ols

Fit linear regression model

Description

Returns an object of class "ols" that represents a linear model fit.

Usage

ols(formula, data, subset, na.action, method = "qr", tol = 1e-7, maxiter = 100,
  model = FALSE, x = FALSE, y = FALSE, contrasts = NULL, ...)

Arguments

Argument	Description
formula	an object of class " (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data , the variables are taken from environment(formula) , typically the environment from which ols is called.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
na.action	a function which indicates what should happen when the data contain NA s. The default is set by the na.action setting of options , and is na.fail if that is unset.
method	the least squares fitting method to be used; the options are "cg" (conjugate gradients), "chol" , "qr" (the default), "svd" and "sweep" .
tol	tolerance for the conjugate gradients ( gc ) method. Default is tol = 1e-7 .
maxiter	The maximum number of iterations for the conjugate gradients ( gc ) method. Defaults to 100.
model, x, y	logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response) are returned.
contrasts	an optional list. See the contrasts.arg of model.matrix.default .
list()	additional arguments (currently disregarded).

Value

ols returns an object of class "ols" .

The function summary is used to obtain and print a summary of the results. The generic accessor functions coefficients , fitted.values and residuals extract various useful features of the value returned by ols .

An object of class "ols" is a list containing at least the following components:

Value	Description
coefficients	a named vector of coefficients.
residuals	the residuals, that is response minus fitted values.
fitted.values	the fitted mean values.
RSS	the residual sum of squares.
cov.unscaled	a \(p \times p\) matrix of (unscaled) covariances of the \(\hat{\beta}_{j}\), \(j = 1,...,p\).
call	the matched call.
terms	the terms object used.
contrasts	(only where relevant) the contrasts used.
x	if requested, the response used.
y	if requested, the model matrix used.
model	if requested (the default), the model frame used.

Seealso

ols.fit , lm , lsfit

Examples

# tiny example of regression
y <- c(1, 3, 3, 2, 2, 1)
x <- matrix(c(1, 1,
2, 1,
3, 1,
1,-1,
2,-1,
3,-1), ncol = 2, byrow = TRUE)
f0 <- ols(y ~ x) # intercept is included by default
f0 # printing results (QR method was used)

f1 <- ols(y ~ x, method = "svd") # using SVD method instead
f1