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The least squares estimate of
lsfit(x, y, wt = NULL, intercept = TRUE, tolerance = 1e-07,
yname = NULL)
a matrix whose rows correspond to cases and whose columns correspond to variables.
the responses, possibly a matrix if you want to fit multiple left hand sides.
an optional vector of weights for performing weighted least squares.
whether or not an intercept term should be used.
the tolerance to be used in the matrix decomposition.
names to be used for the response variables.
A list with the following named components:
the least squares estimates of the coefficients in
the model (
residuals from the fit.
indicates whether an intercept was fitted.
the QR decomposition of the design matrix.
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.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
# NOT RUN {
##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = unclass(gl(2, 10)), y = weight)
ls.print(lsD9)
# }
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