Determinants of bus users’ loyalty
Phương Anh
15/03/2021
1. Read Data
# Read Data directly
t = "F:/NGHIEN CUU SINH/NCS - PHUONG ANH/Part 2-Satisfaction and Loyalty/So lieu/So lieu - PA xu ly/873_Loyaltyofbuspassenger_PAcode_NonBus_Mising data_outliers_forMLM_Analyse.csv"
DataLOY = read.csv(t, header = T)
head(DataLOY)## ID AGE CITY FRE TripPurpose Departure TimeUseonBus TravelTime PSSW PSSS PSAB
## 1 3 1 2 1 5 0 4 3.00 4.9 5.4 5.5
## 2 4 1 2 2 7 0 4 2.00 3.4 2.4 3.5
## 3 5 1 2 1 5 1 4 0.17 3.9 3.4 4.5
## 4 6 1 2 1 5 1 1 4.00 4.1 4.4 5.5
## 5 7 1 2 1 5 1 4 2.00 3.7 2.6 3.5
## 6 8 1 2 1 5 1 6 2.00 4.6 2.7 3.8
## PSEB PSQ SAT LOY IMA PHB PEV ATM PPI SIM PPA SBE EXB EC_Stop WC_Stop EC_Bus
## 1 5.8 4.8 6.0 5.7 5.6 6.6 4.0 4.3 6.0 3.5 4.5 4.0 4.8 2 1 2
## 2 6.0 4.6 4.7 4.3 4.8 4.0 5.3 4.1 4.0 4.0 4.0 4.8 4.2 2 2 1
## 3 4.5 2.7 2.0 3.7 3.0 5.0 5.8 3.1 2.7 3.5 4.5 6.0 4.0 2 2 2
## 4 6.0 4.8 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 2 1 2
## 5 4.5 4.5 4.0 4.6 4.6 5.6 6.0 3.3 3.0 5.5 5.0 5.0 4.2 2 1 2
## 6 4.0 3.9 4.7 4.4 3.0 5.4 5.3 2.7 3.0 2.0 2.5 2.0 5.0 2 1 2
## WC_Bus Gender MarriedStatus Occupation Education Income
## 1 1 2 1 1 2 1
## 2 2 2 1 1 2 1
## 3 2 1 1 1 2 1
## 4 1 1 1 1 2 1
## 5 1 1 1 1 2 1
## 6 1 2 1 1 3 1names(DataLOY)## [1] "ID" "AGE" "CITY" "FRE"
## [5] "TripPurpose" "Departure" "TimeUseonBus" "TravelTime"
## [9] "PSSW" "PSSS" "PSAB" "PSEB"
## [13] "PSQ" "SAT" "LOY" "IMA"
## [17] "PHB" "PEV" "ATM" "PPI"
## [21] "SIM" "PPA" "SBE" "EXB"
## [25] "EC_Stop" "WC_Stop" "EC_Bus" "WC_Bus"
## [29] "Gender" "MarriedStatus" "Occupation" "Education"
## [33] "Income"dim(DataLOY)## [1] 873 332. Desscriptive statistic
# 2.1 Recoding
# Regroup value of variables
DataLOY$PSSW[DataLOY$PSSW <2.5] <- 1
DataLOY$PSSW[DataLOY$PSSW >= 2.5 & DataLOY$PSSW <5.5] <- 2
DataLOY$PSSW[DataLOY$PSSW >= 5.5] <- 3
DataLOY$PSSS[DataLOY$PSSS <2.5] <- 1
DataLOY$PSSS[DataLOY$PSSS >= 2.5 & DataLOY$PSSS <5.5] <- 2
DataLOY$PSSS[DataLOY$PSSS >= 5.5] <- 3
DataLOY$PSAB[DataLOY$PSAB <2.5] <- 1
DataLOY$PSAB[DataLOY$PSAB >= 2.5 & DataLOY$PSAB <5.5] <- 2
DataLOY$PSAB[DataLOY$PSAB >= 5.5] <- 3
DataLOY$PSEB[DataLOY$PSEB <2.5] <- 1
DataLOY$PSEB[DataLOY$PSEB >= 2.5 & DataLOY$PSEB <5.5] <- 2
DataLOY$PSEB[DataLOY$PSEB >= 5.5] <- 3
DataLOY$PSQ[DataLOY$PSQ <2.5] <- 1
DataLOY$PSQ[DataLOY$PSQ >= 2.5 & DataLOY$PSQ <5.5] <- 2
DataLOY$PSQ[DataLOY$PSQ >= 5.5] <- 3
DataLOY$SAT[DataLOY$SAT <2.5] <- 1
DataLOY$SAT[DataLOY$SAT >= 2.5 & DataLOY$SAT <5.5] <- 2
DataLOY$SAT[DataLOY$SAT >= 5.5] <- 3
DataLOY$LOY[DataLOY$LOY <2.5] <- 1
DataLOY$LOY[DataLOY$LOY >= 2.5 & DataLOY$LOY <5.5] <- 2
DataLOY$LOY[DataLOY$LOY >= 5.5] <- 3
DataLOY$IMA[DataLOY$IMA <2.5] <- 1
DataLOY$IMA[DataLOY$IMA >= 2.5 & DataLOY$IMA <5.5] <- 2
DataLOY$IMA[DataLOY$IMA >= 5.5] <- 3
DataLOY$PHB[DataLOY$PHB <2.5] <- 1
DataLOY$PHB[DataLOY$PHB >= 2.5 & DataLOY$PHB <5.5] <- 2
DataLOY$PHB[DataLOY$PHB >= 5.5] <- 3
DataLOY$PEV[DataLOY$PEV <2.5] <- 1
DataLOY$PEV[DataLOY$PEV >= 2.5 & DataLOY$PEV <5.5] <- 2
DataLOY$PEV[DataLOY$PEV >= 5.5] <- 3
DataLOY$ATM[DataLOY$ATM <2.5] <- 1
DataLOY$ATM[DataLOY$ATM >= 2.5 & DataLOY$ATM <5.5] <- 2
DataLOY$ATM[DataLOY$ATM >= 5.5] <- 3
DataLOY$PPI[DataLOY$PPI <2.5] <- 1
DataLOY$PPI[DataLOY$PPI >= 2.5 & DataLOY$PPI <5.5] <- 2
DataLOY$PPI[DataLOY$PPI >= 5.5] <- 3
DataLOY$SIM[DataLOY$SIM <2.5] <- 1
DataLOY$SIM[DataLOY$SIM >= 2.5 & DataLOY$SIM <5.5] <- 2
DataLOY$SIM[DataLOY$SIM >= 5.5] <- 3
DataLOY$PPA[DataLOY$PPA <2.5] <- 1
DataLOY$PPA[DataLOY$PPA >= 2.5 & DataLOY$PPA <5.5] <- 2
DataLOY$PPA[DataLOY$PPA >= 5.5] <- 3
DataLOY$SBE[DataLOY$SBE <2.5] <- 1
DataLOY$SBE[DataLOY$SBE >= 2.5 & DataLOY$SBE <5.5] <- 2
DataLOY$SBE[DataLOY$SBE >= 5.5] <- 3
DataLOY$EXB[DataLOY$EXB <2.5] <- 1
DataLOY$EXB[DataLOY$EXB >= 2.5 & DataLOY$EXB <5.5] <- 2
DataLOY$EXB[DataLOY$EXB >= 5.5] <- 3# 2.2. Subset Data DaNang and HoChiMinh
LOY_DN <- subset(DataLOY, CITY == 1)
dim(LOY_DN)## [1] 422 33LOY_HCM <- subset(DataLOY, CITY == 2)
dim(LOY_HCM)## [1] 451 33# Data coding
## DataLOY : all DN and HCM
head(DataLOY)## ID AGE CITY FRE TripPurpose Departure TimeUseonBus TravelTime PSSW PSSS PSAB
