# robust.lm.onesided: Robust Regression using One-Sided Huber Function In surveyoutliers: Helps Manage Outliers in Sample Surveys

## Description

This function performs robust regression using M-estimation using the one-sided Huber function, with residuals truncated at Q / (data\$gregwt-1) where data\$gregwt is the generalized regression weight.

## Usage

 `1` ```robust.lm.onesided(formula, data, Q, Qname, maxit = 100, stop = F) ```

## Arguments

 `formula` The regression formula (e.g. income ~ employment + old.turnover if income is survey variable and employment and old.turnover are auxiliary variables). `data` A data frame including the variables in formula, and gregwt (generalized regression estimator weight), and regwt (weight to be used in regression - will be set to 1 if missing). `Q` The tuning parameter where large Q corresponds to no outlier treatment, and small Q corresponds to many outliers being flagged. `Qname` Gives a variable name on data which contains a separate tuning parameter Q for every observation (either Q or Qname should be specified but not both). `maxit` The maximum number of iterations. `stop` Set to T to open a browser window (for debugging purposes)

## Details

Uses iteratively reweighted least squares.

## Value

The final linear model fit (an object of class "lm").

## References

Clark, R. G. (1995), "Winsorisation methods in sample surveys," Masters thesis, Australian National University, http://hdl.handle.net/10440/1031.

Kokic, P. and Bell, P. (1994), "Optimal winsorizing cutoffs for a stratified finite population estimator," J. Off. Stat., 10, 419-435.

## Examples

 `1` ```robust.lm.onesided(formula=y~x1+x2,data=survdat.example,Q=250) ```

### Example output

```Call:
lm(formula = formula, data = data, weights = irls.w)

Coefficients:
(Intercept)           x1           x2
-13.345        5.912       11.957
```

surveyoutliers documentation built on May 2, 2019, 2:44 p.m.