Description Usage Arguments Details Value Author(s) Examples

This function calculates leave-multiple-out (LMO) *p*-values for an increasing number of data points and identifies those resulting in "significance reversal" of the model, i.e. in the slope's *p*-value traversing the user-defined *α*-level.

1 2 |

`model` |
the linear model of class |

`max` |
the maximum number of points to eliminate. |

`n` |
the number of samples to draw for each 1... |

`alpha` |
the |

`method` |
select either parametric ( |

`verbose` |
logical. If |

The algorithm

1) calculates the *p*-value of the full model (all data points),

2) calculates a LMO-*p*-value for all `n`

sampled groups of 1...`max`

points removed,

3) checks for significance reversal in the resulting model and

4) returns all `n`

samples and the corresponding *p*-values.

A list with the following items:

`sample` |
a matrix with all |

`stat` |
for each 1... |

Andrej-Nikolai Spiess

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Example with single influencers and insignificant model (p = 0.115).
set.seed(123)
a <- 1:20
b <- 5 + 0.08 * a + rnorm(20, 0, 1)
LM1 <- lm(b ~ a)
res1 <- lmMult(LM1)
multPlot(res1)
stability(res1)
## Large example with 100 data points and highly significant model (p = 6.72E-8).
## No significance reversal up to the elimination of 20 points.
set.seed(123)
a <- 1:100
b <- 5 + 0.08 * a + rnorm(100, 0, 5)
LM2 <- lm(b ~ a)
res2 <- lmMult(LM2, max = 20)
multPlot(res2)
stability(res2)
``` |

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