## 1 3 1 2 1 5 0 4 3.00 2 2 3
## 2 4 1 2 2 7 0 4 2.00 2 1 2
## 3 5 1 2 1 5 1 4 0.17 2 2 2
## 4 6 1 2 1 5 1 1 4.00 2 2 3
## 5 7 1 2 1 5 1 4 2.00 2 2 2
## 6 8 1 2 1 5 1 6 2.00 2 2 2
## PSEB PSQ SAT LOY IMA PHB PEV ATM PPI SIM PPA SBE EXB EC_Stop WC_Stop EC_Bus
## 1 3 2 3 3 3 3 2 2 3 2 2 2 2 2 1 2
## 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
## 3 2 2 1 2 2 2 3 2 2 2 2 3 2 2 2 2
## 4 3 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2
## 5 2 2 2 2 2 3 3 2 2 3 2 2 2 2 1 2
## 6 2 2 2 2 2 2 2 2 2 1 2 1 2 2 1 2
## WC_Bus Gender MarriedStatus Occupation Education Income
## 1 1 2 1 1 2 1
## 2 2 2 1 1 2 1
## 3 2 1 1 1 2 1
## 4 1 1 1 1 2 1
## 5 1 1 1 1 2 1
## 6 1 2 1 1 3 1str(DataLOY)## 'data.frame': 873 obs. of 33 variables:
## $ ID : int 3 4 5 6 7 8 10 11 12 13 ...
## $ AGE : int 1 1 1 1 1 1 4 1 1 2 ...
## $ CITY : int 2 2 2 2 2 2 2 2 2 2 ...
## $ FRE : int 1 2 1 1 1 1 1 1 1 1 ...
## $ TripPurpose : int 5 7 5 5 5 5 4 5 5 5 ...
## $ Departure : int 0 0 1 1 1 1 1 1 0 1 ...
## $ TimeUseonBus : int 4 4 4 1 4 6 4 4 3 3 ...
## $ TravelTime : num 3 2 0.17 4 2 2 2 2.5 1.5 2 ...
## $ PSSW : num 2 2 2 2 2 2 2 2 2 2 ...
## $ PSSS : num 2 1 2 2 2 2 2 2 2 2 ...
## $ PSAB : num 3 2 2 3 2 2 3 2 2 3 ...
## $ PSEB : num 3 3 2 3 2 2 2 3 3 2 ...
## $ PSQ : num 2 2 2 2 2 2 2 2 3 2 ...
## $ SAT : num 3 2 1 2 2 2 3 2 3 3 ...
## $ LOY : num 3 2 2 2 2 2 3 2 3 3 ...
## $ IMA : num 3 2 2 2 2 2 3 3 3 3 ...
## $ PHB : num 3 2 2 2 3 2 3 3 3 3 ...
## $ PEV : num 2 2 3 2 3 2 3 3 3 2 ...
## $ ATM : num 2 2 2 2 2 2 2 2 2 2 ...
## $ PPI : num 3 2 2 2 2 2 3 1 2 2 ...
## $ SIM : num 2 2 2 2 3 1 3 2 2 3 ...
## $ PPA : num 2 2 2 2 2 2 2 2 2 2 ...
## $ SBE : num 2 2 3 2 2 1 2 2 2 2 ...
## $ EXB : num 2 2 2 2 2 2 2 2 2 2 ...
## $ EC_Stop : int 2 2 2 2 2 2 2 2 2 2 ...
## $ WC_Stop : int 1 2 2 1 1 1 2 1 2 2 ...
## $ EC_Bus : int 2 1 2 2 2 2 2 2 2 2 ...
## $ WC_Bus : int 1 2 2 1 1 1 2 1 2 2 ...
## $ Gender : int 2 2 1 1 1 2 2 2 2 1 ...
## $ MarriedStatus: int 1 1 1 1 1 1 2 1 1 1 ...
## $ Occupation : int 1 1 1 1 1 1 2 1 1 7 ...
## $ Education : int 2 2 2 2 2 3 1 2 3 5 ...
## $ Income : int 1 1 1 1 1 1 1 1 1 1 ...attach(DataLOY)
DataLOY = within(DataLOY, {
AGE = factor(AGE, labels = c("16-25", "26-35", "36-45", "46-55", ">55"))
CITY = factor(CITY,labels = c("DaNang", "HoChiMinh"))
FRE = factor(FRE, labels = c(">=3 days/week", "2days/month-2days/week", "2days/year-1day/month", "<2 days/year"))
TripPurpose = factor(TripPurpose, labels = c("Working", "Studying", "Shopping", "Entertaining", "Others"))
Departure = factor(Departure, labels = c("Normal", "Peak-Hour"))
TimeUseonBus = factor(TimeUseonBus, labels = c("Using.telephone", "Reading", "Listening", "Nothing", "Talking", "Others"))
PSSW = factor(PSSW, labels = c("Bad", "Normal", "Good"))
PSSS = factor(PSSS, labels = c("Bad", "Normal", "Good"))
PSAB = factor(PSAB, labels = c("Bad", "Normal", "Good"))
PSEB = factor(PSEB, labels = c("Bad", "Normal", "Good"))
PSQ = factor(PSQ, labels = c("Bad", "Normal", "Good"))
SAT = factor(SAT, labels = c("Not satisfied", "Normal", "Satisfied"))
LOY = factor(LOY, labels = c("Notloyal", "Normal", "Loyal"))
IMA = factor(IMA, labels = c("Bad", "Normal", "Good"))
PHB = factor(PHB, labels = c("Bad", "Normal", "Good"))
PEV = factor(PEV, labels = c("Bad", "Normal", "Good"))
ATM = factor(ATM, labels = c("Bad", "Normal", "Good"))
PPI = factor(PPI, labels = c("Bad", "Normal", "Good"))
SIM = factor(SIM, labels = c("Bad", "Normal", "Good"))
PPA = factor(PPA, labels = c("Bad", "Normal", "Good"))
SBE = factor(SBE, labels = c("Bad", "Normal", "Good"))
EXB = factor(EXB, labels = c("Bad", "Normal", "Good"))
EC_Stop = factor(EC_Stop, labels = c("Ever", "Never"))
WC_Stop = factor(WC_Stop, labels = c("Ever", "Never"))
EC_Bus = factor(EC_Bus, labels = c("Ever", "Never"))
WC_Bus = factor(WC_Bus, labels = c("Ever", "Never"))
Gender = factor(Gender, labels = c("Male", "Female"))
MarriedStatus = factor(MarriedStatus, labels = c("Single", "Married"))
Occupation = factor(Occupation, labels = c("Students/Pupils", "Full.time.job", "Part.time.job", "Retirement", "No.job", "Housewife", "Others"))
Education = factor(Education, labels = c("Secondary.school", "Undergraduate", "High.school", "Postgraduate", "Others"))
Income = factor(Income, labels = c("<5millions", "5-10millions", "10-15millions", ">15millions"))
} )
str(DataLOY)## 'data.frame': 873 obs. of 33 variables:
