Description Usage Arguments Details Value Examples

Performs Robust Regularized Linear Discriminant Analysis.

1 |

`x` |
Matrix or data.frame of observations. |

`grouping` |
Grouping variable. A vector of numeric values >= 1 is recommended. Length has to correspond to nrow(x). |

`prior` |
Vector of prior probabilities for each group. If not supplied the priors are computed from the data. |

`lambda` |
Penalty parameter which controls the sparseness of the resulting inverse scatter matrix. Default is 0.5 |

`hp` |
Robustness parameter which specifies the amount of observations to be included in the computations. Default is 0.75 |

`nssamples` |
Number of start samples to be user for iterated estimations. |

`maxit` |
Maximum number of iterations of the algorithm. Default is 10. |

`penalty` |
Type of penalty to be applied. Possible values are "L1" and "L2". |

Performs Robust Regularized Discriminant Analysis using a sparse estimation of the inverse covariance matrix. The sparseness is controlled by a penalty parameter lambda. Possible outliers are dealt with by a robustness parameter alpha which specifies the amount of observations for which the likelihood function is maximized.

An object of class "rrlda" is returned which can be used for class prediction (see predict()). prior=prior, counts=counts, means=means, cov=covm, covi=covi, lev=lev, n=n, h=h, bic=bic, loglik=loglik, nonnuls=nonnuls, subs=est$subset

`prior` |
Vector of prior probabilities. |

`counts` |
Number of obervations for each group. |

`means` |
Estimated mean vectors for each group. |

`covi` |
Estimated (common) inverse covariance matrix. |

`lev` |
Levels. Corresponds to the groups. |

`n` |
Number of observations. |

`h` |
Number of observations included in the computations (see robustness parameter alpha). |

`bic` |
Adapted bic value. Can be used for optimal selection of lambda |

`loglik` |
The maximized (log-)likelihood value. |

`df` |
Degrees of freedom of the estimated inverse covariance matrix. |

`subs` |
An index vector specifying the data subset used (see robustness parameter alpha). |

1 2 3 4 5 | ```
data(iris)
x <- iris[,1:4]
rr <- rrlda(x, grouping=as.numeric(iris[,5]), lambda=0.2, hp=0.75) ## perform rrlda
pred <- predict(rr, x) ## predict
table(as.numeric(pred$class), as.numeric(iris[,5])) ## show errors
``` |

rrlda documentation built on May 29, 2017, 9:07 p.m.

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