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 modelformula
,data
,subset
, andcontrasts
. -
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 ofbroom::glance.glm()
andbroom::tidy.glm()
to obtain compact summaries of anestedLogit
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()
usescar::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()
andBIC()
provide model-comparison statistics.