| lsfit {base} | R Documentation |
The least squares estimate of \beta in the model
\bold{Y} = \bold{X \beta} + \bold{\epsilon}
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.
lsfit(x, y, wt, intercept=TRUE, tolerance=1e-07, yname=NULL)
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. |
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. |
lm which usually is preferable;
ls.print, ls.diag, rreg.
##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = as.numeric(gl(2,10,20)), y = weight)
ls.print(lsD9)