Functions in Robustbase Package in R
The Robustbase package in R programming provides a number of functions that are used to perform statistics operations. A few of them are listed below:
| Method | Description | |
| 1) | lmrob() | Computes MM-type estimators for linear Regression |
| 2) | covMcd() | Computes robust location and scatter estimation via MCD |
| 3) | colMedians() | Computes the median of rows or columns for a matrix |
| 4) | outlierStats() | Computes robust regression outlier statistics |
| 5) | sigma() | Extracts standard deviation of errors for robust models |
| 6) | lmrob.control() | Tune parameters for lmrob() and auxiliaries |
| 7) | weights.lmrob() | Extracts robustness and model weights |
| 8) | plot.mcd() | Plots diagnostic plot formcd objects |
| 9) | lmrob.lar() | Computes least absolute residuals of L1 regression |
| 10) | predict.lmrob() | Predicts values for robust linear model |
| 11) | ltsReg() | Carries out least trimmed squares robust regression |
| 12) | lmrob.S() | Computes S-estimator for linear regression |
| 13) | smoothWgt() | Computes smooth weight functions |
| 14) | lmrob.fit() | Computes MM-type estimators for regression |
| 15) | lmrob..M..fit() | Computes M-estimators of regression by performing RWLS iterations |
| 16) | plot.lmrob() | Plots diagnostic plot for lmrob objects |
| 17) | summary.lmrob() | Summary methods for lmrob objects |
| 18) | summary.mcd() | Summary methods for mcd objects |
| 19) | nlrob | Computes robust fitting of non-linear regression |
| 20) | plot.lts() | Plots diagnostic plot for lts objects |
| 21) | rrcov.control() | Controls settings for covMcd and ltsReg |
| 22) | summary.lts() | Summary method for lts objects |
| 23) | summary.nlrob() | Summary method for non-linear regression objects |
| 24) | covComed() | Computes the multivariate location and scatter estimator |
| 25) | estimethod() | Extracts the estimation method as a character string from a fitted model |
| 26) | nlrob.control() | Controls the non-linear robust regression algorithm |
| 27) | Sn | Computes robust scale estimator, an efficient alternative to the MAD |
Robustbase Package in R
The Robustbase package in R programming is a collection of functions and methods that are widely used and was designed to do robust statistics. The Robustbase package provides tools for Robust Regression, Multivariate Analysis, and Outlier Detection.
Robust Statistics:
It is a branch of statistics that aims to provide methods that are more resistant to non-normal data compared to traditional statistics.
Robust Regression:
It is a sort of regression analysis that is less sensitive to outliers than standard regression approaches. The Robustbase package includes robust regression functions such as the MM-estimator, S-estimator, and LMS-estimator.
Multivariate Analysis:
The Robustbase package includes robust multivariate analysis utilities. These methods are useful and are widely used when studying datasets with numerous variables because outliers or non-normal data in one variable might have a large impact on the overall analysis.
Outlier Detection:
The Robustbase package includes outlier detection tools which are really helpful that can help find exceptional observations in a dataset that may be impacting the analysis’s conclusions.