knitr::opts_chunk$set(echo = TRUE)
if(!require("tidyverse")) {install.packages("tidyverse"); library("tidyverse")}
if(!require("klaR")) {install.packages("klaR"); library("klaR")}
if(!require("caret")) {install.packages("caret"); library("caret")}
if(!require("cowplot")) {install.packages("cowplot"); library("cowplot")}
if(!require("scales")) {install.packages("scales"); library("scales")}
if(!require("Hmisc")) {install.packages("Hmisc"); library("Hmisc")}
if(!require("sjlabelled")) {install.packages("sjlabelled"); library("sjlabelled")}
# clean up working environment
rm(list = ls())
mydatc4_case.1 <- data.frame(
"ID" = c( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127 ),
"Price" = c( 3, 6, 2, 4, 7, 5, 6, 3, 7, 3, 7, 7, 6, 3, 4, 4, 3, 6, 7, 5, 3, 6, 4, 6, 6, 6, 5, 6, 3, 7, 2, 6, 5, 5, 4, 7, 5, 4, 2, 3, 3, 7, 3, 6, 4, 2, 3, 4, 6, 1, 2, 2, 3, 2, 5, 1, 3, 3, 5, 7, 3, 2, 3, 2, 6, 2, 3, 7, 5, 3, 6, 5, 6, 6, 4, 5, 5, 5, 6, 7, 5, 7, 5, 7, 6, 6, 6, 6, 7, 6, 5, 5, 4, 7, 7, 7, 5, 5, 6, 5, 2, 6, 5, 5, 4, 4, 6, 5, 5, 5, 6, 6, 5, 4, 5, 4, 7, 4, 5, 5, 3, 5, 6, 4, 5, 3, 5 ),
"Refreshing" = c( 3, 6, 3, 3, 5, 4, 5, 3, 6, 4, 1, 7, 5, 3, 6, 3, 4, 3, 7, 2, 4, 2, 3, 2, 5, 2, 2, 5, 4, 7, 2, 5, 3, 4, 6, 4, 5, 5, 2, 6, 7, 7, 5, 2, 5, 7, 7, 6, 7, 7, 2, 4, 6, 4, 4, 3, 6, 7, 4, 5, 5, 7, 1, 2, 4, 1, 4, 4, 3, 4, 2, 3, 3, 4, 2, 3, 3, 5, 4, 5, 4, 6, 2, 5, 2, 4, 6, 2, 6, 6, 4, 4, 5, 6, 4, 7, 2, 4, 4, 2, 2, 4, 6, 4, 3, 7, 5, 5, 1, 4, NA, 5, 3, 1, 3, 4, 5, 4, 3, 3, 3, 5, 7, 3, 4, 4, 4 ),
"Delicious" = c( 5, 5, 3, 3, 5, 5, 6, 3, 6, 4, 4, 3, 4, 4, 2, 4, 4, 5, 3, 3, 4, 5, 4, 4, 7, 6, 4, 6, 6, 4, 5, 4, 4, 4, 4, 6, 4, 4, 2, 3, 3, 7, 3, 3, 4, 5, 5, 6, 5, 1, 2, 4, 2, 3, 5, 3, 4, 3, 4, 6, 5, 3, 3, 2, 4, 2, 6, 6, 6, 3, 6, 4, 5, 5, 4, 4, 4, 5, 5, 4, 4, 6, 5, 4, 6, 5, 6, 6, 2, 6, 4, 4, 4, 7, 2, 7, 5, 4, 4, 6, 5, 6, 5, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 4, 5, 4, 4, 4, 4, 5, 3, 5, 5, 4, 4, 4, 4 ),
"Healthy" = c( 4, 2, 3, 4, 7, 2, 5, 4, 2, 4, 5, 7, 3, 3, 4, 4, 4, 4, 2, 4, 4, 2, 3, 5, 3, 4, 4, 4, 2, NA, 4, 1, 4, 4, 4, 4, 3, 3, 2, 3, 1, 3, 2, 2, 5, 6, 6, 4, 4, NA, 6, 4, 2, 5, 3, 3, 4, 1, 4, 3, 4, 2, 6, 6, 4, 2, 4, 4, 5, 4, 4, 4, 5, 1, 2, 3, 5, 4, 5, 5, 4, 3, 2, 4, 4, 6, 4, 4, 4, 6, 3, 5, 4, 1, 6, 4, 2, 5, 6, 3, 4, 3, 4, 4, 3, 4, 4, 5, 3, 4, 4, 4, 4, 3, 6, 4, 4, 3, 4, 4, 4, 5, 5, 3, 6, 4, 1 ),
"Bitter" = c( 1, 2, 2, 4, 3, 5, 6, 3, 3, 2, 1, 1, 1, 1, 1, 4, 3, 4, 1, 4, 3, 3, 4, 6, 3, 6, 6, 6, 6, 4, 3, 1, 4, 4, 4, 5, 4, 4, 3, 7, 4, 7, 3, 4, 5, 5, 1, 6, 2, 7, 6, 4, 2, 2, 3, 1, 6, 4, 4, 6, 3, 6, 5, 6, 4, 2, 6, 5, 7, 2, 6, 5, 3, 5, 5, 4, 2, 5, 5, 3, 4, 2, 3, 3, 5, 6, 6, 6, 3, 3, 3, 3, 3, 7, 1, 5, 3, 3, 7, 5, 3, 5, 5, 4, 3, 4, 5, 4, 6, 5, 2, 4, 3, 1, 7, 4, 4, 2, 3, 4, 4, 4, 5, 3, 4, 4, 4 ),
"Light" = c( 2, 5, 3, 3, 6, 4, 5, 2, 7, 5, 4, 3, 2, 4, 3, 4, 3, 4, 2, 4, 4, 4, 5, 6, 2, 6, 5, 6, 4, 6, 5, 3, 3, 5, 4, 5, 3, 5, 2, 5, 6, 7, 3, 3, 5, 6, 7, 7, 6, 7, 5, 6, 4, 5, 4, 7, 6, 6, 4, 6, 5, 7, 5, 5, 4, 4, 6, 6, 6, 4, 6, 5, 6, 6, 3, 3, 1, 4, 4, 5, 4, 6, 5, 4, 6, 5, 6, 6, 4, 6, 5, 4, 3, 7, 2, 7, 2, 4, 5, 4, 5, 4, 5, 4, 5, 4, 4, 4, 4, 5, 6, 3, 4, 2, 5, 4, 4, 2, 3, 3, 5, 5, 5, 3, 5, 4, 4 ),
"Crunchy" = c( 3, 2, 5, 5, 5, 3, 6, 3, 5, 5, 1, 1, NA, 2, 1, 4, 3, 4, 5, 5, 3, 2, 4, 3, 3, 4, 4, 4, 6, 7, 3, 4, 4, 4, 3, 5, 1, 5, 6, 4, 3, 7, 5, 5, 5, 5, 6, 4, 6, 7, 5, 4, 5, 7, 5, 5, 7, 3, 3, 4, 6, 7, 6, 5, 4, 5, 7, 4, 2, 5, 4, 3, 3, 6, 5, 6, 4, 6, 6, 6, 3, 1, 2, 4, 2, 5, 3, 4, 3, 2, 4, 3, 3, 2, 3, 7, 2, 4, 6, 6, 3, 3, NA, 4, 3, 1, 5, 5, 5, 4, 5, 5, 3, 3, 7, 4, 4, 3, 3, 5, 3, 3, 5, 4, 5, 4, 1 ),
"Exotic" = c( 1, 1, 1, 2, 1, 7, 5, 1, 1, 1, 1, 1, 1, 3, 2, 4, 7, 3, 1, 3, 1, 1, 7, 1, 1, 6, 6, 5, 7, 1, 1, 1, 1, 4, 1, 1, 1, 1, 6, 7, 7, 1, 7, 1, 1, 7, 7, 5, 7, 7, 7, 2, 7, 2, 2, 7, 7, 7, 5, 1, 7, 7, NA, 7, NA, 7, 7, 1, 7, 2, 5, 1, 6, 7, 1, 1, 7, 5, 1, 1, 1, 1, 1, 1, 7, 1, 1, NA, 1, NA, 1, 1, NA, 7, 1, 1, 1, 1, 1, 7, 1, 1, 1, NA, 1, 1, 1, 7, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 5, 5, 5, 1, 1, 1, 1 ),
"Sweet" = c( 3, 6, 3, 4, 5, 7, 6, 3, 6, 4, 3, 4, 3, 4, 4, 4, 5, 4, 1, 3, 1, 3, 4, 5, 3, 5, 5, 4, 2, 5, 4, 3, 2, 4, 4, 3, 3, 3, 3, 4, 4, 6, 2, 5, 3, 5, 5, 5, 6, 1, 4, 4, 3, 4, 4, 7, 6, 4, 3, 5, 5, 7, 5, 4, 4, 2, 6, 5, 6, 4, 5, 4, 4, 3, 5, 5, 4, 4, 5, 5, 3, 2, 4, 5, 6, 6, 4, 5, 4, 4, 6, 4, 4, 7, 5, 6, 3, 4, 5, 3, 4, 3, 2, 4, 3, 3, 4, 3, 4, 4, 3, 4, 3, 3, 6, 4, 5, 2, 3, 5, 4, 4, 5, 3, 4, 4, 1 ),
"Fruity" = c( 4, 7, 2, 4, 5, 3, 5, 3, 3, 4, 5, 5, 1, 2, 4, 4, 5, 4, 1, 3, 3, 4, 4, 4, 1, 5, 4, 4, 4, 1, 5, 3, 2, 4, 4, 5, 3, 6, 2, 4, 5, 6, 4, 5, 6, 6, 6, 5, 5, 7, 6, 4, 6, 4, 4, 7, 7, 5, 3, 5, 5, 7, 4, 6, 4, 5, 7, 5, 3, 3, 5, 4, 4, 3, 4, 4, 1, 3, 6, 3, 2, 3, 5, 4, 3, 3, 2, 5, 4, 4, 5, 3, 3, 7, 2, 6, 4, 3, 6, 3, 5, 7, 4, 4, 3, 3, 4, 3, 4, 4, 4, 1, 4, 3, 6, 4, 5, 2, 3, 4, 3, 4, 5, 4, 4, 4, 4 ),
"Respondent" = c( 1, 3, 4, 7, 11, 12, 16, 18, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 2, 8, 10, 11, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 4, 7, 8, 10, 12, 13, 14, 16, 2, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 1, 2, 3, 4, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 17, 18, 4, 5, 7, 9, 12, 13, 14, 15, 16, 17, 18, 1, 2, 3, 4, 6, 8, 9, 13, 17, 18 ),
"Flavor" = c( 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11 ),
