As befits a model-fitting function, the package defines a nearly
complete set of methods for "nestedLogit" objects:
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print()andsummary()print the results for each of the submodels. -
update()re-fits the model, allowing changes to the modelformula,data,subset, andcontrastsarguments. -
coef()returns the coefficients for the predictors in each dichotomy. -
vcov()returns the variance-covariance matrix of the predictors. -
predict()computes predicted probabilities for the response categories, either for the cases in the data or for arbitrary combinations of the predictors; the latter is useful for producing plots to aid interpretation. -
glance()andtidy()are extensions ofbroom::glance.glm()andbroom::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 for the binary logit models in the"nestedLogit"object
These are supplemented by various methods for testing hypotheses about and comparing nested logit models:
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anova()provides analysis-of-deviance Type I (sequential) tests for each dichotomy and for the combined model. When given a sequence of model objects,anova()tests the models against one another in the order specified. -
Anova()usescar::Anova()to provide analysis-of-deviance Type II or III (partial) tests for each dichotomy and for the combined model. -
linearHypothesis()computes Wald tests for hypotheses about coefficients or their linear combinations. -
logLik()returns the log-likelihood and degrees of freedom for the nested-dichotomies logit model. - Through
logLik(), theAIC()andBIC()functions compute the Akaike and Bayesian information criteria model-comparison statistics.
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