MHDA-package {MHDA}

Massive Hierarchically Data Analysis

Description

Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories.

Details

Package:	MHDA
Type:	Package
Version:	1.2
Date:	2022-06-12
License:	GPL-2

Author(s)

Yi Ya and Jacob Zhang

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"))