em.cens: Fits Univariate Censored Linear Regression Model With Normal...

In CensRegMod: Fits Normal and Student-t Censored Regression Model

Description

Returns EM estimates for right censored regression model (under Normal or Student-t distribution) and calculates some diagnostic measures for detecting influential observations

Usage

em.cens(cc, x, y, nu="NULL", dist="Normal", diagnostic="FALSE", typediag="NULL")

Arguments

cc	Vector of censoring indicators. For each observation: 0 if non-censored, 1 if censored
x	Design matrix
y	Vector with the responde variable
nu	Initial value for the degree of freedon in case of Student-t model (greater than 2)
dist	Distribution to be used for the errors. "Normal", for normal or "T" for Student-t
diagnostic	TRUE or FALSE. Indicates if any diagnostic measure should or not be computed
typediag	If diagnostic=TRUE, typediag indicates which diagnostic measure should be computed. If typediag=1, computes generalized Cook distance (GD) and its decomposition into the generalized Cook distance for the parameter subsets: betas (GDbeta) and sigma2 (GDsigma2). For local influence with case-weight perturbation, set typediag=2. For local influence with scale perturbation, set typediag=3

Details

Despite of this function has been built to deal with right censored response variables, one can easily adapt it to work with left censored responses: set -y and -x to obtain the left censored model fit and any diagnostic measure for it. The specification of the initial value for nu must be made carefully: if the data have many outliers, then you must choose a small value for nu (greater but near to 2), otherwise you can choose a greater value

Value

beta	EM estimatives for regression coefficients
sigma2	EM estimative for the error variance
nu	EM estimative for degree of freedom. Only returned when type="T"
logver	Value of the log-likelihood under the fitted model
SE	Standard error for EM estimators
measure	Vector with the diagnostic measure chosen in typediag. Only returned when diagnostic=TRUE
AIC	AIC model selection criteria
BIC	BIC model selection criteria
EDC	EDC model selection criteria

Author(s)

Monique Bettio Massuia [e-mail], Larrisa Avila Matos [e-mail] and Victor Hugo Lachos [e-mail]

References

Monique B. Massuia, Celso R. Cabral, Larissa A. Matos, Victor H. Lachos. "Influence Diagnostics for Student-t Censored Linear Regression Models"

Examples

 ##see examples in \code{\link{wage.rates}} 

CensRegMod documentation built on May 2, 2019, 4:31 a.m.