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 intervals.

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.usedThe number of bootstrap samples used. This may not = R if NAs occur because MAD = 0.
ci.typeThe method used to construct the confidence interval.
confThe level of confidence used.
seThe bootstrap distribution of differences standard error.
originalThe original, observed difference.
lowerLower confidence bound.
upperUpper 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

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