## $ ID : int 3 4 5 6 7 8 10 11 12 13 ...
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 3 1 1 2 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ FRE : Factor w/ 4 levels ">=3 days/week",..: 1 2 1 1 1 1 1 1 1 1 ...
## $ TripPurpose : Factor w/ 5 levels "Working","Studying",..: 2 4 2 2 2 2 1 2 2 2 ...
## $ Departure : Factor w/ 2 levels "Normal","Peak-Hour": 1 1 2 2 2 2 2 2 1 2 ...
## $ TimeUseonBus : Factor w/ 6 levels "Using.telephone",..: 4 4 4 1 4 6 4 4 3 3 ...
## $ TravelTime : num 3 2 0.17 4 2 2 2 2.5 1.5 2 ...
## $ PSSW : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ PSSS : Factor w/ 3 levels "Bad","Normal",..: 2 1 2 2 2 2 2 2 2 2 ...
## $ PSAB : Factor w/ 3 levels "Bad","Normal",..: 3 2 2 3 2 2 3 2 2 3 ...
## $ PSEB : Factor w/ 3 levels "Bad","Normal",..: 3 3 2 3 2 2 2 3 3 2 ...
## $ PSQ : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 3 2 ...
## $ SAT : Factor w/ 3 levels "Not satisfied",..: 3 2 1 2 2 2 3 2 3 3 ...
## $ LOY : Factor w/ 3 levels "Notloyal","Normal",..: 3 2 2 2 2 2 3 2 3 3 ...
## $ IMA : Factor w/ 3 levels "Bad","Normal",..: 3 2 2 2 2 2 3 3 3 3 ...
## $ PHB : Factor w/ 3 levels "Bad","Normal",..: 3 2 2 2 3 2 3 3 3 3 ...
## $ PEV : Factor w/ 3 levels "Bad","Normal",..: 2 2 3 2 3 2 3 3 3 2 ...
## $ ATM : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ PPI : Factor w/ 3 levels "Bad","Normal",..: 3 2 2 2 2 2 3 1 2 2 ...
## $ SIM : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 3 1 3 2 2 3 ...
## $ PPA : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ SBE : Factor w/ 3 levels "Bad","Normal",..: 2 2 3 2 2 1 2 2 2 2 ...
## $ EXB : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EC_Stop : Factor w/ 2 levels "Ever","Never": 2 2 2 2 2 2 2 2 2 2 ...
## $ WC_Stop : Factor w/ 2 levels "Ever","Never": 1 2 2 1 1 1 2 1 2 2 ...
## $ EC_Bus : Factor w/ 2 levels "Ever","Never": 2 1 2 2 2 2 2 2 2 2 ...
## $ WC_Bus : Factor w/ 2 levels "Ever","Never": 1 2 2 1 1 1 2 1 2 2 ...
## $ Gender : Factor w/ 2 levels "Male","Female": 2 2 1 1 1 2 2 2 2 1 ...
## $ MarriedStatus: Factor w/ 2 levels "Single","Married": 1 1 1 1 1 1 2 1 1 1 ...
## $ Occupation : Factor w/ 7 levels "Students/Pupils",..: 1 1 1 1 1 1 2 1 1 7 ...
## $ Education : Factor w/ 5 levels "Secondary.school",..: 2 2 2 2 2 3 1 2 3 5 ...
## $ Income : Factor w/ 4 levels "<5millions","5-10millions",..: 1 1 1 1 1 1 1 1 1 1 ...dim(DataLOY)## [1] 873 33# 2.3. Descritive Table
library(tableone)
require(tableone)
library(magrittr)
summary(DataLOY)## ID AGE CITY FRE
## Min. : 3.0 16-25:425 DaNang :422 >=3 days/week :508
## 1st Qu.:273.0 26-35:172 HoChiMinh:451 2days/month-2days/week:168
## Median :526.0 36-45:105 2days/year-1day/month : 99
## Mean :521.7 46-55: 77 <2 days/year : 98
## 3rd Qu.:769.0 >55 : 94
## Max. :993.0
##
## TripPurpose Departure TimeUseonBus TravelTime
## Working :305 Normal :296 Using.telephone:198 Min. : 0.000
## Studying :303 Peak-Hour:577 Reading : 53 1st Qu.: 0.500
## Shopping : 60 Listening :138 Median : 1.000
## Entertaining:100 Nothing :428 Mean : 1.291
## Others :105 Talking : 34 3rd Qu.: 2.000
## Others : 22 Max. :20.000
##
## PSSW PSSS PSAB PSEB PSQ
## Bad : 29 Bad : 47 Bad : 39 Bad : 8 Bad : 10
## Normal:639 Normal:645 Normal:392 Normal:189 Normal:466
## Good :205 Good :181 Good :442 Good :676 Good :397
##
##
##
##
## SAT LOY IMA PHB PEV
## Not satisfied: 16 Notloyal: 12 Bad : 11 Bad : 16 Bad : 29
## Normal :347 Normal :342 Normal:390 Normal:279 Normal:233
## Satisfied :510 Loyal :519 Good :472 Good :578 Good :611
##
##
##
##
## ATM PPI SIM PPA SBE EXB
## Bad : 35 Bad :121 Bad : 27 Bad : 14 Bad : 21 Bad : 3
## Normal:504 Normal:548 Normal:489 Normal:519 Normal:426 Normal:298
## Good :334 Good :204 Good :357 Good :340 Good :426 Good :572
##
##
##
##
## EC_Stop WC_Stop EC_Bus WC_Bus Gender MarriedStatus
## Ever : 36 Ever :121 Ever : 42 Ever :116 Male :364 Single :535
## Never:837 Never:752 Never:831 Never:757 Female:509 Married:338
##
##
