Performs an L1 regression on a matrix of explanatory variables and a vector of responses.
l1fit(x, y, intercept = TRUE, tolerance = 1e-07, print.it = TRUE)list defining the regression (compare with function lsfit).
vector of coefficients.
residuals from the fit.
character strings stating whether a non-unique solution is possible,
or if the x matrix was found to be rank deficient.
vector or matrix of explanatory variables. Each row corresponds to an
observation and each column to a variable. The number of rows of x should
equal the number of data values in y, and there should be fewer columns
than rows. Missing values are not allowed.
numeric vector containing the response. Missing values are not allowed.
logical flag. If TRUE, an intercept term is included in
the regression model.
numerical value used to test for singularity in the regression.
logical flag. If TRUE, then warnings about non-unique solutions
and rank deficiency are given.
The Barrodale-Roberts algorithm, which is a specialized linear programming algorithm, is used.
Barrodale, I., Roberts, F.D.K. (1973). An improved algorithm for discrete L1 linear approximations. SIAM Journal of Numerical Analysis 10, 839-848.
Barrodale, I., Roberts, F.D.K. (1974). Solution of an overdetermined system of equations in the L1 norm. Communications of the ACM 17, 319-320.
Bloomfield, P., Steiger, W.L. (1983). Least Absolute Deviations: Theory, Applications, and Algorithms. Birkhauser, Boston, Mass.