# lsfit

0th

Percentile

##### Find the Least Squares Fit

The least squares estimate of $\beta$ 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:
• coefthe least squares estimates of the coefficients in the model ($\beta$ as stated above).
• residualsresiduals from the fit.
• interceptindicates whether an intercept was fitted.
• qrthe 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.

lm which usually is preferable; ls.print, ls.diag.
library(stats) utils::example("lm", echo = FALSE) ##-- Using the same data as the lm(.) example: lsD9 <- lsfit(x = unclass(gl(2, 10)), y = weight) ls.print(lsD9)