and that its coefficients and residuals are finite;
  2. verify that data is a data.frame with at least min_obs rows;
  3. ensure the residual vector is available, finite, and aligned with the supplied data;
  4. emit warnings when studentised residuals exceed five standard deviations in absolute value or when the dataset is very large (more than 10,000 observations).

Results are cached (when the digest package is installed) so repeated calls with unchanged inputs return immediately.

References

Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models (3rd ed.). SAGE.

Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley.

See also

checkModel(), rvalidateModelInputs(), rvalidateTestRequirements()

Examples

data(mtcars)
lm_fit <- stats::lm(mpg ~ wt + hp, data = mtcars)
validateTestInputs(lm_fit, mtcars, "white")


noisy <- mtcars
noisy$mpg[1] <- 100
outlier_fit <- stats::lm(mpg ~ wt + hp, data = noisy)
validateTestInputs(outlier_fit, noisy, "white")