ols {fastmatrix}

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,
  x = FALSE, y = FALSE, contrasts = NULL, ...)

Arguments

formula

an object of class "formula" (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 NAs. 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.

x, y

logicals. If TRUE the corresponding components of the fit (the model matrix, the response) are returned.

contrasts

an optional list. See the contrasts.arg of model.matrix.default.

...

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:

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, \dots, p.

call

the matched call.

terms

the terms object used.

contrasts

(only where relevant) the contrasts used.

y

if requested, the response used.

x

if requested, the model matrix used.

model

if requested (the default), the model frame used.

See Also

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