"Segment" = c( 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 )
)
mydatc4_case.1$Flavor <- factor(mydatc4_case.1$Flavor,
levels = c(1:11),
labels = c("Milk",
"Espresso",
"Biscuit",
"Orange",
"Strawberry",
"Mango",
"Cappuccino",
"Mousse",
"Caramel",
"Nougat",
"Nut"))
mydatc4_case.1$Segment <- factor(mydatc4_case.1$Segment,
levels = c(1:3),
labels = c("Seg_1 Classic",
"Seg_2 Fruit",
"Seg_3 Coffee"))
print(mydatc4_case.1)
## ID Price Refreshing Delicious Healthy Bitter Light Crunchy Exotic Sweet
## 1 1 3 3 5 4 1 2 3 1 3
## 2 2 6 6 5 2 2 5 2 1 6
## 3 3 2 3 3 3 2 3 5 1 3
## 4 4 4 3 3 4 4 3 5 2 4
## 5 5 7 5 5 7 3 6 5 1 5
## 6 6 5 4 5 2 5 4 3 7 7
## 7 7 6 5 6 5 6 5 6 5 6
## 8 8 3 3 3 4 3 2 3 1 3
## 9 9 7 6 6 2 3 7 5 1 6
## 10 10 3 4 4 4 2 5 5 1 4
## 11 11 7 1 4 5 1 4 1 1 3
## 12 12 7 7 3 7 1 3 1 1 4
## 13 13 6 5 4 3 1 2 NA 1 3
## 14 14 3 3 4 3 1 4 2 3 4
## 15 15 4 6 2 4 1 3 1 2 4
## 16 16 4 3 4 4 4 4 4 4 4
## 17 17 3 4 4 4 3 3 3 7 5
## 18 18 6 3 5 4 4 4 4 3 4
## 19 19 7 7 3 2 1 2 5 1 1
## 20 20 5 2 3 4 4 4 5 3 3
## 21 21 3 4 4 4 3 4 3 1 1
## 22 22 6 2 5 2 3 4 2 1 3
## 23 23 4 3 4 3 4 5 4 7 4
## 24 24 6 2 4 5 6 6 3 1 5
## 25 25 6 5 7 3 3 2 3 1 3
## 26 26 6 2 6 4 6 6 4 6 5
## 27 27 5 2 4 4 6 5 4 6 5
## 28 28 6 5 6 4 6 6 4 5 4
## 29 29 3 4 6 2 6 4 6 7 2
## 30 30 7 7 4 NA 4 6 7 1 5
## 31 31 2 2 5 4 3 5 3 1 4
## 32 32 6 5 4 1 1 3 4 1 3
## 33 33 5 3 4 4 4 3 4 1 2
## 34 34 5 4 4 4 4 5 4 4 4
## 35 35 4 6 4 4 4 4 3 1 4
## 36 36 7 4 6 4 5 5 5 1 3
## 37 37 5 5 4 3 4 3 1 1 3
## 38 38 4 5 4 3 4 5 5 1 3
## 39 39 2 2 2 2 3 2 6 6 3
## 40 40 3 6 3 3 7 5 4 7 4
## 41 41 3 7 3 1 4 6 3 7 4
## 42 42 7 7 7 3 7 7 7 1 6
## 43 43 3 5 3 2 3 3 5 7 2
## 44 44 6 2 3 2 4 3 5 1 5
## 45 45 4 5 4 5 5 5 5 1 3
## 46 46 2 7 5 6 5 6 5 7 5
## 47 47 3 7 5 6 1 7 6 7 5
## 48 48 4 6 6 4 6 7 4 5 5
## 49 49 6 7 5 4 2 6 6 7 6
## 50 50 1 7 1 NA 7 7 7 7 1
## 51 51 2 2 2 6 6 5 5 7 4
## 52 52 2 4 4 4 4 6 4 2 4
## 53 53 3 6 2 2 2 4 5 7 3
## 54 54 2 4 3 5 2 5 7 2 4
## 55 55 5 4 5 3 3 4 5 2 4
## 56 56 1 3 3 3 1 7 5 7 7
## 57 57 3 6 4 4 6 6 7 7 6
## 58 58 3 7 3 1 4 6 3 7 4
## 59 59 5 4 4 4 4 4 3 5 3
## 60 60 7 5 6 3 6 6 4 1 5
## 61 61 3 5 5 4 3 5 6 7 5
## 62 62 2 7 3 2 6 7 7 7 7
## 63 63 3 1 3 6 5 5 6 NA 5
## 64 64 2 2 2 6 6 5 5 7 4
## 65 65 6 4 4 4 4 4 4 NA 4
## 66 66 2 1 2 2 2 4 5 7 2
## 67 67 3 4 6 4 6 6 7 7 6
## 68 68 7 4 6 4 5 6 4 1 5
## 69 69 5 3 6 5 7 6 2 7 6
## 70 70 3 4 3 4 2 4 5 2 4
## 71 71 6 2 6 4 6 6 4 5 5
## 72 72 5 3 4 4 5 5 3 1 4
## 73 73 6 3 5 5 3 6 3 6 4
## 74 74 6 4 5 1 5 6 6 7 3
## 75 75 4 2 4 2 5 3 5 1 5
## 76 76 5 3 4 3 4 3 6 1 5
## 77 77 5 3 4 5 2 1 4 7 4
## 78 78 5 5 5 4 5 4 6 5 4
## 79 79 6 4 5 5 5 4 6 1 5
## 80 80 7 5 4 5 3 5 6 1 5
## 81 81 5 4 4 4 4 4 3 1 3
## 82 82 7 6 6 3 2 6 1 1 2
## 83 83 5 2 5 2 3 5 2 1 4
## 84 84 7 5 4 4 3 4 4 1 5
## 85 85 6 2 6 4 5 6 2 7 6
## 86 86 6 4 5 6 6 5 5 1 6
## 87 87 6 6 6 4 6 6 3 1 4
## 88 88 6 2 6 4 6 6 4 NA 5
## 89 89 7 6 2 4 3 4 3 1 4
## 90 90 6 6 6 6 3 6 2 NA 4
## 91 91 5 4 4 3 3 5 4 1 6
## 92 92 5 4 4 5 3 4 3 1 4
## 93 93 4 5 4 4 3 3 3 NA 4
## 94 94 7 6 7 1 7 7 2 7 7
## 95 95 7 4 2 6 1 2 3 1 5
## 96 96 7 7 7 4 5 7 7 1 6
## 97 97 5 2 5 2 3 2 2 1 3
## 98 98 5 4 4 5 3 4 4 1 4
## 99 99 6 4 4 6 7 5 6 1 5
## 100 100 5 2 6 3 5 4 6 7 3
## 101 101 2 2 5 4 3 5 3 1 4
## 102 102 6 4 6 3 5 4 3 1 3
## 103 103 5 6 5 4 5 5 NA 1 2
## 104 104 5 4 4 4 4 4 4 NA 4
## 105 105 4 3 4 3 3 5 3 1 3
## 106 106 4 7 4 4 4 4 1 1 3
## 107 107 6 5 4 4 5 4 5 1 4
## 108 108 5 5 4 5 4 4 5 7 3
## 109 109 5 1 4 3 6 4 5 1 4
## 110 110 5 4 4 4 5 5 4 1 4
## 111 111 6 NA 3 4 2 6 5 1 3
## 112 112 6 5 4 4 4 3 5 1 4
## 113 113 5 3 3 4 3 4 3 1 3
## 114 114 4 1 4 3 1 2 3 1 3
## 115 115 5 3 5 6 7 5 7 1 6
## 116 116 4 4 4 4 4 4 4 1 4
## 117 117 7 5 4 4 4 4 4 3 5
## 118 118 4 4 4 3 2 2 3 1 2
## 119 119 5 3 4 4 3 3 3 1 3
## 120 120 5 3 5 4 4 3 5 1 5
## 121 121 3 3 3 4 4 5 3 5 4
## 122 122 5 5 5 5 4 5 3 5 4
## 123 123 6 7 5 5 5 5 5 5 5
## 124 124 4 3 4 3 3 3 4 1 3
## 125 125 5 4 4 6 4 5 5 1 4
## 126 126 3 4 4 4 4 4 4 1 4
## 127 127 5 4 4 1 4 4 1 1 1
## Fruity Respondent Flavor Segment
## 1 4 1 Milk Seg_1 Classic
## 2 7 3 Milk Seg_1 Classic
## 3 2 4 Milk Seg_1 Classic
## 4 4 7 Milk Seg_1 Classic
## 5 5 11 Milk Seg_1 Classic
## 6 3 12 Milk Seg_1 Classic
## 7 5 16 Milk Seg_1 Classic
## 8 3 18 Milk Seg_1 Classic
## 9 3 2 Espresso Seg_3 Coffee
## 10 4 4 Espresso Seg_3 Coffee
## 11 5 7 Espresso Seg_3 Coffee
## 12 5 8 Espresso Seg_3 Coffee
## 13 1 9 Espresso Seg_3 Coffee
## 14 2 10 Espresso Seg_3 Coffee
## 15 4 11 Espresso Seg_3 Coffee
## 16 4 12 Espresso Seg_3 Coffee
## 17 5 13 Espresso Seg_3 Coffee
## 18 4 14 Espresso Seg_3 Coffee
## 19 1 15 Espresso Seg_3 Coffee
## 20 3 16 Espresso Seg_3 Coffee
## 21 3 1 Biscuit Seg_1 Classic
## 22 4 3 Biscuit Seg_1 Classic
## 23 4 4 Biscuit Seg_1 Classic
## 24 4 5 Biscuit Seg_1 Classic
## 25 1 6 Biscuit Seg_1 Classic
## 26 5 7 Biscuit Seg_1 Classic
## 27 4 8 Biscuit Seg_1 Classic
## 28 4 9 Biscuit Seg_1 Classic
## 29 4 10 Biscuit Seg_1 Classic
## 30 1 11 Biscuit Seg_1 Classic
## 31 5 12 Biscuit Seg_1 Classic
## 32 3 13 Biscuit Seg_1 Classic
## 33 2 14 Biscuit Seg_1 Classic
## 34 4 15 Biscuit Seg_1 Classic
## 35 4 16 Biscuit Seg_1 Classic
## 36 5 17 Biscuit Seg_1 Classic
## 37 3 18 Biscuit Seg_1 Classic
## 38 6 2 Orange