mv.2way.est: Treatment Effect Estimates in the Randomized Complete Block...

In MNM: Multivariate Nonparametric Methods. An Approach Based on Spatial Signs and Ranks

Description

The treatment effect estimates for different score functions and their asymptotic covariance matrices in the randomized complete block case.

Usage

mv.2way.est(x, block, treatment, score = c("identity", "sign", "rank"),
             stand = c("outer", "inner"), 
             eps=1.0e-10, n.iter=1000, na.action = na.fail)

Arguments

x	a numeric data frame or matrix.
block	a factor with at least two levels.
treatment	a factor with at least two levels.
score	the score to be used. Possible choices are identity, sign and rank.
stand	the standardization method used. Possible choices are outer and inner.
eps	convergence criterion.
n.iter	maximum number of iterations.
na.action	a function which indicates what should happen when the data contain 'NA's. Default is to fail.

Details

This implements the treatment effect estimates described in chapter 12 of the MNM book.

Value

A list of length c(c-1)/2 with class 'mvcloc' where c is the number of treatments. Each component of the list is a list with class 'mvloc' containing the following components:

location	the adjusted treatment effect estimate when comparing the treatment pair given in dname.
vcov	the asymptotic covariance matrix of the adjusted treatment effect estimate.
est.name	name of the adjusted treatment effect estimate.
dname	the treatment pair for which the adjusted treatment effect estimate was computed.

Author(s)

Jyrki M<f6>tt<f6>nen [e-mail]

References

Oja, H. (2010), Multivariate Nonparametric Methods with R, Springer.

See Also

mv.2way.test, mv.1sample.est, mv.2sample.est

Examples

data(beans)
est<-mv.2way.est(beans[,3:5],beans$Block,beans$Treatment,score="r",stand="i")
summary(est)

MNM documentation built on May 2, 2019, 5:09 a.m.