##
##
##
## Occupation Education Income
## Students/Pupils:368 Secondary.school: 57 <5millions :465
## Full.time.job :305 Undergraduate :287 5-10millions :249
## Part.time.job : 69 High.school :368 10-15millions:122
## Retirement : 46 Postgraduate :106 >15millions : 37
## No.job : 3 Others : 55
## Housewife : 54
## Others : 28library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-Tab1_LOY <- table1(~ PSSW + PSSS + PSAB + PSEB + PSQ + SAT + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + EC_Stop + WC_Stop + EC_Bus + WC_Bus + Gender + MarriedStatus + Occupation + Education + Income + AGE + CITY + FRE + TripPurpose + Departure + TimeUseonBus + TravelTime| LOY , data = DataLOY)
Tab1_LOY| Notloyal (N=12) |
Normal (N=342) |
Loyal (N=519) |
Overall (N=873) |
|
|---|---|---|---|---|
| PSSW | ||||
| Bad | 3 (25.0%) | 19 (5.6%) | 7 (1.3%) | 29 (3.3%) |
| Normal | 8 (66.7%) | 286 (83.6%) | 345 (66.5%) | 639 (73.2%) |
| Good | 1 (8.3%) | 37 (10.8%) | 167 (32.2%) | 205 (23.5%) |
| PSSS | ||||
| Bad | 4 (33.3%) | 23 (6.7%) | 20 (3.9%) | 47 (5.4%) |
| Normal | 8 (66.7%) | 296 (86.5%) | 341 (65.7%) | 645 (73.9%) |
| Good | 0 (0%) | 23 (6.7%) | 158 (30.4%) | 181 (20.7%) |
| PSAB | ||||
| Bad | 4 (33.3%) | 24 (7.0%) | 11 (2.1%) | 39 (4.5%) |
| Normal | 6 (50.0%) | 215 (62.9%) | 171 (32.9%) | 392 (44.9%) |
| Good | 2 (16.7%) | 103 (30.1%) | 337 (64.9%) | 442 (50.6%) |
| PSEB | ||||
| Bad | 1 (8.3%) | 5 (1.5%) | 2 (0.4%) | 8 (0.9%) |
| Normal | 9 (75.0%) | 127 (37.1%) | 53 (10.2%) | 189 (21.6%) |
| Good | 2 (16.7%) | 210 (61.4%) | 464 (89.4%) | 676 (77.4%) |
| PSQ | ||||
| Bad | 4 (33.3%) | 6 (1.8%) | 0 (0%) | 10 (1.1%) |
| Normal | 8 (66.7%) | 281 (82.2%) | 177 (34.1%) | 466 (53.4%) |
| Good | 0 (0%) | 55 (16.1%) | 342 (65.9%) | 397 (45.5%) |
| SAT | ||||
| Not satisfied | 6 (50.0%) | 10 (2.9%) | 0 (0%) | 16 (1.8%) |
| Normal | 6 (50.0%) | 254 (74.3%) | 87 (16.8%) | 347 (39.7%) |
| Satisfied | 0 (0%) | 78 (22.8%) | 432 (83.2%) | 510 (58.4%) |
| IMA | ||||
| Bad | 2 (16.7%) | 8 (2.3%) | 1 (0.2%) | 11 (1.3%) |
| Normal | 8 (66.7%) | 248 (72.5%) | 134 (25.8%) | 390 (44.7%) |
| Good | 2 (16.7%) | 86 (25.1%) | 384 (74.0%) | 472 (54.1%) |
| PHB | ||||
| Bad | 2 (16.7%) | 11 (3.2%) | 3 (0.6%) | 16 (1.8%) |
| Normal | 7 (58.3%) | 196 (57.3%) | 76 (14.6%) | 279 (32.0%) |
| Good | 3 (25.0%) | 135 (39.5%) | 440 (84.8%) | 578 (66.2%) |
| PEV | ||||
| Bad | 1 (8.3%) | 21 (6.1%) | 7 (1.3%) | 29 (3.3%) |
| Normal | 4 (33.3%) | 157 (45.9%) | 72 (13.9%) | 233 (26.7%) |
| Good | 7 (58.3%) | 164 (48.0%) | 440 (84.8%) | 611 (70.0%) |
| ATM | ||||
| Bad | 4 (33.3%) | 21 (6.1%) | 10 (1.9%) | 35 (4.0%) |
| Normal | 7 (58.3%) | 282 (82.5%) | 215 (41.4%) | 504 (57.7%) |
| Good | 1 (8.3%) | 39 (11.4%) | 294 (56.6%) | 334 (38.3%) |
| PPI | ||||
| Bad | 4 (33.3%) | 57 (16.7%) | 60 (11.6%) | 121 (13.9%) |
| Normal | 6 (50.0%) | 246 (71.9%) | 296 (57.0%) | 548 (62.8%) |
| Good | 2 (16.7%) | 39 (11.4%) | 163 (31.4%) | 204 (23.4%) |
| SIM | ||||
| Bad | 3 (25.0%) | 17 (5.0%) | 7 (1.3%) | 27 (3.1%) |
| Normal | 5 (41.7%) | 260 (76.0%) | 224 (43.2%) | 489 (56.0%) |
| Good | 4 (33.3%) | 65 (19.0%) | 288 (55.5%) | 357 (40.9%) |
| PPA | ||||
| Bad | 1 (8.3%) | 5 (1.5%) | 8 (1.5%) | 14 (1.6%) |
| Normal | 8 (66.7%) | 267 (78.1%) | 244 (47.0%) | 519 (59.5%) |
| Good | 3 (25.0%) | 70 (20.5%) | 267 (51.4%) | 340 (38.9%) |
| SBE | ||||
| Bad | 1 (8.3%) | 9 (2.6%) | 11 (2.1%) | 21 (2.4%) |
| Normal | 8 (66.7%) | 225 (65.8%) | 193 (37.2%) | 426 (48.8%) |
| Good | 3 (25.0%) | 108 (31.6%) | 315 (60.7%) | 426 (48.8%) |
| EXB | ||||
| Bad | 1 (8.3%) | 2 (0.6%) | 0 (0%) | 3 (0.3%) |
| Normal | 10 (83.3%) | 221 (64.6%) | 67 (12.9%) | 298 (34.1%) |
| Good | 1 (8.3%) | 119 (34.8%) | 452 (87.1%) | 572 (65.5%) |
| EC_Stop | ||||
| Ever | 1 (8.3%) | 21 (6.1%) | 14 (2.7%) | 36 (4.1%) |
| Never | 11 (91.7%) | 321 (93.9%) | 505 (97.3%) | 837 (95.9%) |
| WC_Stop | ||||
| Ever | 2 (16.7%) | 65 (19.0%) | 54 (10.4%) | 121 (13.9%) |
