orangeJuice

0th

Percentile

Store-level Panel Data on Orange Juice Sales

yx, weekly sales of refrigerated orange juice at 83 stores. storedemo, contains demographic information on those stores.

Keywords
datasets
Usage
data(orangeJuice)
Details

store

store number

brand

brand indicator

week

week number

logmove

log of the number of units sold

constant

a vector of 1

price1

price of brand 1

deal

in-store coupon activity

feature

feature advertisement

STORE

store number

AGE60

percentage of the population that is aged 60 or older

EDUC

percentage of the population that has a college degree

ETHNIC

percent of the population that is black or Hispanic

INCOME

median income

HHLARGE

percentage of households with 5 or more persons

WORKWOM

percentage of women with full-time jobs

HVAL150

percentage of households worth more than $150,000

SSTRDIST

distance to the nearest warehouse store

SSTRVOL

ratio of sales of this store to the nearest warehouse store

CPDIST5

average distance in miles to the nearest 5 supermarkets

CPWVOL5

ratio of sales of this store to the average of the nearest five stores

Format

This R object is a list of two data frames, list(yx,storedemo).

List of 2 $ yx :'data.frame': 106139 obs. of 19 variables: … $ store : int [1:106139] 2 2 2 2 2 2 2 2 2 2 … $ brand : int [1:106139] 1 1 1 1 1 1 1 1 1 1 … $ week : int [1:106139] 40 46 47 48 50 51 52 53 54 57 … $ logmove : num [1:106139] 9.02 8.72 8.25 8.99 9.09 … $ constant: int [1:106139] 1 1 1 1 1 1 1 1 1 1 … $ price1 : num [1:106139] 0.0605 0.0605 0.0605 0.0605 0.0605 … $ price2 : num [1:106139] 0.0605 0.0603 0.0603 0.0603 0.0603 … $ price3 : num [1:106139] 0.0420 0.0452 0.0452 0.0498 0.0436 … $ price4 : num [1:106139] 0.0295 0.0467 0.0467 0.0373 0.0311 … $ price5 : num [1:106139] 0.0495 0.0495 0.0373 0.0495 0.0495 … $ price6 : num [1:106139] 0.0530 0.0478 0.0530 0.0530 0.0530 … $ price7 : num [1:106139] 0.0389 0.0458 0.0458 0.0458 0.0466 … $ price8 : num [1:106139] 0.0414 0.0280 0.0414 0.0414 0.0414 … $ price9 : num [1:106139] 0.0289 0.0430 0.0481 0.0423 0.0423 … $ price10 : num [1:106139] 0.0248 0.0420 0.0327 0.0327 0.0327 … $ price11 : num [1:106139] 0.0390 0.0390 0.0390 0.0390 0.0382 … $ deal : int [1:106139] 1 0 0 0 0 0 1 1 1 1 … $ feat : num [1:106139] 0 0 0 0 0 0 0 0 0 0 … $ profit : num [1:106139] 38.0 30.1 30.0 29.9 29.9

1 Tropicana Premium 64 oz; 2 Tropicana Premium 96 oz; 3 Florida's Natural 64 oz; 4 Tropicana 64 oz; 5 Minute Maid 64 oz; 6 Minute Maid 96 oz; 7 Citrus Hill 64 oz; 8 Tree Fresh 64 oz; 9 Florida Gold 64 oz; 10 Dominicks 64 oz; 11 Dominicks 128 oz.

$ storedemo:'data.frame': 83 obs. of 12 variables: … $ STORE : int [1:83] 2 5 8 9 12 14 18 21 28 32 … $ AGE60 : num [1:83] 0.233 0.117 0.252 0.269 0.178 … $ EDUC : num [1:83] 0.2489 0.3212 0.0952 0.2222 0.2534 … $ ETHNIC : num [1:83] 0.1143 0.0539 0.0352 0.0326 0.3807 … $ INCOME : num [1:83] 10.6 10.9 10.6 10.8 10.0 … $ HHLARGE : num [1:83] 0.1040 0.1031 0.1317 0.0968 0.0572 … $ WORKWOM : num [1:83] 0.304 0.411 0.283 0.359 0.391 … $ HVAL150 : num [1:83] 0.4639 0.5359 0.0542 0.5057 0.3866 … $ SSTRDIST: num [1:83] 2.11 3.80 2.64 1.10 9.20 … $ SSTRVOL : num [1:83] 1.143 0.682 1.500 0.667 1.111 … $ CPDIST5 : num [1:83] 1.93 1.60 2.91 1.82 0.84 … $ CPWVOL5 : num [1:83] 0.377 0.736 0.641 0.441 0.106

References

Chapter 5, Bayesian Statistics and Marketing by Rossi et al. http://www.perossi.org/home/bsm-1

Aliases
  • orangeJuice
Examples

## Example 
## load data
data(orangeJuice)

## print some quantiles of yx data  
cat("Quantiles of the Variables in yx data",fill=TRUE)
mat=apply(as.matrix(orangeJuice$yx),2,quantile)
print(mat)

## print some quantiles of storedemo data
cat("Quantiles of the Variables in storedemo data",fill=TRUE)
mat=apply(as.matrix(orangeJuice$storedemo),2,quantile)
print(mat)


## Example 2 processing for use with rhierLinearModel
##
##
if(0)
{

## select brand 1 for analysis
brand1=orangeJuice$yx[(orangeJuice$yx$brand==1),]

store = sort(unique(brand1$store))
nreg = length(store)
nvar=14

regdata=NULL
for (reg in 1:nreg) {
        y=brand1$logmove[brand1$store==store[reg]]
        iota=c(rep(1,length(y)))
        X=cbind(iota,log(brand1$price1[brand1$store==store[reg]]),
                     log(brand1$price2[brand1$store==store[reg]]),
                     log(brand1$price3[brand1$store==store[reg]]),
                     log(brand1$price4[brand1$store==store[reg]]),
                     log(brand1$price5[brand1$store==store[reg]]),
                     log(brand1$price6[brand1$store==store[reg]]),
                     log(brand1$price7[brand1$store==store[reg]]),
                     log(brand1$price8[brand1$store==store[reg]]),
                     log(brand1$price9[brand1$store==store[reg]]),
                     log(brand1$price10[brand1$store==store[reg]]),
                     log(brand1$price11[brand1$store==store[reg]]),
                     brand1$deal[brand1$store==store[reg]],
                     brand1$feat[brand1$store==store[reg]])
        regdata[[reg]]=list(y=y,X=X)
      }

## storedemo is standardized to zero mean.

Z=as.matrix(orangeJuice$storedemo[,2:12]) 
dmean=apply(Z,2,mean)
for (s in 1:nreg){
        Z[s,]=Z[s,]-dmean
}
iotaz=c(rep(1,nrow(Z)))
Z=cbind(iotaz,Z)
nz=ncol(Z)


Data=list(regdata=regdata,Z=Z)
Mcmc=list(R=R,keep=1)

out=rhierLinearModel(Data=Data,Mcmc=Mcmc)

summary(out$Deltadraw)
summary(out$Vbetadraw)

if(0){
## plotting examples
plot(out$betadraw)
}
}

Documentation reproduced from package bayesm, version 3.0-2, License: GPL (>= 2)

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