Seg_2 Fruit
## 39 2 8 Orange Seg_2 Fruit
## 40 4 10 Orange Seg_2 Fruit
## 41 5 11 Orange Seg_2 Fruit
## 42 6 13 Orange Seg_2 Fruit
## 43 4 1 Strawberry Seg_2 Fruit
## 44 5 2 Strawberry Seg_2 Fruit
## 45 6 3 Strawberry Seg_2 Fruit
## 46 6 4 Strawberry Seg_2 Fruit
## 47 6 5 Strawberry Seg_2 Fruit
## 48 5 6 Strawberry Seg_2 Fruit
## 49 5 7 Strawberry Seg_2 Fruit
## 50 7 8 Strawberry Seg_2 Fruit
## 51 6 9 Strawberry Seg_2 Fruit
## 52 4 10 Strawberry Seg_2 Fruit
## 53 6 11 Strawberry Seg_2 Fruit
## 54 4 12 Strawberry Seg_2 Fruit
## 55 4 13 Strawberry Seg_2 Fruit
## 56 7 14 Strawberry Seg_2 Fruit
## 57 7 15 Strawberry Seg_2 Fruit
## 58 5 16 Strawberry Seg_2 Fruit
## 59 3 17 Strawberry Seg_2 Fruit
## 60 5 18 Strawberry Seg_2 Fruit
## 61 5 4 Mango Seg_2 Fruit
## 62 7 7 Mango Seg_2 Fruit
## 63 4 8 Mango Seg_2 Fruit
## 64 6 10 Mango Seg_2 Fruit
## 65 4 12 Mango Seg_2 Fruit
## 66 5 13 Mango Seg_2 Fruit
## 67 7 14 Mango Seg_2 Fruit
## 68 5 16 Mango Seg_2 Fruit
## 69 3 2 Cappuccino Seg_3 Coffee
## 70 3 6 Cappuccino Seg_3 Coffee
## 71 5 7 Cappuccino Seg_3 Coffee
## 72 4 8 Cappuccino Seg_3 Coffee
## 73 4 9 Cappuccino Seg_3 Coffee
## 74 3 12 Cappuccino Seg_3 Coffee
## 75 4 13 Cappuccino Seg_3 Coffee
## 76 4 14 Cappuccino Seg_3 Coffee
## 77 1 15 Cappuccino Seg_3 Coffee
## 78 3 16 Cappuccino Seg_3 Coffee
## 79 6 17 Cappuccino Seg_3 Coffee
## 80 3 18 Cappuccino Seg_3 Coffee
## 81 2 1 Mousse Seg_1 Classic
## 82 3 2 Mousse Seg_1 Classic
## 83 5 3 Mousse Seg_1 Classic
## 84 4 4 Mousse Seg_1 Classic
## 85 3 6 Mousse Seg_1 Classic
## 86 3 8 Mousse Seg_1 Classic
## 87 2 10 Mousse Seg_1 Classic
## 88 5 11 Mousse Seg_1 Classic
## 89 4 12 Mousse Seg_1 Classic
## 90 4 13 Mousse Seg_1 Classic
## 91 5 14 Mousse Seg_1 Classic
## 92 3 15 Mousse Seg_1 Classic
## 93 3 16 Mousse Seg_1 Classic
## 94 7 17 Mousse Seg_1 Classic
## 95 2 1 Caramel Seg_1 Classic
## 96 6 2 Caramel Seg_1 Classic
## 97 4 3 Caramel Seg_1 Classic
## 98 3 4 Caramel Seg_1 Classic
## 99 6 5 Caramel Seg_1 Classic
## 100 3 6 Caramel Seg_1 Classic
## 101 5 8 Caramel Seg_1 Classic
## 102 7 9 Caramel Seg_1 Classic
## 103 4 11 Caramel Seg_1 Classic
## 104 4 13 Caramel Seg_1 Classic
## 105 3 17 Caramel Seg_1 Classic
## 106 3 18 Caramel Seg_1 Classic
## 107 4 4 Nougat Seg_1 Classic
## 108 3 5 Nougat Seg_1 Classic
## 109 4 7 Nougat Seg_1 Classic
## 110 4 9 Nougat Seg_1 Classic
## 111 4 12 Nougat Seg_1 Classic
## 112 1 13 Nougat Seg_1 Classic
## 113 4 14 Nougat Seg_1 Classic
## 114 3 15 Nougat Seg_1 Classic
## 115 6 16 Nougat Seg_1 Classic
## 116 4 17 Nougat Seg_1 Classic
## 117 5 18 Nougat Seg_1 Classic
## 118 2 1 Nut Seg_1 Classic
## 119 3 2 Nut Seg_1 Classic
## 120 4 3 Nut Seg_1 Classic
## 121 3 4 Nut Seg_1 Classic
## 122 4 6 Nut Seg_1 Classic
## 123 5 8 Nut Seg_1 Classic
## 124 4 9 Nut Seg_1 Classic
## 125 4 13 Nut Seg_1 Classic
## 126 4 17 Nut Seg_1 Classic
## 127 4 18 Nut Seg_1 Classic
mydatc4_case.2 <- na.omit(mydatc4_case.1)
dim(mydatc4_case.2); print(mydatc4_case.2)
## [1] 116 14
## ID Price Refreshing Delicious Healthy Bitter Light Crunchy Exotic Sweet
## 1 1 3 3 5 4 1 2 3 1 3
## 2 2 6 6 5 2 2 5 2 1 6
## 3 3 2 3 3 3 2 3 5 1 3
## 4 4 4 3 3 4 4 3 5 2 4
## 5 5 7 5 5 7 3 6 5 1 5
## 6 6 5 4 5 2 5 4 3 7 7
## 7 7 6 5 6 5 6 5 6 5 6
## 8 8 3 3 3 4 3 2 3 1 3
## 9 9 7 6 6 2 3 7 5 1 6
## 10 10 3 4 4 4 2 5 5 1 4
## 11 11 7 1 4 5 1 4 1 1 3
## 12 12 7 7 3 7 1 3 1 1 4
## 14 14 3 3 4 3 1 4 2 3 4
## 15 15 4 6 2 4 1 3 1 2 4
## 16 16 4 3 4 4 4 4 4 4 4
## 17 17 3 4 4 4 3 3 3 7 5
## 18 18 6 3 5 4 4 4 4 3 4
## 19 19 7 7 3 2 1 2 5 1 1
## 20 20 5 2 3 4 4 4 5 3 3
## 21 21 3 4 4 4 3 4 3 1 1
## 22 22 6 2 5 2 3 4 2 1 3
## 23 23 4 3 4 3 4 5 4 7 4
## 24 24 6 2 4 5 6 6 3 1 5
## 25 25 6 5 7 3 3 2 3 1 3
## 26 26 6 2 6 4 6 6 4 6 5
## 27 27 5 2 4 4 6 5 4 6 5
## 28 28 6 5 6 4 6 6 4 5 4
## 29 29 3 4 6 2 6 4 6 7 2
## 31 31 2 2 5 4 3 5 3 1 4
## 32 32 6 5 4 1 1 3 4 1 3
## 33 33 5 3 4 4 4 3 4 1 2
## 34 34 5 4 4 4 4 5 4 4 4
## 35 35 4 6 4 4 4 4 3 1 4
## 36 36 7 4 6 4 5 5 5 1 3
## 37 37 5 5 4 3 4 3 1 1 3
## 38 38 4 5 4 3 4 5 5 1 3
## 39 39 2 2 2 2 3 2 6 6 3
## 40 40 3 6 3 3 7 5 4 7 4
## 41 41 3 7 3 1 4 6 3 7 4
## 42 42 7 7 7 3 7 7 7 1 6
## 43 43 3 5 3 2 3 3 5 7 2
## 44 44 6 2 3 2 4 3 5 1 5
## 45 45 4 5 4 5 5 5 5 1 3
## 46 46 2 7 5 6 5 6 5 7 5
## 47 47 3 7 5 6 1 7 6 7 5
## 48 48 4 6 6 4 6 7 4 5 5
## 49 49 6 7 5 4 2 6 6 7 6
## 51 51 2 2 2 6 6 5 5 7 4
## 52 52 2 4 4 4 4 6 4 2 4
## 53 53 3 6 2 2 2 4 5 7 3
## 54 54 2 4 3 5 2 5 7 2 4
## 55 55 5 4 5 3 3 4 5 2 4
## 56 56 1 3 3 3 1 7 5 7 7
## 57 57 3 6 4 4 6 6 7 7 6
## 58 58 3 7 3 1 4 6 3 7 4
## 59 59 5 4 4 4 4 4 3 5 3
## 60 60 7 5 6 3 6 6 4 1 5
## 61 61 3 5 5 4 3 5 6 7 5
## 62 62 2 7 3 2 6 7 7 7 7
## 64 64 2 2 2 6 6 5 5 7 4
## 66 66 2 1 2 2 2 4 5 7 2
## 67 67 3 4 6 4 6 6 7 7 6
## 68 68 7 4 6 4 5 6 4 1 5
## 69 69 5 3 6 5 7 6 2 7 6
## 70 70 3 4 3 4 2 4 5 2 4
## 71 71 6 2 6 4 6 6 4 5 5
## 72 72 5 3 4 4 5 5 3 1 4
## 73 73 6 3 5 5 3 6 3 6 4
## 74 74 6 4 5 1 5 6 6 7 3
## 75 75 4 2 4 2 5 3 5 1 5
## 76 76 5 3 4 3 4 3 6 1 5
## 77 77 5 3 4 5 2 1 4 7 4
## 78 78 5 5 5 4 5 4 6 5 4
## 79 79 6 4 5 5 5 4 6 1 5
## 80 80 7 5 4 5 3 5 6 1 5
## 81 81 5 4 4 4 4 4 3 1 3
## 82 82 7 6 6 3 2 6 1 1 2
## 83 83 5 2 5 2 3 5 2 1 4
## 84 84 7 5 4 4 3 4 4 1 5
## 85 85 6 2 6 4 5 6 2 7 6
## 86 86 6 4 5 6 6 