boot.ci.M {asbio} | R Documentation |
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.
boot.ci.M(X1, X2, alpha = 0.05, est = huber.one.step, R = 1000, type = "perc")
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 . |
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. |
Ken Aho
Manly, B. F. J. (1997) Randomization and Monte Carlo methods in biology, 2nd edition. Chapman and Hall, London.
X1<-rnorm(100,2,2.5) X2<-rnorm(100,3,3) boot.ci.M(X1,X2)