Description Usage Arguments Value Warning Author(s) Examples

View source: R/boot.ratio.sd.per.R

Obtains an independent-samples confidence interval and (optionally) performs an independent-samples hypothesis test for the ratio of two population standard deviations, using the percentile bootstrap method.

1 2 3 |

`x` |
a numeric vector of observations of the variable (stacked case) or a numeric vector of data values representing the first of the two samples (unstacked case). |

`y` |
a vector of corresponding population identifiers (stacked case) or a numeric vector of data values representing the second of the two samples (unstacked case). |

`stacked` |
a logical value (default TRUE) indicating whether the data are stacked. |

`variable` |
an optional string that gives the name of the variable under consideration; ignored if stacked is TRUE. |

`null.hyp` |
the null-hypothesis value; if omitted, no hypothesis test is performed. |

`alternative` |
a character string specifying the alternative hypothesis; must be one of "two.sided" (default), "greater", or "less". |

`conf.level` |
the confidence level (between 0 and 1); default is 0.95. |

`type` |
a character string specifying the type of CI; if user supplied, must be one of "two-sided", "upper-bound", or "lower-bound"; defaults to "two-sided" if alternative is "two.sided", "upper-bound" if alternative is "less", and "lower-bound" if alternative is "greater". |

`R` |
the number of bootstrap replications; default is 9999. |

A list with class "boot.two" containing the following components:

`Stacked ` |
a logical indicating whether the data are stacked (TRUE) or not (FALSE). |

`Boot.values ` |
the point estimates for the ratio of the standard deviations obtained from the bootstrap. |

`Confidence.limits ` |
the confidence limit(s) for the confidence interval. |

`Parameter ` |
the parameter under consideration, here standard deviation. |

`Header ` |
the main title for the output. |

`Variable ` |
the name of the variable under consideration or NULL |

`Pop.1 ` |
the first population. |

`Pop.2 ` |
the second population. |

`n.1 ` |
the sample size for the first population. |

`n.2 ` |
the sample size for the second population. |

`Statistic ` |
the name of the statistic, here ratio.sd. |

`Observed.1 ` |
the observed point estimate for the standard deviation of the first population. |

`Observed.2 ` |
the observed point estimate for the standard deviation of the second population. |

`Observed ` |
the observed point estimate for the ratio of the two standard deviations. |

`Replications ` |
the number of bootstrap replications. |

`Mean ` |
the mean of the bootstrap values. |

`SE ` |
the standard deviation of the bootstrap values. |

`Bias ` |
the difference between the mean of the bootstrap values and the observed value. |

`Percent.bias ` |
the percentage bias: 100*|Bias/Observed|. |

`Null ` |
the null-hypothesis value or NULL. |

`Alternative ` |
the alternative hypothesis or NULL. |

`P.value ` |
the P-value or a statement like P < 0.001 or NULL. |

`p.value ` |
the P-value or NULL. |

`Level ` |
the confidence level. |

`Type ` |
the type of confidence interval. |

`Confidence.interval ` |
the confidence interval. |

This routine should be used only when bias is small and the sampling distribution is roughly symmetric, as indicated by the output of the bootstrap. Otherwise, use the BCa version.

Neil A. Weiss

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# Elmendorf tear strengths, in grams, for independent samples of
# Brand A and Brand B vinyl floor coverings.
data("elmendorf")
str(elmendorf)
attach(elmendorf)
# Note that the data are stacked.
# 90% confidence interval for the ratio of the population standard
# deviations of tear strength for Brands A and B.
boot.ratio.sd.per(STRENGTH, BRAND, conf.level = 0.90)
# See the preceeding warning!
# 95% (default) confidence interval for the ratio of the population
# standard deviations of tear strength for Brands A and B, and a
# two-tailed hypothesis test with null hypothesis 1 (i.e., the
# population standard deviations are equal).
boot.ratio.sd.per(STRENGTH, BRAND, null.hyp = 1)
# See the preceeding warning!
detach(elmendorf) # clean up
``` |

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