| Never | 10 (83.3%) | 277 (81.0%) | 465 (89.6%) | 752 (86.1%) |
| EC_Bus | ||||
| Ever | 2 (16.7%) | 20 (5.8%) | 20 (3.9%) | 42 (4.8%) |
| Never | 10 (83.3%) | 322 (94.2%) | 499 (96.1%) | 831 (95.2%) |
| WC_Bus | ||||
| Ever | 2 (16.7%) | 58 (17.0%) | 56 (10.8%) | 116 (13.3%) |
| Never | 10 (83.3%) | 284 (83.0%) | 463 (89.2%) | 757 (86.7%) |
| Gender | ||||
| Male | 6 (50.0%) | 152 (44.4%) | 206 (39.7%) | 364 (41.7%) |
| Female | 6 (50.0%) | 190 (55.6%) | 313 (60.3%) | 509 (58.3%) |
| MarriedStatus | ||||
| Single | 8 (66.7%) | 247 (72.2%) | 280 (53.9%) | 535 (61.3%) |
| Married | 4 (33.3%) | 95 (27.8%) | 239 (46.1%) | 338 (38.7%) |
| Occupation | ||||
| Students/Pupils | 7 (58.3%) | 178 (52.0%) | 183 (35.3%) | 368 (42.2%) |
| Full.time.job | 5 (41.7%) | 118 (34.5%) | 182 (35.1%) | 305 (34.9%) |
| Part.time.job | 0 (0%) | 19 (5.6%) | 50 (9.6%) | 69 (7.9%) |
| Retirement | 0 (0%) | 9 (2.6%) | 37 (7.1%) | 46 (5.3%) |
| No.job | 0 (0%) | 0 (0%) | 3 (0.6%) | 3 (0.3%) |
| Housewife | 0 (0%) | 12 (3.5%) | 42 (8.1%) | 54 (6.2%) |
| Others | 0 (0%) | 6 (1.8%) | 22 (4.2%) | 28 (3.2%) |
| Education | ||||
| Secondary.school | 0 (0%) | 14 (4.1%) | 43 (8.3%) | 57 (6.5%) |
| Undergraduate | 2 (16.7%) | 110 (32.2%) | 175 (33.7%) | 287 (32.9%) |
| High.school | 10 (83.3%) | 157 (45.9%) | 201 (38.7%) | 368 (42.2%) |
| Postgraduate | 0 (0%) | 43 (12.6%) | 63 (12.1%) | 106 (12.1%) |
| Others | 0 (0%) | 18 (5.3%) | 37 (7.1%) | 55 (6.3%) |
| Income | ||||
| <5millions | 5 (41.7%) | 187 (54.7%) | 273 (52.6%) | 465 (53.3%) |
| 5-10millions | 3 (25.0%) | 101 (29.5%) | 145 (27.9%) | 249 (28.5%) |
| 10-15millions | 3 (25.0%) | 39 (11.4%) | 80 (15.4%) | 122 (14.0%) |
| >15millions | 1 (8.3%) | 15 (4.4%) | 21 (4.0%) | 37 (4.2%) |
| AGE | ||||
| 16-25 | 8 (66.7%) | 214 (62.6%) | 203 (39.1%) | 425 (48.7%) |
| 26-35 | 2 (16.7%) | 68 (19.9%) | 102 (19.7%) | 172 (19.7%) |
| 36-45 | 1 (8.3%) | 27 (7.9%) | 77 (14.8%) | 105 (12.0%) |
| 46-55 | 1 (8.3%) | 21 (6.1%) | 55 (10.6%) | 77 (8.8%) |
| >55 | 0 (0%) | 12 (3.5%) | 82 (15.8%) | 94 (10.8%) |
| CITY | ||||
| DaNang | 2 (16.7%) | 136 (39.8%) | 284 (54.7%) | 422 (48.3%) |
| HoChiMinh | 10 (83.3%) | 206 (60.2%) | 235 (45.3%) | 451 (51.7%) |
| FRE | ||||
| >=3 days/week | 4 (33.3%) | 179 (52.3%) | 325 (62.6%) | 508 (58.2%) |
| 2days/month-2days/week | 2 (16.7%) | 62 (18.1%) | 104 (20.0%) | 168 (19.2%) |
| 2days/year-1day/month | 2 (16.7%) | 49 (14.3%) | 48 (9.2%) | 99 (11.3%) |
| <2 days/year | 4 (33.3%) | 52 (15.2%) | 42 (8.1%) | 98 (11.2%) |
| TripPurpose | ||||
| Working | 4 (33.3%) | 114 (33.3%) | 187 (36.0%) | 305 (34.9%) |
| Studying | 4 (33.3%) | 135 (39.5%) | 164 (31.6%) | 303 (34.7%) |
| Shopping | 0 (0%) | 13 (3.8%) | 47 (9.1%) | 60 (6.9%) |
| Entertaining | 1 (8.3%) | 45 (13.2%) | 54 (10.4%) | 100 (11.5%) |
| Others | 3 (25.0%) | 35 (10.2%) | 67 (12.9%) | 105 (12.0%) |
| Departure | ||||
| Normal | 5 (41.7%) | 105 (30.7%) | 186 (35.8%) | 296 (33.9%) |
| Peak-Hour | 7 (58.3%) | 237 (69.3%) | 333 (64.2%) | 577 (66.1%) |
| TimeUseonBus | ||||
| Using.telephone | 3 (25.0%) | 79 (23.1%) | 116 (22.4%) | 198 (22.7%) |
| Reading | 1 (8.3%) | 19 (5.6%) | 33 (6.4%) | 53 (6.1%) |
| Listening | 1 (8.3%) | 64 (18.7%) | 73 (14.1%) | 138 (15.8%) |
| Nothing | 5 (41.7%) | 156 (45.6%) | 267 (51.4%) | 428 (49.0%) |
| Talking | 1 (8.3%) | 11 (3.2%) | 22 (4.2%) | 34 (3.9%) |
| Others | 1 (8.3%) | 13 (3.8%) | 8 (1.5%) | 22 (2.5%) |
| TravelTime | ||||
| Mean (SD) | 1.67 (2.69) | 1.35 (1.65) | 1.24 (1.29) | 1.29 (1.46) |
| Median [Min, Max] | 1.00 [0, 10.0] | 1.00 [0, 20.0] | 1.00 [0.100, 20.0] | 1.00 [0, 20.0] |
library(compareGroups)
Des_LOY <- compareGroups(LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + SAT + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + EC_Stop + WC_Stop + EC_Bus + WC_Bus + Gender + MarriedStatus + Occupation + Education + Income + AGE + CITY + FRE + TripPurpose + Departure + TimeUseonBus + TravelTime, data = DataLOY)
