R로 배우는 마케팅 선택 모델링
Elea McDonnell Feit
Assistant Professor of Marketing, Drexel University
유형밀크, 다크, 밀크 넛, 다크 넛, 화이트
브랜드Dove, Ghirardelli, Godiva, Hershey's, Lindt
가격0.5, 0.6, ... 0.1 단위 ..., 3.9, 4.0
choc_survey[choc_survey$Subject == 1 & choc_survey$Trial == 1, ]
Subject Trial Alt Type Brand Price 1 1 1 1 NA NA NA 2 1 1 2 NA NA NA 3 1 1 3 NA NA NA
# Setup your attributes and levels list attribs <- list(Type = c("Milk", "Dark", "White"), Brand = c("Cadbury", "Toblerone", "Kinder"), Price = 5:30 / 10) # Create all possible combinations of attributes all_comb <- expand.grid(attribs) nrow(all_comb) head(all_comb)
# Setup your attributes and levels list attribs <- list(Type = c("Milk", "Dark", "White"), Brand = c("Cadbury", "Toblerone", "Kinder"), Price = 5:30 / 10)
# Create all possible combinations of attributes all_comb <- expand.grid(attribs) nrow(all_comb) head(all_comb)
144 Type Brand Price 1 Milk Cadbury 0.5 2 Dark Cadbury 0.5 3 White Cadbury 0.5 4 Milk Toblerone 0.5 5 Dark Toblerone 0.5 6 White Toblerone 0.5
144
Type Brand Price 1 Milk Cadbury 0.5 2 Dark Cadbury 0.5 3 White Cadbury 0.5 4 Milk Toblerone 0.5 5 Dark Toblerone 0.5 6 White Toblerone 0.5
for (i in 1:100) { rand_rows <- sample(1:nrow(all_comb), size = 12 * 3) rand_alts <- all_comb[rand_rows, ] choc_survey[choc_survey$Subject == i, 4:6] <- rand_alts }
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