boot.ci.M: 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

Sample from population one.

Sample from population two.

alpha

Significance level.

est

Location estimator; default is the Huber one step estimator.

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 = R if NAs occur because MAD = 0.

ci.type

The method used to construct the confidence interval.

conf

The level of confidence used.

The bootstrap distribution of differences standard error.

original

The original, observed difference.

lower

Lower confidence bound.

upper

Upper confidence bound.

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.

Examples

Run this code

# NOT RUN {
X1<-rnorm(100,2,2.5)
X2<-rnorm(100,3,3)
boot.ci.M(X1,X2)
# }