| % File src/library/stats/man/lsfit.Rd |
| % Part of the R package, https://www.R-project.org |
| % Copyright 1995-2007 R Core Team |
| % Distributed under GPL 2 or later |
| |
| \name{lsfit} |
| \title{Find the Least Squares Fit} |
| \usage{ |
| lsfit(x, y, wt = NULL, intercept = TRUE, tolerance = 1e-07, |
| yname = NULL) |
| } |
| \alias{lsfit} |
| \arguments{ |
| \item{x}{a matrix whose rows correspond to cases and whose columns |
| correspond to variables.} |
| \item{y}{the responses, possibly a matrix if you want to fit multiple |
| left hand sides.} |
| \item{wt}{an optional vector of weights for performing weighted least squares.} |
| \item{intercept}{whether or not an intercept term should be used.} |
| \item{tolerance}{the tolerance to be used in the matrix decomposition.} |
| \item{yname}{names to be used for the response variables.} |
| } |
| \description{ |
| The least squares estimate of \bold{\eqn{\beta}{b}} in the model |
| \deqn{\bold{Y} = \bold{X \beta} + \bold{\epsilon}}{y = X b + e} |
| is found. |
| } |
| \details{ |
| If weights are specified then a weighted least squares is performed |
| with the weight given to the \emph{j}th case specified by the \emph{j}th |
| entry in \code{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: |
| \item{coef}{the least squares estimates of the coefficients in |
| the model (\bold{\eqn{\beta}{b}} as stated above).} |
| \item{residuals}{residuals from the fit.} |
| \item{intercept}{indicates whether an intercept was fitted.} |
| \item{qr}{the QR decomposition of the design matrix.} |
| } |
| \references{ |
| Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) |
| \emph{The New S Language}. |
| Wadsworth & Brooks/Cole. |
| } |
| \seealso{ |
| \code{\link{lm}} which usually is preferable; |
| \code{\link{ls.print}}, \code{\link{ls.diag}}. |
| } |
| \examples{ |
| \dontshow{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) |
| } |
| \keyword{regression} |