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Ordinary least squares.

Source: R/ols.R
ols.Rd

Simple implementation of ordinary least squares that computes with sparse feature matrices.

Usage

ols(y, X, const = FALSE, w = NULL)

Arguments

y

The outcome variable.

X

The feature matrix.

const

Boolean equal to TRUE if a constant should be included. The default is FALSE

w

A vector of weights for weighted least squares.

Value

ols returns an object of S3 class ols. An object of class ols is a list containing the following components:

coef

A vector with the regression coefficents.

y, X, const, w

Pass-through of the user-provided arguments. See above.

See also

Other ml_wrapper: mdl_glmnet(), mdl_glm(), mdl_ranger(), mdl_xgboost()

Examples

ols_fit <- ols(rnorm(100), cbind(rnorm(100), rnorm(100)), const = TRUE)
ols_fit$coef
#>             [,1]
#> [1,]  0.03270868
#> [2,]  0.08204289
#> [3,] -0.05981607