| lsfit {stats} | R Documentation |
Find the Least Squares Fit
Description
The least squares estimate of \beta in the model
\bold{Y} = \bold{X \beta} + \bold{\epsilon}
is found.
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 j-th case specified by the j-th
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 ( |
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
lm which usually is preferable;
ls.print, ls.diag.
Examples
##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = unclass(gl(2, 10)), y = weight)
ls.print(lsD9)