nestedLogit (version 0.3.2)

Effect.nestedLogit: Effect Displays for Nested Logit Models

Description

Computes effects (in the sense of the effects package---see, in particular, Effect)---for "nestedLogit" models, which then can be used with other functions in the effects package, for example, predictorEffects and to produce effect plots.

Usage

# S3 method for nestedLogit
Effect(
  focal.predictors,
  mod,
  confidence.level = 0.95,
  fixed.predictors = NULL,
  ...
)

Value

an object of class "effpoly" (see Effect).

Arguments

focal.predictors

a character vector of the names of one or more of the predictors in the model, for which the effect display should be computed.

mod

a "nestedLogit" model object.

confidence.level

for point-wise confidence bands around the effects (the default is 0.95).

fixed.predictors

controls the values at which other predictors are fixed; see Effect for details; if NULL (the default), numeric predictors are set to their means, factors to their distribution in the data.

...

optional arguments to be passed to the Effect method for binary logit models (fit by the glm function).

Author

John Fox

References

John Fox and Sanford Weisberg (2019). An R Companion to Applied Regression, 3rd Edition. Sage, Thousand Oaks, CA.

John Fox, Sanford Weisberg (2018). Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals. Journal of Statistical Software, 87(9), 1-27.

See Also

Effect, plot.effpoly, predictorEffects

Examples

data("Womenlf", package = "carData")
comparisons <- logits(work=dichotomy("not.work",
                                     working=c("parttime", "fulltime")),
                      full=dichotomy("parttime", "fulltime"))
m <- nestedLogit(partic ~ hincome + children,
                   dichotomies = comparisons,
                   data=Womenlf)
peff.women <- effects::predictorEffects(m)
plot(peff.women)
plot(peff.women, axes=list(y=list(style="stacked")))
summary(peff.women)

dichots <- logits(AB_CD = dichotomy(c("A", "B"), c("C", "D")),
                  A_B   = dichotomy("A", "B"),
                  C_D   = dichotomy("C", "D"))
m.health <- nestedLogit(product4 ~ age + gender*household + position_level,
                        dichotomies = dichots, data = HealthInsurance)
eff.gen.hh <- effects::Effect(c("gender", "household"), m.health,
                              xlevels=list(household=0:7))
eff.gen.hh
plot(eff.gen.hh, axes=list(x=list(rug=FALSE)))
plot(eff.gen.hh, axes=list(x=list(rug=FALSE), 
                           y=list(style="stacked")))