boot.ci.M {asbio} | R Documentation |

## Bootstrap CI of M-estimators differences of two samples

### Description

Creates a bootstrap confidence interval for location differences for two samples. The default location
estimator is the Huber one-step estimator, although any estimator can be used. The function is based on
a function written by Wilcox (2005). Note, importantly, that *P*-values may be in conflict with the confidence interval bounds.

### Usage

```
boot.ci.M(X1, X2, alpha = 0.05, est = huber.one.step, R = 1000)
```

### Arguments

`X1` |
Sample from population one. |

`X2` |
Sample from population two. |

`alpha` |
Significance level. |

`est` |
Location estimator; default is the Huber one step estimator. |

`R` |
Number of bootstrap samples. |

### Value

Returns a list with one component, a dataframe containing summary information from the analysis:

`R.used` |
The number of bootstrap samples used. This may not = |

`ci.type` |
The method used to construct the confidence interval. |

`conf` |
The level of confidence used. |

`se` |
The bootstrap distribution of differences standard error. |

`original` |
The original, observed difference. |

`lower` |
Lower confidence bound. |

`upper` |
Upper confidence bound. |

### Author(s)

Ken Aho and R. R. Wilcox from whom I stole liberally from code in the function `m2ci`

on R-forge

### References

Manly, B. F. J. (1997) *Randomization and Monte Carlo methods in Biology, 2nd edition*.
Chapman and Hall, London.

Wilcox, R. R. (2005) *Introduction to Robust Estimation and Hypothesis Testing, 2nd edition*. Elsevier,
Burlington, MA.

### See Also

### Examples

```
## Not run:
X1<-rnorm(100,2,2.5)
X2<-rnorm(100,3,3)
boot.ci.M(X1,X2)
## End(Not run)
```

*asbio*version 1.9-7 Index]