Description Usage Arguments Value Author(s) See Also Examples

Fits a smoothing spline robustly using the *L_1* norm. Currently, the
algorithm is an *iterative reweighted smooth spline* algorithm which
calls `smooth.spline(x,y,w,...)`

at each iteration with the weights
`w`

equal to the inverse of the absolute value of the residuals for
the last iteration step.

1 2 3 |

`x` |
a |

`y` |
responses. If |

`w` |
a |

`...` |
Other arguments passed to |

`minIter` |
the minimum number of iterations used to fit the smoothing spline robustly. Default value is 3. |

`maxIter` |
the maximum number of iterations used to fit the smoothing spline robustly. Default value is 25. |

`sdCriteria` |
Convergence criteria, which the difference between the standard deviation of the residuals between two consecutive iteration steps. Default value is 2e-4. |

`reps` |
Small positive number added to residuals to avoid division by zero when calculating new weights for next iteration. |

`tol` |
Passed to |

`plotCurves` |
If |

Returns an object of class `smooth.spline`

.

Henrik Bengtsson

This implementation of this function was adopted from
`smooth.spline`

of the stats package.
Because of this, this function is also licensed under GPL v2.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
data(cars)
attach(cars)
plot(speed, dist, main = "data(cars) & robust smoothing splines")
# Fit a smoothing spline using L_2 norm
cars.spl <- smooth.spline(speed, dist)
lines(cars.spl, col = "blue")
# Fit a smoothing spline using L_1 norm
cars.rspl <- robustSmoothSpline(speed, dist)
lines(cars.rspl, col = "red")
# Fit a smoothing spline using L_2 norm with 10 degrees of freedom
lines(smooth.spline(speed, dist, df=10), lty=2, col = "blue")
# Fit a smoothing spline using L_1 norm with 10 degrees of freedom
lines(robustSmoothSpline(speed, dist, df=10), lty=2, col = "red")
legend(5,120, c(
paste("smooth.spline [C.V.] => df =",round(cars.spl$df,1)),
paste("robustSmoothSpline [C.V.] => df =",round(cars.rspl$df,1)),
"standard with s( * , df = 10)", "robust with s( * , df = 10)"
), col = c("blue","red","blue","red"), lty = c(1,1,2,2), bg='bisque')
``` |

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