a matrix whose rows correspond to cases and whose columns
correspond to variables.
y
the responses, possibly a matrix if you want to fit multiple
left hand sides.
wt
an optional vector of weights for performing weighted least squares.
intercept
whether or not an intercept term should be used.
tolerance
the tolerance to be used in the matrix decomposition.
yname
names to be used for the response variables.
Value
A list with the following named components:
coef
the least squares estimates of the coefficients in
the model (\(\beta\) as stated above).
residuals
residuals from the fit.
intercept
indicates whether an intercept was fitted.
qr
the QR decomposition of the design matrix.
Details
If weights are specified then a weighted least squares is performed
with the weight given to the jth case specified by the jth
entry in wt. If any observation has a missing value in any field, that observation
is removed before the analysis is carried out.
This can be quite inefficient if there is a lot of missing data. The implementation is via a modification of the LINPACK subroutines
which allow for multiple left-hand sides.
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
The New S Language.
Wadsworth & Brooks/Cole.