boot.ratio.sd.bca: BCa Bootstrap Independent-Samples Test and CI for Two...
In wBoot: Bootstrap Methods

Description Usage Arguments Value Author(s) Examples

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

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boot.ratio.sd.bca(x, y, stacked = TRUE, variable = NULL, null.hyp = NULL,
                  alternative = c("two.sided", "less", "greater"),
                  conf.level = 0.95, type = NULL, R = 9999)

`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.

Neil A. Weiss

# 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.bca(STRENGTH, BRAND, conf.level = 0.90)

# 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.bca(STRENGTH, BRAND, null.hyp = 1)

detach(elmendorf)  # clean up