Robust package provides a library of robust methods including regression.
It derives from the Latin robustus , meaning “strength.”
- A robust product can be one that doesn’t break easily.
- Robust is also sometimes used to mean a product or system of products designed with a full complement of capabilities.
Robust regression is an alternative to least squares regression when data are contaminated with outliers or influential observations, and it can also be used for the purpose of detecting influential observations. Robust regression is an iterative procedure that seeks to identify outliers and minimize their impact on the coefficient estimates. The amount of weighting assigned to each observation in robust regression is controlled by a special curve called an influence function.
Compute an analysis of variance table for one or more robust linear model fits.
##S3 method for class’lmRob’
anova(object, …, test = c(“RF”, “RWald”))
##S3 method for class’lmRoblist’
anova(object, const, ipsi, yc, test = c(“RWald”, “RF”), …)
- objectan lmRob object
- …additional arguments required by the generic anova function. If…containsadditional robustly fitted linear models then the functionanova.lmRoblistisdispatched.
- consta numeric value containing the tuning constant.
- ipsian integer value specifying the psi-function.
- yca numeric value containing the tuning constant.
- testa single character value specifying which test should be computed in the Anovatable. The possible choices are “RWald” and “RF”.