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#
#
# Author: FrankKonietschke
###############################################################################
gao<-function(formula, data, alpha = 0.05, control = NULL , silent = FALSE){
dat <- model.frame(formula, data)
if (ncol(dat) != 2) {
stop("Specify one response and only one class variable in the formula !")
}
if (is.numeric(dat[, 1]) == FALSE) {
stop("Response variable must be numeric !")
}
response <- dat[, 1]
group <- as.factor(dat[, 2])
fl <- levels(group)
a <- nlevels(group)
N <- length(response)
n <- aggregate(response,list(group),FUN="length")$x
if (any(n <= 2)) {
warn <- paste("The factor level", fl[n <= 2], "has got less than two observations!")
stop(warn)
}
if (is.null(control)) {
cont <- 1
}
if(! is.null(control)){
if (!any(fl == control)) {
stop("The dataset doesn't contain this control group!")
}
cont <- which(fl == control)
}
C<-contrMat(1:a,"Dunnett",base=cont)
# ------------- Compute the pseudo-ranks------------------ #
#browser()
rx <- c()
for (i in 1:N){
help <- expand.grid(response[i],response)
help1 <- (help[,1]>help[,2])+1/2*(help[,1]== help[,2])
help2 <- data.frame(h1=help1,h2=group)
samples2 <- split(help2$h1, help2$h2)
pseudo <- sapply(1:a, function(arg) {
mean(samples2[[arg]])
})
rx[i] <-N*mean(pseudo)
}
new.data <-data.frame(res=rx,group=group)
# ------------------ Point estimators ---------------------#
pd <- 1/N*aggregate(new.data$res,list(group), FUN="mean")$x
Cpd <- C%*%pd
# ------------ Compute the variance estimators ----------- #
v1 <- 1/N^2*aggregate(new.data$res,list(group),FUN="var")$x
lambda <- N/n
v11 <-c(v1*lambda)
v2 <- diag(v1*lambda)
Cv <- C%*%v2%*%t(C)
# ------------------ Test Statistics ----------------------#
T <-sqrt(N)*Cpd / sqrt(c(diag(Cv)))
# ------------------ Degrees of freedom--------------------#
ncont <-which((1:a)!= cont)
numerator <- c(diag(Cv))^2
denu1<-v1[cont]^2/(n[cont]^2*(n[cont]-1))
denu2 <- v1[ncont]^2 /(n[ncont]^2*(n[ncont]-1))
denu <- N^2*(denu1 + denu2)
df <- numerator / denu
#-------------------------p-Values ------------------------#
pv<- c()
for (h in 1:(a-1)){
pv[h]<- min(2*pt(T[h],df[h]),2-2*pt(T[h],df[h]))
}
adj.p <- p.adjust(pv,"hochberg")
Rejected <- (adj.p<=alpha)
#------------------- Build the output ---------------------#
vj <- which((1:a) != cont)
vi <- rep(cont, a - 1)
cmpid <- sapply(1:(a-1), function(arg) {
i <- vi[arg]
j <- vj[arg]
paste("F", "(", fl[j], ")", "-","F","(" ,fl[i],")", sep = "")
})
result <- data.frame(Comparison=cmpid, Estimator = Cpd, df=df, Statistic = T, P.Raw=pv,P.Adj=adj.p,Rejected = Rejected )
rownames(result)<-1:(a-1)
output = list(Info=data.frame(Sample=fl, Size=n, Single.Effects=pd),
Analysis=result)
if (! silent)
{
cat("#----Xin Gao's (2008) Non-Parametric Multiple Test Procedure","\n")
cat("#----Type of Adjustment: Hochberg", "\n")
cat("#----Level of significance", "=", alpha ,"\n")
cat("#----The procedure compares if the distribution functions F() are equal. The FWER is strongly controlled", "\n")
print(result)
}
return(output)
}
gao.wrapper <- function(model, data, alpha, control) {
control <- NULL
result <- gao(formula=formula(model),
data=data,
alpha = alpha,control)
pvalues <- result$Analysis$P.Adj
estimates <- result$Analysis$Estimator
confint <- cbind(estimates, rep(NA, length(estimates)),rep(NA,length(estimates)))
rownames(confint)<-result$Analysis$Comparison
rejected1 <- result$Analysis$Rejected
return(list(adjPValues=pvalues,rejected=rejected1,confIntervals= confint,
errorControl = new(Class='ErrorControl',type="FWER",alpha=alpha)))
}
mutoss.gao <- function() { return(new(Class="MutossMethod",
label="Nonparametric Multiple contrast tests",
errorControl="FWER",
callFunction="gao.wrapper",
output=c("adjPValues", "rejected","confIntervals","errorControl"),
info="<h2>Nonparametric multiple contrast tests</h2>
<p> This function computes Xin Gao's nonparametric multiple test procedures in an unbalanced one way layout. <p>
<p></p>
<h3>Reference:</h3>
<ul>
<li>Gao, X. et al. \"<i>Nonparametric multiple comparison procedures for unbalanced one-way factorial designs.</i>\" Journal of Statistical Planning and Inference, 77, 2574-2591, 2008.</li>
</ul>",
parameters=list(model=list(type="ANY"),
hypotheses=list(type="ANY"),
alpha=list(type="numeric")
)
)) }
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