Documentation |
Robust regression
b = robustfit(X,y)
b = robustfit(X,y,wfun,tune)
b = robustfit(X,y,wfun,tune,const)
[b,stats] = robustfit(...)
b = robustfit(X,y) returns a (p + 1)-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:
Weight Function | Equation | Default Tuning Constant |
---|---|---|
'andrews' | w = (abs(r)<pi) .* sin(r) ./ r | 1.339 |
'bisquare' (default) | w = (abs(r)<1) .* (1 - r.^2).^2 | 4.685 |
'cauchy' | w = 1 ./ (1 + r.^2) | 2.385 |
'fair' | w = 1 ./ (1 + abs(r)) | 1.400 |
'huber' | w = 1 ./ max(1, abs(r)) | 1.345 |
'logistic' | w = tanh(r) ./ r | 1.205 |
'ols' | Ordinary least squares (no weighting function) | None |
'talwar' | w = 1 * (abs(r)<1) | 2.795 |
'welsch' | w = exp(-(r.^2)) | 2.985 |
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 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 and b becomes a (p + 1)-by-1 vector . When const is 'off', robustfit does not alter X, then b is a p-by-1 vector.
[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
resid — Residual
rstud — Studentized residual (see regress for more information)
se — Standard error of coefficient estimates
covb — Estimated covariance matrix for coefficient estimates
coeffcorr — Estimated correlation of coefficient estimates
t — Ratio of b to se
p — p-values for t
w — Vector of weights for robust fit
R — R factor in QR decomposition of X
dfe — Degrees of freedom for error
h — Vector of leverage values for least-squares fit
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.
[1] DuMouchel, W. H., and 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., and 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. Hoboken, NJ: John Wiley & Sons, Inc., 1981.
[4] Street, J. O., R. J. Carroll, and D. Ruppert. "A Note on Computing Robust Regression Estimates via Iteratively Reweighted Least Squares." The American Statistician. Vol. 42, 1988, pp. 152–154.