InfDiag {ARCensReg} | R Documentation |
It performs influence diagnostic by a local influence approach (Cook, 1986) with three possible perturbations schemes: response perturbation (y), scale matrix perturbation (Sigma) or explanatory variable perturbation (x). A benchmark value is calculated that depends on k.
InfDiag(theta,yest,yyest,x,k=3,plots=T,indpar=rep(1,length(theta)),
perturbation ='y',indcolx = rep(1,ncol(x)))
theta |
Vector of estimated parameters. |
yest |
Vector of responses of length |
yyest |
Should be the value yyest of the ARCensReg function in the case that at least one observation is censored. Otherwise, must be |
x |
Matrix of covariates of dimension |
k |
Constant to be used in the benchmark calculation: |
plots |
TRUE or FALSE. Indicates if a graph should be plotted. |
indpar |
Vector of length equal to the number of parameters, with each element 0 or 1 indicating if the respective parameter should be taking into account in the influence calculation. |
perturbation |
Perturbation scheme. Possible values: "y" for response perturbation, "Sigma" for scale matrix perturbation or "x" for explanatory variable perturbation. |
indcolx |
If |
The function returns a vector of length n with the aggregated contribution (M0) of all eigenvectors of the matrix associated with the normal curvature. For details see (Schumacher et. al., 2016).
M0
Fernanda L. Schumacher <fernandalschumacher@gmail.com>, Victor H. Lachos <hlachos@ime.unicamp.br> and Christian E. Galarza <cgalarza88@gmail.com>
Maintainer: Fernanda L. Schumacher <fernandalschumacher@gmail.com>
Cook, R. D. (1986). Assessment of local influence. Journal of the Royal Statistical Society, Series B, 48, 133-169.
Schumacher, F. L., Lachos, V. H. & Vilca-Labra, F. E. (2016) Influence diagnostics for censored regression models with autoregressive errors. Submitted.
Zhu, H. & Lee, S. (2001). Local influence for incomplete-data models. Journal of the Royal Statistical Society, Series B, 63, 111-126.
## Not run:
#generating the data
set.seed(12341)
x = cbind(1,runif(100))
dat = rARCens(n=100,beta = c(1,-1),pit = c(.4,-.2),sig2=.5,
x=x,cens='left',pcens=.05)
#creating an outlier
dat$data$y[40] = 5
plot.ts(dat$data$y)
#fitting the model
fit = ARCensReg(cc=dat$data$cc,y=dat$data$y,x,p=2,cens='left',
tol=0.001,show_se=F)
#influence diagnostic
M0y = InfDiag(theta=fit$res$theta, yest=fit$yest, yyest=fit$yyest,
x=x, k = 3.5, perturbation = "y")
M0Sigma = InfDiag(theta=fit$res$theta, yest=fit$yest, yyest=fit$yyest,
x=x, k = 3.5, perturbation = "Sigma")
M0x = InfDiag(theta=fit$res$theta, yest=fit$yest, yyest=fit$yyest,
x=x, k = 3.5, perturbation = "x",indcolx =c(0,1))
#perturbation on a subset of parameters
M0y1 = InfDiag(theta=fit$res$theta, yest=fit$yest, yyest=fit$yyest,
x=x, k = 3.5, perturbation = "y",indpar=c(1,1,0,0,0))
M0y2 = InfDiag(theta=fit$res$theta, yest=fit$yest, yyest=fit$yyest,
x=x, k = 3.5, perturbation = "y",indpar=c(0,0,1,1,1))
plot(M0y1,M0y2)
abline(v = mean(M0y1)+3.5*sd(M0y1),h = mean(M0y2)+3.5*sd(M0y2),lty=2)
## End(Not run)