ols: Fit linear regression model

In fastmatrix: Fast Computation of some Matrices Useful in Statistics

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

fastmatrix documentation built on Oct. 12, 2023, 5:14 p.m.