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Simple implementation of ordinary least squares that computes with sparse feature matrices.
ols(y, X, const = TRUE, w = NULL)
ols returns an object of S3 class ols. An object of class ols is a list containing the following components:
ols
coef
A vector with the regression coefficents.
y
X
const
w
Pass-through of the user-provided arguments. See above.
The outcome variable.
The feature matrix.
Boolean equal to TRUE if a constant should be included.
TRUE
A vector of weights for weighted least squares.
Other ml_wrapper: mdl_glmnet(), mdl_glm(), mdl_ranger(), mdl_xgboost()
mdl_glmnet()
mdl_glm()
mdl_ranger()
mdl_xgboost()
ols_fit <- ols(rnorm(100), cbind(rnorm(100), rnorm(100)), const = TRUE) ols_fit$coef
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