boot.paired.bca: BCa Bootstrap Paired-Samples Test and CI for Two Means
In wBoot: Bootstrap Methods

Description Usage Arguments Details Value Author(s) Examples

Obtains a paired-samples confidence interval and (optionally) performs a paired-samples hypothesis test for the difference between two population means, using the BCa bootstrap method.

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

`x`	a (non-empty) numeric vector of data values.
`y`	a (non-empty) numeric vector of data values.
`variable`	an optional character string that gives the name of the variable under consideration.
`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.

Note that x and y must have the same length, as together they represent the paired data. Also note, for instance, that alternative = "greater" is the alternative that x variable has a larger mean than y variable.

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

`Boot.values`	the point estimates for the differences between the means obtained from the bootstrap.
`Confidence.limits`	the confidence limit(s) for the confidence interval.
`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`	the sample size.
`Statistic`	the name of the statistic, here diff.mean.
`Observed`	the observed point estimate for the difference between the means.
`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

# The number of inappropriate words out of 10 that were identified in the
# Times New Roman (TNR) and Gigi fonts by each of 25 participants.
data("fonts")
str(fonts)
attach(fonts)

# 90% confidence interval for the difference between the mean number of
# inappropriate words out of 10 identified for the TNR and Gigi fonts.
boot.paired.bca(TNR, GIGI, conf.level = 0.90)

# A right-tailed test with null hypothesis 2, and a 95% (default) lower
# confidence bound for the difference between the mean number of
# inappropriate words out of 10 identified for the TNR and Gigi fonts. 
boot.paired.bca(TNR, GIGI, null.hyp = 2, alternative = "greater")
# Not significant at the 5% level.

# A right-tailed test with null hypothesis 1, and a 95% (default) lower
# confidence bound for the difference between the mean number of
# inappropriate words out of 10 identifiedd for the TNR and Gigi fonts.
boot.paired.bca(TNR, GIGI, null.hyp = 1, alternative = "greater")
# Significant at the 5% level.

detach(fonts)   # clean up