As befits a model-fitting function, the package defines a nearly
complete set of methods for "nestedLogit"
objects:
-
print()
andsummary()
print the results for each of the submodels. -
update()
re-fits the model, allowing changes to the modelformula
,data
,subset
, andcontrasts
arguments. -
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:
-
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.