5 5 1 6
## 87 87 6 6 6 4 6 6 3 1 4
## 89 89 7 6 2 4 3 4 3 1 4
## 91 91 5 4 4 3 3 5 4 1 6
## 92 92 5 4 4 5 3 4 3 1 4
## 94 94 7 6 7 1 7 7 2 7 7
## 95 95 7 4 2 6 1 2 3 1 5
## 96 96 7 7 7 4 5 7 7 1 6
## 97 97 5 2 5 2 3 2 2 1 3
## 98 98 5 4 4 5 3 4 4 1 4
## 99 99 6 4 4 6 7 5 6 1 5
## 100 100 5 2 6 3 5 4 6 7 3
## 101 101 2 2 5 4 3 5 3 1 4
## 102 102 6 4 6 3 5 4 3 1 3
## 105 105 4 3 4 3 3 5 3 1 3
## 106 106 4 7 4 4 4 4 1 1 3
## 107 107 6 5 4 4 5 4 5 1 4
## 108 108 5 5 4 5 4 4 5 7 3
## 109 109 5 1 4 3 6 4 5 1 4
## 110 110 5 4 4 4 5 5 4 1 4
## 112 112 6 5 4 4 4 3 5 1 4
## 113 113 5 3 3 4 3 4 3 1 3
## 114 114 4 1 4 3 1 2 3 1 3
## 115 115 5 3 5 6 7 5 7 1 6
## 116 116 4 4 4 4 4 4 4 1 4
## 117 117 7 5 4 4 4 4 4 3 5
## 118 118 4 4 4 3 2 2 3 1 2
## 119 119 5 3 4 4 3 3 3 1 3
## 120 120 5 3 5 4 4 3 5 1 5
## 121 121 3 3 3 4 4 5 3 5 4
## 122 122 5 5 5 5 4 5 3 5 4
## 123 123 6 7 5 5 5 5 5 5 5
## 124 124 4 3 4 3 3 3 4 1 3
## 125 125 5 4 4 6 4 5 5 1 4
## 126 126 3 4 4 4 4 4 4 1 4
## 127 127 5 4 4 1 4 4 1 1 1
## Fruity Respondent Flavor Segment
## 1 4 1 Milk Seg_1 Classic
## 2 7 3 Milk Seg_1 Classic
## 3 2 4 Milk Seg_1 Classic
## 4 4 7 Milk Seg_1 Classic
## 5 5 11 Milk Seg_1 Classic
## 6 3 12 Milk Seg_1 Classic
## 7 5 16 Milk Seg_1 Classic
## 8 3 18 Milk Seg_1 Classic
## 9 3 2 Espresso Seg_3 Coffee
## 10 4 4 Espresso Seg_3 Coffee
## 11 5 7 Espresso Seg_3 Coffee
## 12 5 8 Espresso Seg_3 Coffee
## 14 2 10 Espresso Seg_3 Coffee
## 15 4 11 Espresso Seg_3 Coffee
## 16 4 12 Espresso Seg_3 Coffee
## 17 5 13 Espresso Seg_3 Coffee
## 18 4 14 Espresso Seg_3 Coffee
## 19 1 15 Espresso Seg_3 Coffee
## 20 3 16 Espresso Seg_3 Coffee
## 21 3 1 Biscuit Seg_1 Classic
## 22 4 3 Biscuit Seg_1 Classic
## 23 4 4 Biscuit Seg_1 Classic
## 24 4 5 Biscuit Seg_1 Classic
## 25 1 6 Biscuit Seg_1 Classic
## 26 5 7 Biscuit Seg_1 Classic
## 27 4 8 Biscuit Seg_1 Classic
## 28 4 9 Biscuit Seg_1 Classic
## 29 4 10 Biscuit Seg_1 Classic
## 31 5 12 Biscuit Seg_1 Classic
## 32 3 13 Biscuit Seg_1 Classic
## 33 2 14 Biscuit Seg_1 Classic
## 34 4 15 Biscuit Seg_1 Classic
## 35 4 16 Biscuit Seg_1 Classic
## 36 5 17 Biscuit Seg_1 Classic
## 37 3 18 Biscuit Seg_1 Classic
## 38 6 2 Orange Seg_2 Fruit
## 39 2 8 Orange Seg_2 Fruit
## 40 4 10 Orange Seg_2 Fruit
## 41 5 11 Orange Seg_2 Fruit
## 42 6 13 Orange Seg_2 Fruit
## 43 4 1 Strawberry Seg_2 Fruit
## 44 5 2 Strawberry Seg_2 Fruit
## 45 6 3 Strawberry Seg_2 Fruit
## 46 6 4 Strawberry Seg_2 Fruit
## 47 6 5 Strawberry Seg_2 Fruit
## 48 5 6 Strawberry Seg_2 Fruit
## 49 5 7 Strawberry Seg_2 Fruit
## 51 6 9 Strawberry Seg_2 Fruit
## 52 4 10 Strawberry Seg_2 Fruit
## 53 6 11 Strawberry Seg_2 Fruit
## 54 4 12 Strawberry Seg_2 Fruit
## 55 4 13 Strawberry Seg_2 Fruit
## 56 7 14 Strawberry Seg_2 Fruit
## 57 7 15 Strawberry Seg_2 Fruit
## 58 5 16 Strawberry Seg_2 Fruit
## 59 3 17 Strawberry Seg_2 Fruit
## 60 5 18 Strawberry Seg_2 Fruit
## 61 5 4 Mango Seg_2 Fruit
## 62 7 7 Mango Seg_2 Fruit
## 64 6 10 Mango Seg_2 Fruit
## 66 5 13 Mango Seg_2 Fruit
## 67 7 14 Mango Seg_2 Fruit
## 68 5 16 Mango Seg_2 Fruit
## 69 3 2 Cappuccino Seg_3 Coffee
## 70 3 6 Cappuccino Seg_3 Coffee
## 71 5 7 Cappuccino Seg_3 Coffee
## 72 4 8 Cappuccino Seg_3 Coffee
## 73 4 9 Cappuccino Seg_3 Coffee
## 74 3 12 Cappuccino Seg_3 Coffee
## 75 4 13 Cappuccino Seg_3 Coffee
## 76 4 14 Cappuccino Seg_3 Coffee
## 77 1 15 Cappuccino Seg_3 Coffee
## 78 3 16 Cappuccino Seg_3 Coffee
## 79 6 17 Cappuccino Seg_3 Coffee
## 80 3 18 Cappuccino Seg_3 Coffee
## 81 2 1 Mousse Seg_1 Classic
## 82 3 2 Mousse Seg_1 Classic
## 83 5 3 Mousse Seg_1 Classic
## 84 4 4 Mousse Seg_1 Classic
## 85 3 6 Mousse Seg_1 Classic
## 86 3 8 Mousse Seg_1 Classic
## 87 2 10 Mousse Seg_1 Classic
## 89 4 12 Mousse Seg_1 Classic
## 91 5 14 Mousse Seg_1 Classic
## 92 3 15 Mousse Seg_1 Classic
## 94 7 17 Mousse Seg_1 Classic
## 95 2 1 Caramel Seg_1 Classic
## 96 6 2 Caramel Seg_1 Classic
## 97 4 3 Caramel Seg_1 Classic
## 98 3 4 Caramel Seg_1 Classic
## 99 6 5 Caramel Seg_1 Classic
## 100 3 6 Caramel Seg_1 Classic
## 101 5 8 Caramel Seg_1 Classic
## 102 7 9 Caramel Seg_1 Classic
## 105 3 17 Caramel Seg_1 Classic
## 106 3 18 Caramel Seg_1 Classic
## 107 4 4 Nougat Seg_1 Classic
## 108 3 5 Nougat Seg_1 Classic
## 109 4 7 Nougat Seg_1 Classic
## 110 4 9 Nougat Seg_1 Classic
## 112 1 13 Nougat Seg_1 Classic
## 113 4 14 Nougat Seg_1 Classic
## 114 3 15 Nougat Seg_1 Classic
## 115 6 16 Nougat Seg_1 Classic
## 116 4 17 Nougat Seg_1 Classic
## 117 5 18 Nougat Seg_1 Classic
## 118 2 1 Nut Seg_1 Classic
## 119 3 2 Nut Seg_1 Classic
## 120 4 3 Nut Seg_1 Classic
## 121 3 4 Nut Seg_1 Classic
## 122 4 6 Nut Seg_1 Classic
## 123 5 8 Nut Seg_1 Classic
## 124 4 9 Nut Seg_1 Classic
## 125 4 13 Nut Seg_1 Classic
## 126 4 17 Nut Seg_1 Classic
## 127 4 18 Nut Seg_1 Classic
mydatc4_case.2 %>%
group_by(Segment, Flavor) %>%
tally()
## # A tibble: 11 x 3
## # Groups: Segment [3]
## Segment Flavor n
## <fct> <fct> <int>
## 1 Seg_1 Classic Milk 8
## 2 Seg_1 Classic Biscuit 16
## 3 Seg_1 Classic Mousse 11
## 4 Seg_1 Classic Caramel 10
## 5 Seg_1 Classic Nougat 10
## 6 Seg_1 Classic Nut 10
## 7 Seg_2 Fruit Orange 5
## 8 Seg_2 Fruit Strawberry 17
## 9 Seg_2 Fruit Mango 6
## 10 Seg_3 Coffee Espresso 11
## 11 Seg_3 Coffee Cappuccino 12
mydatc4_case.2 %>%
