lsfit
Find the Least Squares Fit
The least squares estimate of $b$ in the model $$\bold{Y} = \bold{X \beta} + \bold{\epsilon}$$ is found.
 Keywords
 regression
Usage
lsfit(x, y, wt = NULL, intercept = TRUE, tolerance = 1e07, yname = NULL)
Arguments
 x
 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.
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 lefthand sides.
Value

A list with the following named components:
 coef
 the least squares estimates of the coefficients in the model ($b$ as stated above).
 residuals
 residuals from the fit.
 intercept
 indicates whether an intercept was fitted.
 qr
 the QR decomposition of the design matrix.
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
See Also
Examples
library(stats)
## Using the same data as the lm(.) example:
lsD9 < lsfit(x = unclass(gl(2, 10)), y = weight)
ls.print(lsD9)
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