Confidence intervals for the total sensitivity indices by a bootstrap method.

1 2 |

`Y` |
Outputs. A data.frame with as many rows as observations and as many columns as response variables. |

`XIndic` |
Object of class |

`B` |
Number of bootstrap replicates. |

`nc` |
Number of components. |

`graph` |
If TRUE, boxplot display. |

`alea` |
If TRUE, an uniform random variable is included
in the analysis (see |

`fast` |
If TRUE, auxiliary results are calculated from the Miller's formulae more adapted to big datasets. |

`alpha` |
Level of the bootstrap confidence intervals. |

A matrix with as many rows as input variables and two columns: the lower and upper bounds of the total sensitivity indices percentile bootstrap confidence intervals.

1 2 3 4 5 6 7 8 | ```
X <- cornell0[,1:3] # X-inputs
Y <- as.data.frame( cornell0[,8]) # response variable
# Creation of the polynomial:
P <- vect2polyX(X, c("1", "2", "3", "3*3*3"))
set.seed(15) #alea seed
nloops <- 3 # number of loops, example for fast running
nc <- 2 # number of components
sivipboot(Y, P, nloops, nc, fast=TRUE)
``` |

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