group_by(Segment) %>%
tally()
## # A tibble: 3 x 2
## Segment n
## <fct> <int>
## 1 Seg_1 Classic 65
## 2 Seg_2 Fruit 28
## 3 Seg_3 Coffee 23
# Group statistics
mydatc4_case.2 %>%
group_by(Segment) %>%
summarise_at(., .vars = vars(2:11), .funs = c(mean, sd)) %>%
cbind(Meaning = paste0("in the following fn1 = mean, fn2 = sd"),.) %>%
t()
## [,1]
## Meaning "in the following fn1 = mean, fn2 = sd"
## Segment "Seg_1 Classic"
## Price_fn1 "5.061538"
## Refreshing_fn1 "3.907692"
## Delicious_fn1 "4.523077"
## Healthy_fn1 "3.784615"
## Bitter_fn1 "3.969231"
## Light_fn1 "4.246154"
## Crunchy_fn1 "3.692308"
## Exotic_fn1 "2.200000"
## Sweet_fn1 "3.953846"
## Fruity_fn1 "3.846154"
## Price_fn2 "1.321421"
## Refreshing_fn2 "1.443986"
## Delicious_fn2 "1.091268"
## Healthy_fn2 "1.231010"
## Bitter_fn2 "1.489224"
## Light_fn2 "1.237825"
## Crunchy_fn2 "1.379799"
## Exotic_fn2 "2.173707"
## Sweet_fn2 "1.304209"
## Fruity_fn2 "1.289753"
## [,2]
## Meaning "in the following fn1 = mean, fn2 = sd"
## Segment "Seg_2 Fruit"
## Price_fn1 "3.535714"
## Refreshing_fn1 "4.785714"
## Delicious_fn1 "3.928571"
## Healthy_fn1 "3.500000"
## Bitter_fn1 "4.178571"
## Light_fn1 "5.285714"
## Crunchy_fn1 "5.107143"
## Exotic_fn1 "5.000000"
## Sweet_fn1 "4.428571"
## Fruity_fn1 "5.214286"
## Price_fn2 "1.731669"
## Refreshing_fn2 "1.853068"
## Delicious_fn2 "1.463850"
## Healthy_fn2 "1.478237"
## Bitter_fn2 "1.785820"
## Light_fn2 "1.329359"
## Crunchy_fn2 "1.227442"
## Exotic_fn2 "2.638743"
## Sweet_fn2 "1.345185"
## Fruity_fn2 "1.227981"
## [,3]
## Meaning "in the following fn1 = mean, fn2 = sd"
## Segment "Seg_3 Coffee"
## Price_fn1 "5.173913"
## Refreshing_fn1 "3.782609"
## Delicious_fn1 "4.217391"
## Healthy_fn1 "3.913043"
## Bitter_fn1 "3.347826"
## Light_fn1 "4.173913"
## Crunchy_fn1 "4.000000"
## Exotic_fn1 "3.086957"
## Sweet_fn1 "4.173913"
## Fruity_fn1 "3.608696"
## Price_fn2 "1.402989"
## Refreshing_fn2 "1.594209"
## Delicious_fn2 "1.042572"
## Healthy_fn2 "1.311247"
## Bitter_fn2 "1.773766"
## Light_fn2 "1.435022"
## Crunchy_fn2 "1.705606"
## Exotic_fn2 "2.372437"
## Sweet_fn2 "1.072473"
## Fruity_fn2 "1.233588"
# lda(Attributes, Group)
lda(mydatc4_case.2[,2:11], mydatc4_case.2[,14])
## Call:
## lda(mydatc4_case.2[, 2:11], mydatc4_case.2[, 14])
##
## Prior probabilities of groups:
## Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
## 0.5603448 0.2413793 0.1982759
##
## Group means:
## Price Refreshing Delicious Healthy Bitter Light Crunchy
## Seg_1 Classic 5.061538 3.907692 4.523077 3.784615 3.969231 4.246154 3.692308
## Seg_2 Fruit 3.535714 4.785714 3.928571 3.500000 4.178571 5.285714 5.107143
## Seg_3 Coffee 5.173913 3.782609 4.217391 3.913043 3.347826 4.173913 4.000000
## Exotic Sweet Fruity
## Seg_1 Classic 2.200000 3.953846 3.846154
## Seg_2 Fruit 5.000000 4.428571 5.214286
## Seg_3 Coffee 3.086957 4.173913 3.608696
##
## Coefficients of linear discriminants:
## LD1 LD2
## Price -0.25708626 0.4142271
## Refreshing 0.20810366 -0.2121803
## Delicious -0.32772535 -0.2862550
## Healthy -0.15785482 0.0708641
## Bitter -0.06348995 -0.4903264
## Light 0.29777066 0.1381961
## Crunchy 0.31538550 0.2353022
## Exotic 0.14700067 0.2570126
## Sweet -0.16433475 0.2644106
## Fruity 0.36487237 -0.2657973
##
## Proportion of trace:
## LD1 LD2
## 0.898 0.102
# Run all single ANOVAs at once and print output all at once
formulae <- lapply(colnames(mydatc4_case.2)[2:11], function(x) as.formula(paste0(x, " ~ Segment")))
Segment_aovres <- lapply(formulae, function(x) summary(aov(x, data = mydatc4_case.2)))
names(Segment_aovres) <- format(formulae)
print(Segment_aovres)
## $`Price ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 51.59 25.795 12.35 1.41e-05 ***
## Residuals 113 236.02 2.089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Refreshing ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 17.88 8.942 3.582 0.031 *
## Residuals 113 282.07 2.496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Delicious ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 7.21 3.606 2.579 0.0803 .
## Residuals 113 157.99 1.398
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Healthy ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 2.43 1.215 0.709 0.495
## Residuals 113 193.81 1.715
##
## $`Bitter ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 9.5 4.748 1.805 0.169
## Residuals 113 297.3 2.631
##
## $`Light ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 23.89 11.943 7.063 0.00129 **
## Residuals 113 191.08 1.691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Crunchy ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 39.43 19.716 9.835 0.000115 ***
## Residuals 113 226.52 2.005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Exotic ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 153.5 76.73 14.12 3.37e-06 ***
## Residuals 113 614.2 5.44
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Sweet ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 4.52 2.26 1.395 0.252
## Residuals 113 183.02 1.62
##
## $`Fruity ~ Segment`
## Df Sum Sq Mean Sq F value Pr(>F)
## Segment 2 44.41 22.203 13.89 4.05e-06 ***
## Residuals 113 180.65 1.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Wilks' Lambda
klaR::greedy.wilks(Segment ~
