PLreg (version 0.4.1)

influence: Influence Diagnostics for PLreg Objects

Description

The influence function provides two influence measures and the generalized leverage for power logit regression models.

Usage

influence(model, graph = TRUE, ...)

Value

influence returns a list with three objects:

case.weights

The values of \(h_{max}\) eigenvector based on case weights perturbation scheme (see Queiroz and Ferrari (2022)).

totalLI

The total local influence (see Lesaffre and Verbeke (1998))

GL

The diagonal elements of the generalized leverage matrix.

Arguments

model

fitted model object of class "PLreg".

graph

logical. If graph = TRUE the plots are shown, if graph = FALSE the plots are not shown. Default is graph = TRUE.

...

currently not used.

References

Queiroz, F. F. and Ferrari, S. L. P. (2022). Power logit regression for modeling bounded data. arXiv:2202.01697.

See Also

PLreg, residuals.PLreg, envelope, plot.PLreg

Examples

data("Firm")

fitPL <- PLreg(firmcost ~ sizelog + indcost | sizelog + indcost,
              data = Firm, family = "SLASH", zeta = 2.13)
# \donttest{
influence_measures = influence(fitPL, graph = FALSE)
plot(influence_measures$case.weights, type = "h", ylim = c(0,1))
plot(influence_measures$totalLI, type = "h", ylim = c(0,6))
plot(Firm$sizelog, influence_measures$GL, pch = "+")# }