Statistics Toolbox™

robustfit - Robust regression

Syntax

b = robustfit(X,y)
b = robustfit(X,y,wfun,tune)
b = robustfit(X,y,wfun,tune,const)
[b,stats] = robustfit(...)

Description

b = robustfit(X,y) returns a p-by-1 vector b of coefficient estimates for a robust multilinear regression of the responses in y on the predictors in X. X is an n-by-p matrix of p predictors at each of n observations. y is an n-by-1 vector of observed responses. By default, the algorithm uses iteratively reweighted least squares with a bisquare weighting function.

Note   By default, robustfit adds a first column of 1s to X, corresponding to a constant term in the model. Do not enter a column of 1s directly into X. You can change the default behavior of robustfit using the input const, below.

robustfit treats NaNs in X or y as missing values, and removes them.

b = robustfit(X,y,wfun,tune) specifies a weighting function wfun. tune is a tuning constant that is divided into the residual vector before computing weights.

The weighting function wfun can be any one of the following strings:

If tune is unspecified, the default value in the table is used. Default tuning constants give coefficient estimates that are approximately 95% as statistically efficient as the ordinary least-squares estimates, provided the response has a normal distribution with no outliers. Decreasing the tuning constant increases the downweight assigned to large residuals; increasing the tuning constant decreases the downweight assigned to large residuals.

The value r in the weight functions is

r = resid/(tune*s*sqrt(1-h))

where resid is the vector of residuals from the previous iteration, h is the vector of leverage values from a least-squares fit, and s is an estimate of the standard deviation of the error term given by

s = MAD/0.6745

Here MAD is the median absolute deviation of the residuals from their median. The constant 0.6745 makes the estimate unbiased for the normal distribution. If there are p columns in X, the smallest p absolute deviations are excluded when computing the median.

You can write your own M-file weight function. The function must take a vector of scaled residuals as input and produce a vector of weights as output. In this case, wfun is specified using a function handle @ (as in @myfun), and the input tune is required.

b = robustfit(X,y,wfun,tune,const) controls whether or not the model will include a constant term. const is 'on' to include the constant term (the default), or 'off' to omit it. When const is 'on', robustfit adds a first column of 1s to X. When const is 'off', robustfit does not alter X.

[b,stats] = robustfit(...) returns the structure stats, whose fields contain diagnostic statistics from the regression. The fields of stats are:

• ols_s — Sigma estimate (RMSE) from ordinary least squares

• robust_s — Robust estimate of sigma

• mad_s — Estimate of sigma computed using the median absolute deviation of the residuals from their median; used for scaling residuals during iterative fitting

• s — Final estimate of sigma, the larger of robust_s and a weighted average of ols_s and robust_s

• se — Standard error of coefficient estimates

• t — Ratio of b to se

• p — p-values for t

• covb — Estimated covariance matrix for coefficient estimates

• coeffcorr — Estimated correlation of coefficient estimates

• w — Vector of weights for robust fit

• h — Vector of leverage values for least-squares fit

• dfe — Degrees of freedom for error

• R — R factor in QR decomposition of X

The robustfit function estimates the variance-covariance matrix of the coefficient estimates using inv(X'*X)*stats.s^2. Standard errors and correlations are derived from this estimate.

Example

Generate data with the trend y = 10-2*x, then change one value to simulate an outlier:

x = (1:10)';
y = 10 - 2*x + randn(10,1);
y(10) = 0;

Use both ordinary least squares and robust regression to estimate a straight-line fit:

bls = regress(y,[ones(10,1) x])
bls =
  7.2481
 -1.3208

brob = robustfit(x,y)
brob =
  9.1063
 -1.8231

A scatter plot of the data together with the fits shows that the robust fit is less influenced by the outlier than the least-squares fit:

scatter(x,y,'filled'); grid on; hold on
plot(x,bls(1)+bls(2)*x,'r','LineWidth',2);
plot(x,brob(1)+brob(2)*x,'g','LineWidth',2)
legend('Data','Ordinary Least Squares','Robust Regression')

References

[1] DuMouchel, W. H., F. L. O'Brien, "Integrating a Robust Option into a Multiple Regression Computing Environment," Computer Science and Statistics: Proceedings of the 21st Symposium on the Interface, Alexandria, VA, American Statistical Association, 1989.

[2] Holland, P. W., R. E. Welsch, "Robust Regression Using Iteratively Reweighted Least-Squares," Communications in Statistics: Theory and Methods, A6, 1977, pp. 813-827.

[3] Huber, P. J., Robust Statistics, Wiley, 1981.

[4] Street, J. O., R. J. Carroll, D. Ruppert, "A Note on Computing Robust Regression Estimates via Iteratively Reweighted Least Squares," The American Statistician, 42, 1988, pp. 152-154.

See Also

regress, robustdemo

robustdemo

rotatefactors