Sphericity Test for Covariance Matrices in R (sphericity.test)

sphericity.test<-function(n,s1,s2=NULL,estsigma=TRUE){
#### Performs a hypothesis test that a covariance matrix is of specified 
#### form. Test is of the form H0: S1=sigma^2*S2. n is the number of
#### observations on which the sample covariance matrix is based.
#### If the input parameter estsigma is TRUE: 
#### Perform test of the hypothesis that S1=sigma^2 S2, for unknown sigma. 
#### If S2 not specified, assumed that S2=I. Reference is Basilevsky, 
#### Statistical Factor Analysis and Related Methods, page 191. 
#### If the input parameter estsigma is FALSE: 
#### Perform test of the hypothesis that S1=S2. If S2 not specified, 
#### assumed that S2=I. Reference is Seber, Multivariate Observations, 
#### sec 3.5.4 
#### Only the lower triangle+diagonal is required at entry, and the upper 
#### triangle is ignored. 
#### DAW July 2000
dname <- paste(substitute(s1))
p<-nrow(s1)
for (i in 1:(p-1)){for (j in ((i+1):p)){
    s1[i,j]<-s1[j,i]
    s2[i,j]<-s2[j,i] }}
if (!is.null(s2)){
    b<-eigen(s2,symmetric=T,only.values=F)
    r<-b$vectors %*% diag(1/sqrt(b$values))
    s<-t(r) %*% s1 %*% r }
else { s<-s1 }
 
d<-eigen(s,symmetric=T,only.values=T)$values
ldet<-sum(log(d))
tr<-sum(d)
 
if (estsigma==TRUE){
    sighat<-tr/p
    cc<--(n-(2*p^2+p+2)/(6*p))*(ldet-p*log(tr/p))
    statistic <- cc
    sighat<-sighat
    names(statistic) <- "L statistic"
    parameter <- 0.5*(p+2)*(p-1)
    names(parameter) <- "df"
    rval<-list(data.name=dname,sighat=sighat,statistic=statistic,parameter=parameter,p.value=1-pchisq(statistic,parameter),method="Sphericity test") }
else {
    cc<--n*(p+ldet-tr)
    statistic <- cc
    names(statistic) <- "L statistic"
    parameter <- 0.5*(p+1)*p
    names(parameter) <- "df"
    rval<-list(data.name="",statistic=statistic,parameter=parameter,p.value=1-pchisq(statistic,parameter),method="Covariance equality test statistic")
}
class(rval) <- "htest"
return(rval)
}
pw <- function(q,n) {
   pdf <- function(w) { 1/2 * (n-2) * w^((n-3)/2) }
   integrate(pdf,0,q)
}
 
varcomp <- function(covmat,n) {
   if (is.list(covmat)) {
    if (length(covmat) < 2)
        stop("covmat must be a list with at least 2 elements")
    ps <- as.vector(sapply(covmat,dim))
    if (sum(ps[1] == ps) != length(ps))
        stop("all covariance matrices must have the same dimension")
    p <- ps[1]
        q <- length(covmat)
        if (length(n) == 1)
        Ng <- rep(n,q)
    else if (length(n) == q)
        Ng <- n
    else
        stop("n must be equal length(covmat) or 1")
 
    DNAME <- deparse(substitute(covmat))
   }
 
   else
    stop("covmat must be a list")
 
   ng <- Ng - 1
   Ag <- lapply(1:length(covmat),function(i,mat,n) { n[i] * mat[[i]] },mat=covmat,n=ng)
   A <- matrix(colSums(matrix(unlist(Ag),ncol=p^2,byrow=T)),ncol=p)
   detAg <- sapply(Ag,det)
   detA <- det(A)
   V1 <- prod(detAg^(ng/2))/(detA^(sum(ng)/2))
   kg <- ng/sum(ng)
   l1 <- prod((1/kg)^kg)^(p*sum(ng)/2) * V1
   rho <- 1 - (sum(1/ng) - 1/sum(ng))*(2*p^2+3*p-1)/(6*(p+1)*(q-1))
   w2 <- p*(p+1) * ((p-1)*(p+2) * (sum(1/ng^2) - 1/(sum(ng)^2)) - 6*(q-1)*(1-rho)^2) / (48*rho^2)
   f <- 0.5 * (q-1)*p*(p+1)
   STATISTIC <- -2*rho*log(l1)
   PVAL <- 1 - (pchisq(STATISTIC,f) + w2*(pchisq(STATISTIC,f+4) - pchisq(STATISTIC,f)))
   names(STATISTIC) <- "corrected lambda*"
   names(f) <- "df"
   RVAL <- structure(list(statistic = STATISTIC, parameter = f,p.value = PVAL, data.name = DNAME, method = "Equality of Covariances Matrices Test"),class="htest")
   return(RVAL)
}