predict.lars | R Documentation |

While lars() produces the entire path of solutions, predict.lars allows one to extract a prediction at a particular point along the path.

## S3 method for class 'lars' predict(object, newx, s, type = c("fit", "coefficients"), mode = c("step", "fraction", "norm", "lambda"), ...) ## S3 method for class 'lars' coef(object, ...)

`object` |
A fitted lars object |

`newx` |
If type="fit", then newx should be the x values at which the fit is required. If type="coefficients", then newx can be omitted. |

`s` |
a value, or vector of values, indexing the path. Its values depends on the mode= argument. By default (mode="step"), s should take on values between 0 and p (e.g., a step of 1.3 means .3 of the way between step 1 and 2.) |

`type` |
If type="fit", predict returns the fitted values. If type="coefficients", predict returns the coefficients. Abbreviations allowed. |

`mode` |
Mode="step" means the s= argument indexes the lars step number, and the coefficients will be returned corresponding to the values corresponding to step s. If mode="fraction", then s should be a number between 0 and 1, and it refers to the ratio of the L1 norm of the coefficient vector, relative to the norm at the full LS solution. Mode="norm" means s refers to the L1 norm of the coefficient vector. Mode="lambda" uses the lasso regularization parameter for s; for other models it is the maximal correlation (does not make sense for lars/stepwise models). Abbreviations allowed. |

`...` |
Any arguments for |

LARS is described in detail in Efron, Hastie, Johnstone and Tibshirani (2002). With the "lasso" option, it computes the complete lasso solution simultaneously for ALL values of the shrinkage parameter in the same computational cost as a least squares fit.

Either a vector/matrix of fitted values, or a vector/matrix of coefficients.

Trevor Hastie

Efron, Hastie, Johnstone and Tibshirani (2002) "Least Angle Regression"
(with discussion) *Annals of Statistics*; see also
doi: 10.1214/009053604000000067.
Hastie, Tibshirani and Friedman (2002) Elements of Statistical Learning, Springer, NY.

print, plot, lars, cv.lars

data(diabetes) attach(diabetes) object <- lars(x,y,type="lasso") ### make predictions at the values in x, at each of the ### steps produced in object fits <- predict.lars(object, x, type="fit") ### extract the coefficient vector with L1 norm=4.1 coef4.1 <- coef(object, s=4.1, mode="norm") # or coef4.1 <- predict(object, s=4.1, type="coef", mode="norm") detach(diabetes)

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