# R/t1wayv2.R In WRS2: A Collection of Robust Statistical Methods

```t1wayv2 <-
function(x,tr=.2,grp=NA,MAT=FALSE,lev.col=1,var.col=2,nboot=100,SEED=TRUE,pr=TRUE,IV=NULL,loc.fun=median){
#
# Same a t1way, but computes explanatory power and related effect size
#
# For n1!=n2, this function calls t1way.effect.
#
#  A heteroscedastic one-way ANOVA for trimmed means
#  using a generalization of Welch's method.
#
#  The data are assumed to be stored in \$x\$ in a matrix or in list mode.
#
# MAT=F, if x is a matrix, columns correspond to groups.
# if MAT=T, assumes argument
# lev.col
# indicates which column of x denotes the groups. And
#  var.col indicates the column where the data are stored.
#
#  IV, if specified, taken to be the independent variable
#      That is, the group id values
#      and x is assumed to be a vector containing all of the data
#
# if x has list mode:
#  length(x) is assumed to correspond to the total number of groups.
#  By default, the null hypothesis is that all groups have a common mean.
#  To compare a subset of the groups, use grp to indicate which
#  groups are to be compared. For example, if you type the
#  command grp<-c(1,3,4), and then execute this function, groups
#  1, 3, and 4 will be compared with the remaining groups ignored.
#
#  Missing values are automatically removed.
#
if(SEED)set.seed(2)
if(MAT){
if(!is.matrix(x))stop("With MAT=T, data must be stored in a matrix")
if(length(lev.col)!=1)stop("Argument lev.col should have 1 value")
temp=selby(x,lev.col,var.col)
x=temp\$x
grp2=rank(temp\$grpn)
x=x[grp2]
}
if(!is.null(IV)){
if(pr)print("Assuming x is a vector containing all of the data, the dependent variable")
xi=elimna(cbind(x,IV))
x=fac2list(xi[,1],xi[,2])
}
if(is.matrix(x))x<-listm(x)
if(is.na(sum(grp)))grp<-c(1:length(x))
if(!is.list(x))stop("Data are not stored in a matrix or in list mode.")
J<-length(grp)
h<-vector("numeric",J)
w<-vector("numeric",J)
xbar<-vector("numeric",J)
pts=NULL
nval=0
for(j in 1:J)x[[j]]=elimna(x[[j]])
for(j in 1:J){
val<-x[[j]]
val<-elimna(val)
nval[j]=length(val)
pts=c(pts,val)
x[[j]]<-val # missing values have been removed
h[j]<-length(x[[grp[j]]])-2*floor(tr*length(x[[grp[j]]]))
# h is the number of observations in the jth group after trimming.
w[j]<-h[j]*(h[j]-1)/((length(x[[grp[j]]])-1)*winvar(x[[grp[j]]],tr))
xbar[j]<-mean(x[[grp[j]]],tr)
}
u<-sum(w)
xtil<-sum(w*xbar)/u
A<-sum(w*(xbar-xtil)^2)/(J-1)
B<-2*(J-2)*sum((1-w/u)^2/(h-1))/(J^2-1)
TEST<-A/(B+1)
nu1<-J-1
nu2<-1./(3*sum((1-w/u)^2/(h-1))/(J^2-1))
sig<-1-pf(TEST,nu1,nu2)
nv=lapply(x,length)
#
# Determine explanatory effect size
#
chkn=var(nval)
if(chkn==0){
top=var(xbar)
bot=winvarN(pts,tr=tr)
e.pow=top/bot
}
if(chkn!=0){
vals=0
N=min(nval)
xdat=list()
for(i in 1:nboot){
for(j in 1:J){
xdat[[j]]=sample(x[[j]],N)
vals[i]=t1way.effect(xdat,tr=tr)\$Var.Explained
}}
e.pow=loc.fun(vals,na.rm=TRUE)
}
list(TEST=TEST,nu1=nu1,nu2=nu2,n=nv,p.value=sig,Var.Explained=e.pow,
Effect.Size=sqrt(e.pow))
}
```

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