# Jackknife estimation for PPLS-coefficients

### Description

This function computes the mean and the covariance of the regression coefficients of Penalized Partial Least Squares.

### Usage

1 | ```
jack.ppls(ppls.object,ncomp,index.lambda)
``` |

### Arguments

`ppls.object` |
an object returned by |

`ncomp` |
integer. The number of components that are used. The default value is the cross-validation optimal number of components. |

`index.lambda` |
integer. The index of the penalization intensity, given by the vector |

### Details

The function needs an object returned by `penalized.pls.cv`

. It estimates the mean and the covariance of the regression coefficient (with `ncomp`

components and penalization intensity indexed by `index.lambda`

). This is done via a jackknife estimate over the `k`

cross-validation splits. We remark that this estimation step is not discussed in Kraemer, Boulesteix and Tutz (2008).

### Value

The function returns an object of class ”ppls”.

`mean.ppls` |
The mean of the regression coefficients over all cross-validation splits. This is a vector of length |

`vcov.ppls` |
The covariance matrix of the regression coefficients. This is a symmetric matrix of size |

`index.lambda` |
Index for the value of lambda that determines the regression coefficient. |

`ncomp` |
Number of components that determines the regression coefficients. |

`k` |
The number of cross-validation splits. These can be used to construct a t-test for the coefficients. |

### Author(s)

Nicole Kraemer

### References

N. Kraemer, A.-L. Boulsteix, and G. Tutz (2008). *Penalized Partial Least Squares with Applications
to B-Spline Transformations and Functional Data*. Chemometrics and Intelligent Laboratory Systems 94, 60 - 69. http://dx.doi.org/10.1016/j.chemolab.2008.06.009

### See Also

`penalized.pls.cv`

,`ttest.ppls`

### Examples

1 2 3 4 5 6 |

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