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 = 1e-07, 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 left-hand 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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