tidy_local_influence.mixpoissonreg: Tidy Functions for Local Influence Diagnostics for...

In mixpoissonreg: Mixed Poisson Regression for Overdispersed Count Data

Description

Functions to provide tidy outputs of local influence diagnostics. tidy_local_influence.mixpoissonreg provides a tibble::tibble() containing the local influence diagnostics under the chosen perturbation schemes. local_influence_benchmarks.mixpoissonreg provides a tibble::tibble() with a single row and one column for each selected perturbation scheme containing influential benchmarks for each perturbation scheme.

Usage

## S3 method for class 'mixpoissonreg'
tidy_local_influence(
  model,
  perturbation = c("case_weights", "hidden_variable", "mean_explanatory",
    "precision_explanatory", "simultaneous_explanatory"),
  curvature = c("conformal", "normal"),
  direction = c("canonical", "max.eigen"),
  parameters = c("all", "mean", "precision"),
  mean.covariates = NULL,
  precision.covariates = NULL,
  ...
)

## S3 method for class 'mixpoissonreg'
local_influence_benchmarks(
  model,
  perturbation = c("case_weights", "hidden_variable", "mean_explanatory",
    "precision_explanatory", "simultaneous_explanatory"),
  curvature = c("conformal", "normal"),
  direction = c("canonical", "max.eigen"),
  parameters = c("all", "mean", "precision"),
  mean.covariates = NULL,
  precision.covariates = NULL,
  ...
)

Arguments

model	A mixpoissonreg model.
perturbation	a list or vector of perturbation schemes to be returned. The currently available schemes are "case_weights", "hidden_variable", "mean_explanatory", "precision_explanatory", "simultaneous_explanatory". See Barreto-Souza and Simas (2016) for further details.
curvature	the curvature to be returned, 'conformal' for the conformal normal curvature (see Zhu and Lee, 2001 and Poon and Poon, 1999) or 'normal' (see Zhu and Lee, 2001 and Cook, 1986).
direction	the 'max.eigen' returns the eigenvector associated to the largest eigenvalue of the perturbation matrix. The 'canonical' considers the curvatures under the canonical directions, which is known as "total local curvature" (see Lesaffre and Verbeke, 1998). For conformal normal curvatures both of them coincide. The default is 'canonical'.
parameters	the parameter to which the local influence will be computed. The options are 'all', 'mean' and 'precision'. This argument affects the 'case_weights' and 'hidden_variable' perturbation schemes. The default is 'all'.
mean.covariates	a list or vector of characters containing the mean-explanatory variables to be used in the 'mean-explanatory' and 'simultaneous-explanatory' perturbation schemes. If NULL, the 'mean-explanatory' and 'simultaneous-explanatory' perturbation schemes will be computed by perturbing all mean-related covariates. The default is NULL.
precision.covariates	a list or vector of characters containing the precision-explanatory variables to be used in the 'precision-explanatory' and 'simultaneous-explanatory' perturbation schemes. If NULL, the 'precision-explanatory' and 'simultaneous-explanatory' perturbation schemes will be computed by perturbing all precision-related covariates. The default is NULL.
...	Currently not used.

Value

A tibble containing the perturbations schemes as columns.

References

DOI:10.1007/s11222-015-9601-6 doi: 10.1007/s11222-015-9601-6(Barreto-Souza and Simas; 2016)

Cook, R. D. (1986) Assessment of Local Influence. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 48, pp.133-169. https://rss.onlinelibrary.wiley.com/doi/10.1111/j.2517-6161.1986.tb01398.x

Lesaffre, E. and Verbeke, G. (1998) Local Influence in Linear Mixed Models. Biometrics, 54, pp. 570-582.

Poon, W.-Y. and Poon, Y.S. (1999) Conformal normal curvature and assessment of local influence. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 61, pp.51-61. https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00162

Zhu, H.-T. and Lee, S.-Y. (2001) Local influence for incomplete data models. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 63, pp.111-126. https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00279

See Also

glance.mixpoissonreg, augment.mixpoissonreg, tidy.mixpoissonreg, autoplot.mixpoissonreg

Examples

daysabs_prog <- mixpoissonreg(daysabs ~ prog | prog, data = Attendance)
tidy_local_influence(daysabs_prog)

mixpoissonreg documentation built on March 11, 2021, 1:07 a.m.