Regression, Genetics & Interaction Models

← Back to Domain-Specific Families

Abstractions about generalized regression, genetic effects, segregation, prediction, interaction, variable control, deviance, and structured population models.

10 abstractions in this family — domain-specific abstractions that sit near one another in structural-signature space (k-means over structural-signature embeddings). Each is shown with its short description.

• Additive genetic effects — Allelic contributions to a quantitative phenotype that combine linearly across copies and loci under a declared population and environmental model.
• Best linear unbiased prediction — The minimum-mean-square-error predictor among estimators linear in observations and unbiased for a target random effect under a specified linear mixed model.
• Complex segregation analysis — Fit competing pedigree transmission models to phenotypic family data to test whether a trait distribution is consistent with a major Mendelian locus alongside polygenic, environmental, and ascertainment effects.
• Controlling for a variable — A design or analysis operation that compares or models observations at fixed or adjusted values of a variable to block a specified noncausal association, with validity determined by the causal structure.
• Deviance (statistics) — A likelihood-based goodness-of-fit quantity comparing a fitted statistical model with a saturated model, conventionally twice their maximized log-likelihood difference.
• Generalized least squares — A linear-model estimator that minimizes residuals in the inverse-covariance metric when errors have known nonconstant variance or correlation.
• Hierarchical generalized linear model — An extension of generalized linear modeling that represents clustered or multilevel responses through random effects and linked conditional distributions that can be nonnormal.
• Interaction (statistics) — A model relation in which the association or effect of one predictor on an outcome changes with the level of another predictor.
• Next-generation matrix — A matrix whose entries give expected new cases or offspring of each type produced by one individual of another type, with spectral radius yielding a reproduction threshold.
• Principle of marginality — The modeling principle that an interaction term should ordinarily be accompanied by its constituent lower-order main effects, whose meanings are marginal across the interacting variable.