R: Fit linear regression model

ols {fastmatrix} | R Documentation

## 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

`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 `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`.
`...` | 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 x p matrix of (unscaled) covariances of the coef[j], j=1, …, 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

[Package fastmatrix version 0.3-8196 Index]