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#' @title Box's M Test
#'
#' @description
#' \code{BoxM} function tests whether the covariance matrices of independent
#' samples are equal or not.
#'
#' @details
#' This function computes Box-M test statistic for the covariance matrices of independent samples.
#' The hypotheses are defined as H0:The Covariance matrices are homogeneous and
#' H1:The Covariance matrices are not homogeneous
#'
#' @importFrom stats cor cov pchisq pf pnorm qchisq qf var
#' @param data a data frame.
#' @param group grouping vector.
#'
#' @export
#' @return a list with 3 elements:
#' \item{ChiSquare}{The value of Test Statistic}
#' \item{df}{The Chi-Square statistic's degree of freedom}
#' \item{p.value}{p value}
#' @author Hasan BULUT <hasan.bulut@omu.edu.tr>
#' @references Rencher, A. C. (2003). Methods of multivariate analysis (Vol. 492). John Wiley & Sons.
#' @examples
#'
#' data(iris)
#' results <- BoxM(data=iris[,1:4],group=iris[,5])
#' summary(results)
BoxM<-function(data,group){
Name<-"BoxM"
data<-as.matrix(data)
group<-as.factor(group)
g<-length(levels(group))
Levels<-levels(group)
N<-nrow(data)
p<-ncol(data)
## Calculation S pooled ##
E<-matrix(0,p,p)
ns<-NULL
#Calculation E matrix
for (i in 1:g) {
ns[i]<-length(which(group==Levels[i]))
Si<-cov(data[which(group==Levels[i]),])
E<-E+(ns[i]-1)*Si
}
# Calculation Spl
S.pooled<-(1/(N-g))*E
## Calculation M using Equation (7.21) ##
M=1
for (i in 1:g){
Si<-cov(data[which(group==Levels[i]),])
M<-M*(det(Si)/det(S.pooled))^((ns[i]-1)/2)
}
#Calculation C
k1<-(2*p^2+3*p-1)/(6*(p+1)*(g-1)) #ns are different
k2<-((g+1)*(2*p^2+3*p-1))/(6*g*(p+1)*(N/g-1)) #ns are same-Equation 7.25
T=0
if (all(ns==mean(ns))==TRUE) { #ns are same
C<-k2
} else {
for (i in 1:g) {
T<-T+(1/(ns[i]-1))
}
C<-k1*(T-(1/(N-g))) # Equation 7.22
}
# Calculation U Statistic
U<--2*(1-C)*log(M) #Equation 7.23
df<-(0.5*p*(p+1)*(g-1))
pval<-pchisq(U, df=df,lower.tail = FALSE)
results <- list(Chisq=U,df=df,p.value=pval,Test=Name)
class(results)<-c("MVTests","list")
return(results)
}
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