Description Usage Arguments Details Value Author(s) See Also Examples

One of five filter methods can be chosen for repeated shaving of
a certain percentage of the worst performing variables. Performance of the
reduced models are stored and viewable through `print`

and `plot`

methods.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
shaving(
y,
X,
ncomp = 10,
method = c("SR", "VIP", "sMC", "LW", "RC"),
prop = 0.2,
min.left = 2,
comp.type = c("CV", "max"),
validation = c("CV", 1),
fixed = integer(0),
newy = NULL,
newX = NULL,
segments = 10,
plsType = "plsr",
Y.add = NULL,
...
)
## S3 method for class 'shaved'
plot(x, y, what = c("error", "spectra"), index = "min", log = "x", ...)
## S3 method for class 'shaved'
print(x, ...)
``` |

`y` |
vector of response values ( |

`X` |
numeric predictor |

`ncomp` |
integer number of components (default = 10). |

`method` |
filter method, i.e. SR, VIP, sMC, LW or RC given as |

`prop` |
proportion of variables to be removed in each iteration ( |

`min.left` |
minimum number of remaining variables. |

`comp.type` |
use number of components chosen by cross-validation, |

`validation` |
type of validation for |

`fixed` |
vector of indeces for compulsory/fixed variables that should always be included in the modelling. |

`newy` |
validation response for RMSEP/error computations. |

`newX` |
validation predictors for RMSEP/error computations. |

`segments` |
see |

`plsType` |
Type of PLS model, "plsr" or "cppls". |

`Y.add` |
Additional response for CPPLS, see |

`...` |
additional arguments for |

`x` |
object of class |

`what` |
plot type. Default = "error". Alternative = "spectra". |

`index` |
which iteration to plot. Default = "min"; corresponding to minimum RMSEP. |

`log` |
logarithmic x (default) or y scale. |

Variables are first sorted with respect to some importancemeasure, and usually one of the filter measures described above are used. Secondly, a threshold is used to eliminate a subset of the least informative variables. Then a model is fitted again to the remaining variables and performance is measured. The procedure is repeated until maximum model performance is achieved.

Returns a list object of class `shaved`

containing the method type,
the error, number of components, and number of variables per reduced model. It
also contains a list of all sets of reduced variable sets plus the original data.

Kristian Hovde Liland

`VIP`

(SR/sMC/LW/RC), `filterPLSR`

, `shaving`

,
`stpls`

, `truncation`

,
`bve_pls`

, `ga_pls`

, `ipw_pls`

, `mcuve_pls`

,
`rep_pls`

, `spa_pls`

,
`lda_from_pls`

, `lda_from_pls_cv`

, `setDA`

.

1 2 3 4 5 6 7 |

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