Design-based rank tests

Description

Design-based versions of k-sample rank tests. The built-in tests are all for location hypotheses, but the user could specify others.

Usage

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svyranktest(formula, design, 
  test = c("wilcoxon", "vanderWaerden", "median","KruskalWallis"), ...)

Arguments

formula

Model formula y~g for outcome variable y and group g

design

A survey design object

test

Which rank test to use: Wilcoxon, van der Waerden's normal-scores test, Mood's test for the median, or a function f(r,N) where r is the rank and N the estimated population size. "KruskalWallis" is a synonym for "wilcoxon" for more than two groups.

...

for future expansion

Value

Object of class htest

References

Lumley, T., & Scott, A. J. (2013). Two-sample rank tests under complex sampling. BIOMETRIKA, 100 (4), 831-842.

See Also

svyttest, svylogrank

Examples

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data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, fpc=~fpc, data=apiclus1)

svyranktest(ell~comp.imp, dclus1)
svyranktest(ell~comp.imp, dclus1, test="median")


svyranktest(ell~stype, dclus1)
svyranktest(ell~stype, dclus1, test="median")



## upper quartile
svyranktest(ell~comp.imp, dclus1, test=function(r,N) as.numeric(r>0.75*N))


quantiletest<-function(p){
	  rval<-function(r,N) as.numeric(r>(N*p))
	  attr(rval,"name")<-paste(p,"quantile")
	  rval
	}
svyranktest(ell~comp.imp, dclus1, test=quantiletest(0.5))
svyranktest(ell~comp.imp, dclus1, test=quantiletest(0.75))

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