| local.MNB {MNB} | R Documentation |
Local influence
Description
It performes influence analysis by a local influence approach by Cook (1986). It is considering three perturbation schemes: Case weights, explanatory variable and dispersion parameter perturbation. Another procedure which considering is the total local curvature corresponding to the ith element approach by Lesaffre and Verbeke (1998).
Usage
local.MNB(star, formula, dataSet, schemes, cova, plot = TRUE)
Arguments
star |
Initial values for the parameters to be optimized over. |
formula |
The structure matrix of covariates of dimension n x p (in models that include an intercept x should contain a column of ones). |
dataSet |
data |
schemes |
Perturbation scheme. Possible values: "cases" for Case weights perturbation on ith subject or cluster, "cases.obs" for Case weights perturbation on jth measurement taken on the ith subject or cluster, "cova.pertu" for explanatory variable perturbation, "dispersion" for dispersion parameter perturbation |
cova |
Indicator which column from dataset (continuous covariate) must be perturbation. |
plot |
TRUE or FALSE. Indicates if a graph should be plotted. |
Details
The function returns a list (L) with the eigenvector associated with the maximum curvature, the total local influence and the index plot.
Value
L and graphics
Author(s)
Jalmar M F Carrasco <carrascojalmar@gmail.com>, Cristian M Villegas Lobos <master.villegas@gmail.com> and Lizandra C Fabio <lizandrafabio@gmail.com>
References
Cook, R. D. (1986). Assessment of local influence (with discussion). Journal of the Royal Statistical Society B, 48, 133-169.
Lesaffre E. and Verbeke G. (1998). Local influence in linear mixed models. Biometrics, 54, 570-582.
Fabio, L. C., Villegas, C., Carrasco, J. M. F., and de Castro, M. (2021). D Diagnostic tools for a multivariate negative binomial model for fitting correlated data with overdispersion. Communications in Statistics - Theory and Methods. https://doi.org/10.1080/03610926.2021.1939380.
Examples
data(seizures)
head(seizures)
star <-list(phi=1, beta0=1, beta1=1, beta2=1, beta3=1)
local.MNB(formula=Y ~ trt + period + trt:period + offset(log(weeks)),star=star,dataSet=seizures,
schemes="weight",plot=FALSE)
local.MNB(formula=Y ~ trt + period + trt:period + offset(log(weeks)),star=star,dataSet=seizures,
schemes="weight.obs",plot=FALSE)
local.MNB(formula=Y ~ trt + period + trt:period + offset(log(weeks)),star=star,dataSet=seizures,
schemes="dispersion",plot=FALSE)