Price +
Refreshing +
Delicious +
Healthy +
Bitter +
Light +
Crunchy +
Exotic +
Sweet +
Fruity,
data = mydatc4_case.2,
niveau = 0.1)
## Formula containing included variables:
##
## Segment ~ Exotic + Fruity + Price + Refreshing + Crunchy + Bitter
## <environment: 0x000000002229db88>
##
##
## Values calculated in each step of the selection procedure:
##
## vars Wilks.lambda F.statistics.overall p.value.overall
## 1 Exotic 0.8000969 14.116450 3.369420e-06
## 2 Fruity 0.6751653 12.152687 5.869362e-09
## 3 Price 0.5992997 10.794695 1.565787e-10
## 4 Refreshing 0.5510574 9.545398 2.527499e-11
## 5 Crunchy 0.5161339 8.544172 9.860912e-12
## 6 Bitter 0.4873151 7.785025 5.444350e-12
## F.statistics.diff p.value.diff
## 1 14.116450 3.369420e-06
## 2 10.362160 7.379799e-05
## 3 7.025763 1.335104e-03
## 4 4.814970 9.877945e-03
## 5 3.687668 2.817547e-02
## 6 3.193450 4.489672e-02
# lda(Attributes, Group)
lda_res <- lda(mydatc4_case.2[,2:11], mydatc4_case.2[,14])
print(lda_res)
## Call:
## lda(mydatc4_case.2[, 2:11], mydatc4_case.2[, 14])
##
## Prior probabilities of groups:
## Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
## 0.5603448 0.2413793 0.1982759
##
## Group means:
## Price Refreshing Delicious Healthy Bitter Light Crunchy
## Seg_1 Classic 5.061538 3.907692 4.523077 3.784615 3.969231 4.246154 3.692308
## Seg_2 Fruit 3.535714 4.785714 3.928571 3.500000 4.178571 5.285714 5.107143
## Seg_3 Coffee 5.173913 3.782609 4.217391 3.913043 3.347826 4.173913 4.000000
## Exotic Sweet Fruity
## Seg_1 Classic 2.200000 3.953846 3.846154
## Seg_2 Fruit 5.000000 4.428571 5.214286
## Seg_3 Coffee 3.086957 4.173913 3.608696
##
## Coefficients of linear discriminants:
## LD1 LD2
## Price -0.25708626 0.4142271
## Refreshing 0.20810366 -0.2121803
## Delicious -0.32772535 -0.2862550
## Healthy -0.15785482 0.0708641
## Bitter -0.06348995 -0.4903264
## Light 0.29777066 0.1381961
## Crunchy 0.31538550 0.2353022
## Exotic 0.14700067 0.2570126
## Sweet -0.16433475 0.2644106
## Fruity 0.36487237 -0.2657973
##
## Proportion of trace:
## LD1 LD2
## 0.898 0.102
print(lda_res$counts)
## Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
## 65 28 23
print(lda_res$prior)
## Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
## 0.5603448 0.2413793 0.1982759
print(lda_res$scaling)
## LD1 LD2
## Price -0.25708626 0.4142271
## Refreshing 0.20810366 -0.2121803
## Delicious -0.32772535 -0.2862550
## Healthy -0.15785482 0.0708641
## Bitter -0.06348995 -0.4903264
## Light 0.29777066 0.1381961
## Crunchy 0.31538550 0.2353022
## Exotic 0.14700067 0.2570126
## Sweet -0.16433475 0.2644106
## Fruity 0.36487237 -0.2657973
t(as.data.frame(lda_res$scaling))
## Price Refreshing Delicious Healthy Bitter Light Crunchy
## LD1 -0.2570863 0.2081037 -0.3277254 -0.1578548 -0.06348995 0.2977707 0.3153855
## LD2 0.4142271 -0.2121803 -0.2862550 0.0708641 -0.49032642 0.1381961 0.2353022
## Exotic Sweet Fruity
## LD1 0.1470007 -0.1643348 0.3648724
## LD2 0.2570126 0.2644106 -0.2657973
# Predictions based on lda coefficients (LD1, LD2)
lda_pred <- predict(lda_res, data = mydatc4_case.2)
print(lda_pred)
## $class
## [1] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [6] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [11] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [16] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [21] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [26] Seg_1 Classic Seg_1 Classic Seg_2 Fruit Seg_1 Classic Seg_1 Classic
## [31] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [36] Seg_2 Fruit Seg_2 Fruit Seg_2 Fruit Seg_2 Fruit Seg_1 Classic
## [41] Seg_2 Fruit Seg_1 Classic Seg_2 Fruit Seg_2 Fruit Seg_2 Fruit
## [46] Seg_1 Classic Seg_2 Fruit Seg_2 Fruit Seg_1 Classic Seg_2 Fruit
## [51] Seg_2 Fruit Seg_1 Classic Seg_2 Fruit Seg_2 Fruit Seg_2 Fruit
## [56] Seg_1 Classic Seg_1 Classic Seg_2 Fruit Seg_2 Fruit Seg_2 Fruit
## [61] Seg_2 Fruit Seg_2 Fruit Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [66] Seg_1 Classic Seg_1 Classic Seg_3 Coffee Seg_2 Fruit Seg_1 Classic
## [71] Seg_1 Classic Seg_3 Coffee Seg_1 Classic Seg_1 Classic Seg_3 Coffee
## [76] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [81] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [86] Seg_1 Classic Seg_3 Coffee Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [91] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [96] Seg_1 Classic Seg_1 Classic Seg_3 Coffee Seg_1 Classic Seg_1 Classic
## [101] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [106] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [111] Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic Seg_1 Classic
## [116] Seg_1 Classic
## Levels: Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
##
## $posterior
## Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