createTable(Des_LOY)##
## --------Summary descriptives table by 'LOY'---------
##
## ________________________________________________________________________
## Notloyal Normal Loyal p.overall
## N=12 N=342 N=519
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## PSSW: <0.001
## Bad 3 (25.0%) 19 (5.56%) 7 (1.35%)
## Normal 8 (66.7%) 286 (83.6%) 345 (66.5%)
## Good 1 (8.33%) 37 (10.8%) 167 (32.2%)
## PSSS: <0.001
## Bad 4 (33.3%) 23 (6.73%) 20 (3.85%)
## Normal 8 (66.7%) 296 (86.5%) 341 (65.7%)
## Good 0 (0.00%) 23 (6.73%) 158 (30.4%)
## PSAB: <0.001
## Bad 4 (33.3%) 24 (7.02%) 11 (2.12%)
## Normal 6 (50.0%) 215 (62.9%) 171 (32.9%)
## Good 2 (16.7%) 103 (30.1%) 337 (64.9%)
## PSEB: <0.001
## Bad 1 (8.33%) 5 (1.46%) 2 (0.39%)
## Normal 9 (75.0%) 127 (37.1%) 53 (10.2%)
## Good 2 (16.7%) 210 (61.4%) 464 (89.4%)
## PSQ: <0.001
## Bad 4 (33.3%) 6 (1.75%) 0 (0.00%)
## Normal 8 (66.7%) 281 (82.2%) 177 (34.1%)
## Good 0 (0.00%) 55 (16.1%) 342 (65.9%)
## SAT: <0.001
## Not satisfied 6 (50.0%) 10 (2.92%) 0 (0.00%)
## Normal 6 (50.0%) 254 (74.3%) 87 (16.8%)
## Satisfied 0 (0.00%) 78 (22.8%) 432 (83.2%)
## IMA: <0.001
## Bad 2 (16.7%) 8 (2.34%) 1 (0.19%)
## Normal 8 (66.7%) 248 (72.5%) 134 (25.8%)
## Good 2 (16.7%) 86 (25.1%) 384 (74.0%)
## PHB: <0.001
## Bad 2 (16.7%) 11 (3.22%) 3 (0.58%)
## Normal 7 (58.3%) 196 (57.3%) 76 (14.6%)
## Good 3 (25.0%) 135 (39.5%) 440 (84.8%)
## PEV: <0.001
## Bad 1 (8.33%) 21 (6.14%) 7 (1.35%)
## Normal 4 (33.3%) 157 (45.9%) 72 (13.9%)
## Good 7 (58.3%) 164 (48.0%) 440 (84.8%)
## ATM: <0.001
## Bad 4 (33.3%) 21 (6.14%) 10 (1.93%)
## Normal 7 (58.3%) 282 (82.5%) 215 (41.4%)
## Good 1 (8.33%) 39 (11.4%) 294 (56.6%)
## PPI: <0.001
## Bad 4 (33.3%) 57 (16.7%) 60 (11.6%)
## Normal 6 (50.0%) 246 (71.9%) 296 (57.0%)
## Good 2 (16.7%) 39 (11.4%) 163 (31.4%)
## SIM: <0.001
## Bad 3 (25.0%) 17 (4.97%) 7 (1.35%)
## Normal 5 (41.7%) 260 (76.0%) 224 (43.2%)
## Good 4 (33.3%) 65 (19.0%) 288 (55.5%)
## PPA: <0.001
## Bad 1 (8.33%) 5 (1.46%) 8 (1.54%)
## Normal 8 (66.7%) 267 (78.1%) 244 (47.0%)
## Good 3 (25.0%) 70 (20.5%) 267 (51.4%)
## SBE: <0.001
## Bad 1 (8.33%) 9 (2.63%) 11 (2.12%)
## Normal 8 (66.7%) 225 (65.8%) 193 (37.2%)
## Good 3 (25.0%) 108 (31.6%) 315 (60.7%)
## EXB: <0.001
## Bad 1 (8.33%) 2 (0.58%) 0 (0.00%)
## Normal 10 (83.3%) 221 (64.6%) 67 (12.9%)
## Good 1 (8.33%) 119 (34.8%) 452 (87.1%)
## EC_Stop: 0.026
## Ever 1 (8.33%) 21 (6.14%) 14 (2.70%)
## Never 11 (91.7%) 321 (93.9%) 505 (97.3%)
## WC_Stop: 0.001
## Ever 2 (16.7%) 65 (19.0%) 54 (10.4%)
## Never 10 (83.3%) 277 (81.0%) 465 (89.6%)
## EC_Bus: 0.058
## Ever 2 (16.7%) 20 (5.85%) 20 (3.85%)
## Never 10 (83.3%) 322 (94.2%) 499 (96.1%)
## WC_Bus: 0.024
## Ever 2 (16.7%) 58 (17.0%) 56 (10.8%)
## Never 10 (83.3%) 284 (83.0%) 463 (89.2%)
## Gender: 0.323
## Male 6 (50.0%) 152 (44.4%) 206 (39.7%)
## Female 6 (50.0%) 190 (55.6%) 313 (60.3%)
## MarriedStatus: <0.001
## Single 8 (66.7%) 247 (72.2%) 280 (53.9%)
## Married 4 (33.3%) 95 (27.8%) 239 (46.1%)
## Occupation: .
## Students/Pupils 7 (58.3%) 178 (52.0%) 183 (35.3%)
## Full.time.job 5 (41.7%) 118 (34.5%) 182 (35.1%)
## Part.time.job 0 (0.00%) 19 (5.56%) 50 (9.63%)
## Retirement 0 (0.00%) 9 (2.63%) 37 (7.13%)
## No.job 0 (0.00%) 0 (0.00%) 3 (0.58%)
## Housewife 0 (0.00%) 12 (3.51%) 42 (8.09%)
## Others 0 (0.00%) 6 (1.75%) 22 (4.24%)
## Education: .
## Secondary.school 0 (0.00%) 14 (4.09%) 43 (8.29%)
## Undergraduate 2 (16.7%) 110 (32.2%) 175 (33.7%)
## High.school 10 (83.3%) 157 (45.9%) 201 (38.7%)
## Postgraduate 0 (0.00%) 43 (12.6%) 63 (12.1%)
## Others 0 (0.00%) 18 (5.26%) 37 (7.13%)
## Income: .
## <5millions 5 (41.7%) 187 (54.7%) 273 (52.6%)
## 5-10millions 3 (25.0%) 101 (29.5%) 145 (27.9%)
## 10-15millions 3 (25.0%) 39 (11.4%) 80 (15.4%)
## >15millions 1 (8.33%) 15 (4.39%) 21 (4.05%)
## AGE: .
## 16-25 8 (66.7%) 214 (62.6%) 203 (39.1%)
## 26-35 2 (16.7%) 68 (19.9%) 102 (19.7%)
## 36-45 1 (8.33%) 27 (7.89%) 77 (14.8%)
## 46-55 1 (8.33%) 21 (6.14%) 55 (10.6%)
## >55 0 (0.00%) 12 (3.51%) 82 (15.8%)
## CITY: <0.001
## DaNang 2 (16.7%) 136 (39.8%) 284 (54.7%)
## HoChiMinh 10 (83.3%) 206 (60.2%) 235 (45.3%)
## FRE: .
## >=3 days/week 4 (33.3%) 179 (52.3%) 325 (62.6%)
## 2days/month-2days/week 2 (16.7%) 62 (18.1%) 104 (20.0%)
## 2days/year-1day/month 2 (16.7%) 49 (14.3%) 48 (9.25%)
## <2 days/year 4 (33.3%) 52 (15.2%) 42 (8.09%)
## TripPurpose: .
## Working 4 (33.3%) 114 (33.3%) 187 (36.0%)
## Studying 4 (33.3%) 135 (39.5%) 164 (31.6%)
## Shopping 0 (0.00%) 13 (3.80%) 47 (9.06%)
## Entertaining 1 (8.33%) 45 (13.2%) 54 (10.4%)
## Others 3 (25.0%) 35 (10.2%) 67 (12.9%)
## Departure: 0.244
## Normal 5 (41.7%) 105 (30.7%) 186 (35.8%)
## Peak-Hour 7 (58.3%) 237 (69.3%) 333 (64.2%)
## TimeUseonBus: .
## Using.telephone 3 (25.0%) 79 (23.1%) 116 (22.4%)
## Reading 1 (8.33%) 19 (5.56%) 33 (6.36%)
## Listening 1 (8.33%) 64 (18.7%) 73 (14.1%)
## Nothing 5 (41.7%) 156 (45.6%) 267 (51.4%)
## Talking 1 (8.33%) 11 (3.22%) 22 (4.24%)
## Others 1 (8.33%) 13 (3.80%) 8 (1.54%)
## TravelTime 1.67 (2.69) 1.35 (1.65) 1.24 (1.29) 0.393
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯3. Describe Data by graph
library(magrittr)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()library(ggplot2)
