MHDA (version 2.0)

MHDA-package: Massive Hierarchically Data Analysis

Description

Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories are demonstrated. Flexible and diverse applications of the function parameters make the data analyses powerful.

Author

Yarong Yang and Jacob Zhang

Details

Package:	MHDA
Type:	Package
Version:	2.0
Date:	2025-09-02
License:	GPL-2

References

Yarong Yang and Jacob Zhang.(2025) MHDA: Massive Hierarchically Data Analysis. manuscript in preparation

Examples

##generating a small data for example###
Slots<-c("2021-01","2021-02")
Units<-c("Store-1","Store-2","Store-3","Store-4")
Class.I<-c("Mall_1","Mall_2","Mall_3","Mall_a","Mall_b","Mall_c")
Class.II<-c("B&H","F&B","HOM","KID","LEI&ENT","RET-SHO-ACC","SPM&SER")
Infor.1<-c("Mall_2","HOM")
Infor.2<-c("Mall_c","B&H")
Infor.3<-c("Mall_2","KID")
Infor.4<-c("Mall_c","F&B")
Store.sales<-list()
Store.sales[[1]]<-Store.sales[[2]]<-list()
names(Store.sales)<-Slots
for(i in 1:2) {
    for(j in 1:4) {
        Store.sales[[i]][[j]]<-list()
        n<-sample(1:30,1)
        for(k in 1:n) {
            t<-Store.sales[[i]][[j]][[k]]<-abs(rnorm(sample(1:50,1),0,1))
            names(Store.sales[[i]][[j]][[k]])<-sample(c(0,1),length(t),replace=TRUE)
        }
        names(Store.sales[[i]][[j]])<-paste("s",1:n,sep="")
    }
    Store.sales[[i]][[4+1]]<-c(Infor.1[1],Infor.2[1],Infor.3[1],Infor.4[1])
    Store.sales[[i]][[4+2]]<-c(Infor.1[2],Infor.2[2],Infor.3[2],Infor.4[2])
    names(Store.sales[[i]])<-c(Units,"Level.I","Level.II")
}

Res<-MHDA(Data=Store.sales,data.infor=NULL,type="Value",is.binary=TRUE,Unit=NULL,
Category.I="Mall_c",Category.II=Class.II,Slot=c("2021-01","2021-02"))