## 1 0.8619031871 5.731822e-03 1.323650e-01
## 2 0.7356805754 1.084532e-01 1.558663e-01
## 3 0.6716406044 9.149125e-02 2.368681e-01
## 4 0.6993253841 7.613796e-02 2.245367e-01
## 5 0.5434439183 2.345054e-02 4.331055e-01
## 6 0.5876488507 8.820301e-03 4.035308e-01
## 7 0.6679950992 7.932773e-02 2.526772e-01
## 8 0.8708858845 8.337538e-03 1.207766e-01
## 9 0.5213401134 2.721862e-02 4.514413e-01
## 10 0.5015929618 3.050909e-01 1.933161e-01
## 11 0.5643269470 5.678271e-04 4.351052e-01
## 12 0.6827325002 4.049182e-03 3.132183e-01
## 14 0.6083348263 8.057977e-03 3.836072e-01
## 15 0.6874824181 5.823899e-02 2.542786e-01
## 16 0.6866002293 6.775731e-02 2.456425e-01
## 17 0.5315308751 2.085111e-01 2.599580e-01
## 18 0.7102883050 6.956922e-03 2.827548e-01
## 19 0.4954358045 2.202567e-02 4.825385e-01
## 20 0.5413937204 4.209430e-02 4.165120e-01
## 21 0.8793556228 5.004498e-02 7.059939e-02
## 22 0.8330405212 1.753879e-03 1.652056e-01
## 23 0.3518039713 3.462209e-01 3.019751e-01
## 24 0.8181098634 3.140428e-03 1.787497e-01
## 25 0.8990379873 4.196981e-05 1.009200e-01
## 26 0.6809468294 2.627036e-02 2.927828e-01
## 27 0.6065472253 4.485715e-02 3.485956e-01
## 28 0.7896236403 4.995200e-02 1.604244e-01
## 29 0.3150774881 6.516033e-01 3.331921e-02
## 31 0.8654335209 5.648818e-02 7.807830e-02
## 32 0.5856912198 2.727930e-02 3.870295e-01
## 33 0.8393386334 2.680496e-03 1.579809e-01
## 34 0.5908987049 1.126287e-01 2.964726e-01
## 35 0.8812450903 4.663908e-02 7.211583e-02
## 36 0.8614450344 1.772864e-02 1.208263e-01
## 37 0.9390348372 1.589159e-03 5.937600e-02
## 38 0.2114426573 7.669779e-01 2.157944e-02
## 39 0.2459249437 4.320873e-01 3.219878e-01
## 40 0.1172492285 8.666143e-01 1.613645e-02
## 41 0.0091808047 9.879364e-01 2.882802e-03
## 42 0.6104875623 3.326742e-01 5.683828e-02
## 43 0.0511583255 9.204243e-01 2.841737e-02
## 44 0.6287760456 3.319211e-02 3.380318e-01
## 45 0.4091918552 5.618126e-01 2.899549e-02
## 46 0.0277178389 9.676975e-01 4.584653e-03
## 47 0.0039368874 9.879226e-01 8.140549e-03
## 48 0.4768991564 4.710189e-01 5.208191e-02
## 49 0.0579591488 7.031500e-01 2.388909e-01
## 51 0.0409226434 9.428847e-01 1.619266e-02
## 52 0.4922173198 4.417943e-01 6.598838e-02
## 53 0.0015493854 9.968799e-01 1.570708e-03
## 54 0.0708749588 8.700195e-01 5.910552e-02
## 55 0.6938721962 5.024116e-02 2.558866e-01
## 56 0.0010318512 9.956557e-01 3.312450e-03
## 57 0.0025855978 9.964588e-01 9.555948e-04
## 58 0.0091808047 9.879364e-01 2.882802e-03
## 59 0.6903456642 2.617236e-02 2.834820e-01
## 60 0.8925842597 1.551862e-02 9.189712e-02
## 61 0.0676645117 8.696657e-01 6.266977e-02
## 62 0.0001352823 9.998087e-01 5.598071e-05
## 64 0.0409226434 9.428847e-01 1.619266e-02
## 66 0.0139448406 9.643441e-01 2.171101e-02
## 67 0.0330932679 9.577493e-01 9.157391e-03
## 68 0.8297864235 8.092170e-03 1.621214e-01
## 69 0.8041313114 1.892155e-03 1.939765e-01
## 70 0.4718429333 2.076544e-01 3.205026e-01
## 71 0.7347936904 1.901299e-02 2.461933e-01
## 72 0.8688226222 1.116021e-02 1.200172e-01
## 73 0.3950959236 2.238092e-02 5.825232e-01
## 74 0.2186731582 5.217622e-01 2.595647e-01
## 75 0.8464596507 1.950799e-02 1.340324e-01
## 76 0.6966939554 2.919873e-02 2.741073e-01
## 77 0.2643975143 5.306802e-04 7.350718e-01
## 78 0.6252678608 9.880649e-02 2.759256e-01
## 79 0.7900957175 5.429764e-02 1.556066e-01
## 80 0.3834982228 1.622909e-02 6.002727e-01
## 81 0.8439147432 2.844964e-03 1.532403e-01
## 82 0.8413144116 3.023254e-03 1.556623e-01
## 83 0.8434958159 1.025528e-02 1.462489e-01
## 84 0.6055667704 8.140254e-03 3.862930e-01
## 85 0.5212982322 1.062554e-03 4.776392e-01
## 86 0.7948593630 1.686224e-03 2.034544e-01
## 87 0.9318857354 1.639275e-03 6.647499e-02
## 89 0.5888638922 4.233700e-02 3.687991e-01
## 91 0.6359673682 7.690504e-02 2.871276e-01
## 92 0.7646875551 3.689422e-03 2.316230e-01
## 94 0.7332564500 1.719701e-01 9.477348e-02
## 95 0.2584229399 1.831778e-04 7.413939e-01
## 96 0.5400923947 3.328235e-01 1.270841e-01
## 97 0.9065095316 7.469259e-04 9.274354e-02
## 98 0.7134681541 7.644175e-03 2.788877e-01
## 99 0.7834675019 1.109883e-01 1.055442e-01
## 100 0.5716635338 4.469458e-02 3.836419e-01
## 101 0.8654335209 5.648818e-02 7.807830e-02
## 102 0.9436192009 2.401603e-02 3.236477e-02
## 105 0.7952168835 2.534873e-02 1.794344e-01
## 106 0.9511874644 9.992614e-03 3.881992e-02
## 107 0.8052395747 3.561740e-02 1.591430e-01
## 108 0.4069254944 1.857220e-01 4.073525e-01
## 109 0.8487303562 1.161411e-02 1.396555e-01
## 110 0.8351432796 3.764838e-02 1.272083e-01
## 112 0.6886431287 1.576859e-03 3.097800e-01
## 113 0.7440102928 2.421165e-02 2.317781e-01
## 114 0.6858377645 1.558619e-03 3.126036e-01
## 115 0.8076434199 8.450121e-02 1.078554e-01
## 116 0.8391330541 3.948584e-02 1.213811e-01
## 117 0.6190733368 3.300148e-02 3.479252e-01
## 118 0.8462556327 3.685711e-03 1.500587e-01
## 119 0.8085155601 2.394805e-03 1.890896e-01
## 120 0.8063425650 4.579725e-03 1.890777e-01
## 121 0.5777471050 1.389227e-01 2.833302e-01
## 122 0.7200726556 4.382686e-02 2.361005e-01
## 123 0.5207658839 2.888029e-01 1.904312e-01
## 124 0.8258085020 3.043294e-02 1.437586e-01
## 125 0.7177553721 4.425997e-02 2.379847e-01
## 126 0.8441764665 6.795622e-02 8.786731e-02
## 127 0.9387819550 1.968223e-02 4.153581e-02
##
## $x
## LD1 LD2
## 1 -1.117537022 -0.62239399
## 2 0.167274201 -0.47318249
## 3 0.088161737 0.11509356
## 4 0.001565154 0.02358754
## 5 -0.472162586 1.12850589
## 6 -0.906522071 1.03275352
## 7 0.023720876 0.20488343
## 8 -0.953938586 -0.76473951
## 9 -0.398819020 1.20995141
## 10 0.714550279 0.11095757