library(car)## Warning: package 'car' was built under R version 4.0.4## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## some## LOY ~ PSSW
DataLOY %>%
group_by(LOY, PSSW) %>%
count() %>%
ggplot(aes(PSSW, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Security & Safety on the way to/from bus stops") +
ylab("Perceived loyalty") +
ggtitle("Perceived loyalty ~ Perceived Security & Safety on the way to/from bus stops")
## LOY ~ PSSS
DataLOY %>%
group_by(LOY, PSSS) %>%
count() %>%
ggplot(aes(PSSS, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Security & Safety at bus Stations") +
ylab("Perceived loyalty") +
ggtitle("Perceived Security & Safety at bus Stations ~ Perceived loyalty")
## LOY ~ PSAB
DataLOY %>%
group_by(LOY, PSAB) %>%
count() %>%
ggplot(aes(PSAB, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Safety on Buses") +
ylab("Perceived loyalty") +
ggtitle("Perceived Safety on Buses ~ Perceived loyalty")
## LOY ~ PSEB
DataLOY %>%
group_by(LOY, PSEB) %>%
count() %>%
ggplot(aes(PSEB, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Security on Buses") +
ylab("Perceived loyalty") +
ggtitle("Perceived Security on Buses ~ Perceived loyalty")
## LOY ~ PSQ
DataLOY %>%
group_by(LOY, PSQ) %>%
count() %>%
ggplot(aes(PSQ, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Service Quality") +
ylab("Perceived loyalty") +
ggtitle("Perceived Service Quality ~ Perceived loyalty")
## LOY ~ SAT
DataLOY %>%
group_by(LOY, SAT) %>%
count() %>%
ggplot(aes(SAT, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Satisfaction") +
ylab("Perceived loyalty") +
ggtitle("Perceived Satisfaction ~ Perceived loyalty")
## LOY ~ IMA
DataLOY %>%
group_by(LOY, IMA) %>%
count() %>%
ggplot(aes(IMA, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Image") +
ylab("Perceived loyalty") +
ggtitle("Perceived Image ~ Perceived loyalty")
## LOY ~ PHB
DataLOY %>%
group_by(LOY, PHB) %>%
count() %>%
ggplot(aes(PHB, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Health Benefits") +
ylab("Perceived loyalty") +
ggtitle("Perceived Health Benefits ~ Perceived loyalty")
## LOY ~ PEV
DataLOY %>%
group_by(LOY, PEV) %>%
count() %>%
ggplot(aes(PEV, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Environment Value/Benefits") +
ylab("Perceived loyalty") +
ggtitle("Perceived Environment Value/Benefits ~ Perceived loyalty")
## LOY ~ ATM
DataLOY %>%
group_by(LOY, ATM) %>%
count() %>%
ggplot(aes(ATM, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Atmospheric") +
ylab("Perceived loyalty") +
ggtitle("Perceived Atmospheric ~ Perceived loyalty")
## LOY ~ PPI
DataLOY %>%
group_by(LOY, PPI) %>%
count() %>%
ggplot(aes(PPI, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Passenger to Passenger Interaction") +
ylab("Perceived loyalty") +
ggtitle("Passenger to Passenger Interaction ~ Perceived loyalty")
## LOY ~ SIM
DataLOY %>%
group_by(LOY, SIM) %>%
count() %>%
ggplot(aes(SIM, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Similarity") +
ylab("Perceived loyalty") +
ggtitle("Similarity ~ Perceived loyalty")
## LOY ~ PPA
DataLOY %>%
group_by(LOY, PPA) %>%
count() %>%
ggplot(aes(PPA, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Physical Appearance") +
ylab("Perceived loyalty") +
ggtitle("Perceived Physical Appearance ~ Perceived loyalty")
## LOY ~ SBE
DataLOY %>%
group_by(LOY, SBE) %>%
count() %>%
ggplot(aes(SBE, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Perceived Suitable Behavior") +
ylab("Perceived loyalty") +
ggtitle("Perceived Suitable Behavior ~ Perceived loyalty")
## LOY ~ EXB
DataLOY %>%
group_by(LOY, EXB) %>%
count() %>%
ggplot(aes(EXB, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Experience on the Bus") +
ylab("Perceived loyalty") +
ggtitle("Experience on the Bus ~ Perceived loyalty")
## LOY ~ EC_Stop
DataLOY %>%
group_by(LOY, EC_Stop) %>%
count() %>%
ggplot(aes(EC_Stop, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Experienced crime at bus stops") +
ylab("Perceived loyalty") +
ggtitle("Experienced crime at bus stops ~ Perceived loyalty")
## LOY ~ WC_Stop
DataLOY %>%
group_by(LOY, WC_Stop) %>%
count() %>%
ggplot(aes(WC_Stop, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Witnessed crime at bus stops") +
ylab("Perceived loyalty") +
ggtitle("Witnessed crime at bus stops ~ Perceived loyalty")
## LOY ~ EC_Bus
DataLOY %>%
group_by(LOY, EC_Bus) %>%
count() %>%
ggplot(aes(EC_Bus, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Experienced crime on bus") +
ylab("Perceived loyalty") +
ggtitle("Experienced crime on bus ~ Perceived loyalty")
## LOY ~ WC_Bus
DataLOY %>%
group_by(LOY, WC_Bus) %>%
count() %>%
ggplot(aes(WC_Bus, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Witnessed crime on bus") +
ylab("Perceived loyalty") +
ggtitle("Witnessed crime on bus ~ Perceived loyalty")
## LOY ~ Gender - Gan nhu khong có khac biet giua 2 nhom gioi tinh
DataLOY %>%
group_by(LOY, Gender) %>%
count() %>%
ggplot(aes(Gender, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Gender") +
ylab("Perceived loyalty") +
ggtitle("Gender ~ Perceived loyalty")
## LOY ~ MarriedStatus
DataLOY %>%
group_by(LOY, MarriedStatus) %>%
count() %>%
ggplot(aes(MarriedStatus, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Married status") +
ylab("Perceived loyalty") +
