# R/rmanovab.R In WRS2: A Collection of Robust Statistical Methods

```rmanovab <- function(y, groups, blocks, tr = 0.2, nboot = 599){

cols1 <- deparse(substitute(y))
cols2 <- deparse(substitute(groups))
cols3 <- deparse(substitute(blocks))
dat <- data.frame(y, groups, blocks)
colnames(dat) <- c(cols1, cols2, cols3)
cl <- match.call()

x <- reshape(dat, idvar = cols3, timevar = cols2, direction = "wide")[-1]  ## wide format

alpha <- 0.05
grp <- 0
if(is.data.frame(x)) x=as.matrix(x)

if(is.matrix(x)){
if(sum(grp)==0)grp<-c(1:ncol(x))
mat<-x[,grp]
}
mat=elimna(mat)
J<-ncol(mat)
connum<-(J^2-J)/2
bvec<-matrix(0,connum,nboot)
# if (SEED) set.seed(2) # set seed of random number generator so that
#             results can be duplicated.
data<-matrix(sample(nrow(mat),size=nrow(mat)*nboot,replace=TRUE),nrow=nboot)
xcen<-matrix(0,nrow(mat),ncol(mat))
for (j in 1:J)xcen[,j]<-mat[,j]-mean(mat[,j],tr) #Center data
bvec<-apply(data,1,tsubrmanovab,xcen,tr)
# bvec is vector of nboot  bootstrap test statistics.
icrit<-round((1-alpha)*nboot)
bvec<-sort(bvec)
crit<-bvec[icrit]
test<-rmanovatemp(mat,tr,grp)\$test
result <- list(test = test, crit = crit, call = cl)
class(result) <- c("rmanovab")
result
}
```

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WRS2 documentation built on May 2, 2019, 4:46 p.m.