## 11 -2.062576151 1.35598474
## 12 -1.264043900 0.63710027
## 14 -0.951879169 0.94411742
## 15 -0.117470663 0.20307194
## 16 -0.049773692 0.15425171
## 17 0.507289650 0.41432341
## 18 -1.038672241 0.43943832
## 19 -0.477483711 1.35575011
## 20 -0.219391050 1.04659052
## 21 -0.149349453 -1.51560371
## 22 -1.628013904 -0.24882330
## 23 0.846853804 0.99262158
## 24 -1.382065528 -0.18043969
## 25 -3.191781394 -0.63070826
## 26 -0.464400192 0.43075416
## 27 -0.214506257 0.71663824
## 28 -0.187627510 -0.46141272
## 29 1.349137767 -1.45422564
## 31 -0.101684706 -1.39189198
## 32 -0.423014895 0.90888498
## 33 -1.446707872 -0.33798160
## 34 0.199014368 0.49449459
## 35 -0.181850231 -1.48862938
## 36 -0.630508210 -0.80583113
## 37 -1.618264616 -1.54049977
## 38 1.590522081 -1.53451785
## 39 1.054766491 1.43450753
## 40 1.865506541 -1.24287084
## 41 2.927047221 -0.48870355
## 42 0.795132772 -1.50571311
## 43 2.117731174 0.28983990
## 44 -0.351464445 0.66409451
## 45 1.211322495 -1.88311606
## 46 2.507227356 -1.14222893
## 47 3.117257059 1.60680217
## 48 1.032956956 -1.36523793
## 49 1.771240171 2.60944064
## 51 2.252959263 -0.11549532
## 52 0.974042477 -1.12401583
## 53 3.580230790 0.72525296
## 54 1.919278747 0.78146656
## 55 -0.183752725 0.21078276
## 56 3.648847483 2.01470861
## 57 3.453291091 -0.39240349
## 58 2.927047221 -0.48870355
## 59 -0.467678744 0.37939566
## 60 -0.674324020 -1.14748695
## 61 1.929365779 0.89818692
## 62 4.695351914 -0.47167747
## 64 2.252959263 -0.11549532
## 66 2.596261144 1.37331438
## 67 2.381633069 -0.54055276
## 68 -0.976792545 -0.37411608
## 69 -1.598404857 -0.03270215
## 70 0.526633270 0.78182638
## 71 -0.611400863 0.17374155
## 72 -0.828966753 -0.78999152
## 73 -0.411879265 1.81275673
## 74 1.193283309 1.30077314
## 75 -0.593379594 -0.67514377
## 76 -0.421341567 0.32339570
## 77 -1.884661959 2.77383723
## 78 0.131031356 0.36095106
## 79 -0.149733707 -0.50280930
## 80 -0.540857502 1.90173343
## 81 -1.420553799 -0.38285746
## 82 -1.395157547 -0.36590523
## 83 -0.872619357 -0.52624106
## 84 -0.946671977 0.95627146
## 85 -1.778760058 1.50349404
## 86 -1.647645170 0.04242489
## 87 -1.612656776 -1.40535104
## 89 -0.234168361 0.81688833
## 91 0.015559992 0.40594285
## 92 -1.314381046 0.17694628
## 94 0.408352902 -1.07108305
## 95 -2.328726228 2.88324305
## 96 0.764257855 -0.45419618
## 97 -1.966468970 -0.93944266
## 98 -0.998995547 0.41224848
## 99 0.190927592 -0.98814691
## 100 -0.204814060 0.88998769
## 101 -0.101684706 -1.39189198
## 102 -0.414364052 -2.43131754
## 105 -0.487583392 -0.29304311
## 106 -0.805055191 -2.17002735
## 107 -0.336845367 -0.46771684
## 108 0.507342437 1.22270904
## 109 -0.817808868 -0.59441307
## 110 -0.305477597 -0.76686967
## 112 -1.665743190 0.68180547
## 113 -0.507697413 0.07430962
## 114 -1.670122798 0.69738204
## 115 0.063235592 -0.97673590
## 116 -0.282672048 -0.82896648
## 117 -0.351288215 0.71417294
## 118 -1.309839396 -0.42809864
## 119 -1.498065800 -0.08434416
## 120 -1.222307237 -0.12729738
## 121 0.299551391 0.45198347
## 122 -0.246846973 0.08863377
## 123 0.680092747 0.05739138
## 124 -0.402866851 -0.59993050
## 125 -0.242311782 0.10048715
## 126 -0.025585785 -1.24319359
## 127 -0.519346104 -2.12646995
# Classification matrix
print("rows = Actual_Group; colums = Predicted_Group"); caret::confusionMatrix(table(mydatc4_case.2$Segment, lda_pred$class));
## [1] "rows = Actual_Group; colums = Predicted_Group"
## Confusion Matrix and Statistics
##
##
## Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
## Seg_1 Classic 62 1 2
## Seg_2 Fruit 8 20 0
## Seg_3 Coffee 19 1 3
##
## Overall Statistics
##
## Accuracy : 0.7328
## 95% CI : (0.6426, 0.8107)
## No Information Rate : 0.7672
## P-Value [Acc > NIR] : 0.8388353
##
## Kappa : 0.4818
##
## Mcnemar's Test P-Value : 0.0001538
##
## Statistics by Class:
##
## Class: Seg_1 Classic Class: Seg_2 Fruit
## Sensitivity 0.6966 0.9091
## Specificity 0.8889 0.9149
## Pos Pred Value 0.9538 0.7143
## Neg Pred Value 0.4706 0.9773
## Prevalence 0.7672 0.1897
## Detection Rate 0.5345 0.1724
## Detection Prevalence 0.5603 0.2414
## Balanced Accuracy 0.7928 0.9120
## Class: Seg_3 Coffee
## Sensitivity 0.60000
## Specificity 0.81982
## Pos Pred Value 0.13043
## Neg Pred Value 0.97849
## Prevalence 0.04310
## Detection Rate 0.02586
## Detection Prevalence 0.19828
## Balanced Accuracy 0.70991
prop.table(table(mydatc4_case.2$Segment, lda_pred$class), margin = 1);
##
## Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
## Seg_1 Classic 0.95384615 0.01538462 0.03076923
## Seg_2 Fruit 0.28571429 0.71428571 0.00000000
## Seg_3 Coffee 0.82608696 0.04347826 0.13043478
table(mydatc4_case.2$Segment, lda_pred$class)
##
## Seg_1 Classic Seg_2 Fruit Seg_3 Coffee
## Seg_1 Classic 62 1 2
## Seg_2 Fruit 8 20 0
## Seg_3 Coffee 19 1 3
plot(lda_res, col = as.integer(mydatc4_case.2$Segment),
cex.main = 1,
cex.lab = 1,
cex.axis = 1)
plot(lda_res, dimen = 1, type = "b",
cex.main = 1,
cex.lab = 1,
cex.axis = 1)
klaR::partimat(Segment ~
Price +
Refreshing +
Delicious +
Healthy +
Bitter +
Light +
Crunchy +
Exotic +
Sweet +
Fruity,
data = mydatc4_case.2,
method = "lda",
nplots.hor = 3,
nplots.ver = 3,
cex.main = 1,
cex.lab = 1,
cex.axis = 1)