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 similar to a function written by Wilcox (2005) but does not compute two-tailed p-values as these are often in conflict with the calculated confidence intervals. The default method for bootstrap confidence intervals is the percentile method which is recommended by Wilcox (2005) for M-estimators.

Usage

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

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.
type Method for computing bootstrap confidence intervals. Other alternatives are given in boot.ci from the library boot.

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 = R if NAs occur because MAD = 0.
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

References

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

See Also

bootstrap, boot

Examples

X1<-rnorm(100,2,2.5)
X2<-rnorm(100,3,3)
boot.ci.M(X1,X2)

[Package asbio version 0.1 Index]