R: Find the Least Squares Fit

Find the Least Squares Fit

Usage

lsfit(x, y, wt, 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 matrix valued 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`	an unused parameter for compatibility.

Description

The least squares estimate of b in the model

y = X b + e

is found. 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 (stated below).
`residuals`	residuals from the fit.
`intercept`	indicates whether an intercept was fitted.
`qr`	the QR decomposition of the design matrix.

Examples



##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = codes(gl(2,10)), y = weight)
ls.print(lsD9)

##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = codes(gl(2,10)), y = weight)
ls.print(lsD9)