R: Design-based rank tests

svyranktest {survey} | R Documentation

## 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

    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

    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))

[Package survey version 3.32-1 Index]