Skip to contents

As befits a model-fitting function, the package defines a nearly complete set of methods for class nestedLogit objects:

  • print(), summary(): prints the results for each of the submodels
  • update() re-fits a model, allowing changes in the model formula, data, subset, and contrasts.
  • coef() returns the coefficients for the predictors in each dichotomy
  • vcov() returns the variance-covariance matrix of the predictors
  • predict() obtains predicted probabilities for the response categories, useful for producing plots to aid interpretation.
  • glance(), tidy() are extensions of broom::glance.glm() and broom::tidy.glm() to obtain compact summaries of a nestedLogit model object`.
  • plot() provides basic plots of the predicted probabilities over a range of values of the predictor variables.
  • models() is an extractor function to extract the separate models binary logit models from the "nestedLogit" object

These are supplemented by various methods for testing hypotheses about nested logit models:

  • anova() provides ANOVA Type I (sequential) tests for each dichotomy and for the combined model. When given a sequence of objects, anova() tests the models against one another in the order specified.
  • Anova() uses car::Anova() to provide ANOVA Type II (partial) tests for each dichotomy and for the combined model.
  • linearHypothesis() gives Wald tests for hypotheses about coefficients or their linear combinations
  • logLike() returns the log-likelihood and degrees of freedom for the nested-dichotomies model;
  • through the last, AIC() and BIC() provide model-comparison statistics.