orangeJuice {bayesm} | R Documentation |
yx, weekly sales of refrigerated orange juice at 83 stores.
storedemo, contains demographic information on those stores.
data(orangeJuice)
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
store
brand
week
logmove
constant
price1
deal
feature
STORE
AGE60
EDUC
ETHNIC
INCOME
HHLARGE
WORKWOM
HVAL150
SSTRDIST
SSTRVOL
CPDIST5
CPWVOL5
Alan L. Montgomery (1997), "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," Marketing Science 16(4) 315-337.
Chapter 5, Bayesian Statistics and Marketing by Rossi et al.
http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html
## 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) } }