ggtitle("Married status ~ Perceived loyalty")
## LOY ~ Occupation - Khac biet khong ro
DataLOY %>%
group_by(LOY, Occupation) %>%
count() %>%
ggplot(aes(Occupation, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Occupation") +
ylab("Perceived loyalty") +
ggtitle("Occupation ~ Perceived loyalty")
## LOY ~ Education - Khac biet khong ro
DataLOY %>%
group_by(LOY, Education) %>%
count() %>%
ggplot(aes(Education, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Level of completed education") +
ylab("Perceived loyalty") +
ggtitle("Level of completed education ~ Perceived loyalty")
## LOY ~ Income - Khac biet khong ro
DataLOY %>%
group_by(LOY, Income) %>%
count() %>%
ggplot(aes(Income, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Income") +
ylab("Perceived loyalty") +
ggtitle("Income ~ Perceived loyalty")
## LOY ~ AGE - Khac biet khong ro
DataLOY %>%
group_by(LOY, AGE) %>%
count() %>%
ggplot(aes(AGE, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Age") +
ylab("Perceived loyalty") +
ggtitle("Age ~ Perceived loyalty")
## LOY ~ CITY
DataLOY %>%
group_by(LOY, CITY) %>%
count() %>%
ggplot(aes(CITY, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("City") +
ylab("Perceived loyalty") +
ggtitle("City ~ Perceived loyalty")
## LOY ~ FRE
DataLOY %>%
group_by(LOY, FRE) %>%
count() %>%
ggplot(aes(FRE, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Frequency") +
ylab("Perceived loyalty") +
ggtitle("Frequency ~ Perceived loyalty")
## LOY ~ TripPurpose - Khac biet khong ro
DataLOY %>%
group_by(LOY, TripPurpose) %>%
count() %>%
ggplot(aes(TripPurpose, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Main purpose of trip") +
ylab("Perceived loyalty") +
ggtitle("Main purpose of trip ~ Perceived loyalty")
## LOY ~ Departure - Khac biet khong ro
DataLOY %>%
group_by(LOY, Departure) %>%
count() %>%
ggplot(aes(Departure, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Departure time") +
ylab("Perceived loyalty") +
ggtitle("Departure time ~ Perceived loyalty")
## LOY ~ TimeUseonBus - Khac biet khong ro
DataLOY %>%
group_by(LOY, TimeUseonBus) %>%
count() %>%
ggplot(aes(TimeUseonBus, n, fill = LOY)) +
geom_col(position = "fill") +
xlab("Time use on bus") +
ylab("Perceived loyalty") +
ggtitle("Time use on bus ~ Perceived loyalty")
## LOY ~ Travel time
ggplot(DataLOY, aes(x=TravelTime, fill = LOY, color = LOY)) +
geom_histogram (position = "dodge") +
xlab("Travel time") +
ylab("Count") +
ggtitle("Histogram of Travel time")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Correlation in continuous variables
library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:car':
##
## logit## The following objects are masked from 'package:ggplot2':
##
## %+%, alphaCor1 = data.frame(DataLOY$LOY, DataLOY$PSSW, DataLOY$PSSS, DataLOY$PSAB, DataLOY$PSEB, DataLOY$PSQ, DataLOY$SAT, DataLOY$IMA, DataLOY$PHB, DataLOY$PEV)
pairs.panels(Cor1)
Cor2 = data.frame(DataLOY$LOY, DataLOY$ATM, DataLOY$PPI, DataLOY$SIM, DataLOY$PPA, DataLOY$SBE, DataLOY$EXB)
pairs.panels(Cor2)
Cor3 = data.frame(DataLOY$LOY, DataLOY$EC_Stop, DataLOY$WC_Stop, DataLOY$EC_Bus, DataLOY$WC_Bus)
pairs.panels(Cor3)
Cor4 = data.frame(DataLOY$LOY, DataLOY$Gender, DataLOY$MarriedStatus, DataLOY$Occupation, DataLOY$Education, DataLOY$Income, DataLOY$AGE)
pairs.panels(Cor4)
Cor5 = data.frame(DataLOY$LOY, DataLOY$CITY, DataLOY$FRE, DataLOY$TripPurpose, DataLOY$Departure, DataLOY$TimeUseonBus, DataLOY$TravelTime)
pairs.panels(Cor5)
### Boxplot of TravelTime ~ LOY - khong co su khac biet
DataLOY %>%
group_by(TravelTime, LOY) %>%
count() %>%
ggplot(aes(x = LOY, y = TravelTime, fill = LOY)) +
geom_boxplot() +
xlab("LOY") +
ylab("Travel time") +
ggtitle("Boxplot of perceived loyalty ~ Travel time")
### Boxplot of Travel time ~ Gender
DataLOY %>%
group_by(TravelTime, Gender) %>%
count() %>%
ggplot(aes(x = Gender, y = TravelTime, fill = Gender)) +
geom_boxplot() +
xlab("Gender") +
ylab("Travel time") +
ggtitle("Boxplot of Travel time ~ Gender")
DataLOY %>%
group_by(TravelTime, CITY) %>%
count() %>%
ggplot(aes(x = CITY, y = TravelTime, fill = CITY)) +
geom_boxplot() +
xlab("CITY") +
ylab("Travel time") +
ggtitle("Boxplot of Travel time ~ City")
DataLOY %>%
group_by(TravelTime, AGE) %>%
count() %>%
ggplot(aes(x = AGE, y = TravelTime, fill = AGE)) +
geom_boxplot() +
xlab("Age") +
ylab("Travel time") +
ggtitle("Boxplot of Travel time ~ age")
DataLOY %>%
group_by(TravelTime, Departure) %>%
count() %>%
ggplot(aes(x = Departure, y = TravelTime, fill = Departure)) +
geom_boxplot() +
xlab("Departure") +
ylab("Travel time") +
ggtitle("Boxplot of Travel time ~ Departure")
DataLOY %>%
group_by(TravelTime, Occupation) %>%
count() %>%
ggplot(aes(x = Occupation, y = TravelTime, fill = Occupation)) +
geom_boxplot() +
xlab("Occupation") +
ylab("Travel time") +
ggtitle("Boxplot of Travel time ~ Occupation")
4. Descriptive statistical analysis
summary(DataLOY)## ID AGE CITY FRE
## Min. : 3.0 16-25:425 DaNang :422 >=3 days/week :508
## 1st Qu.:273.0 26-35:172 HoChiMinh:451 2days/month-2days/week:168
## Median :526.0 36-45:105 2days/year-1day/month : 99
## Mean :521.7 46-55: 77 <2 days/year : 98
## 3rd Qu.:769.0 >55 : 94
## Max. :993.0
##
## TripPurpose Departure TimeUseonBus TravelTime
## Working :305 Normal :296 Using.telephone:198 Min. : 0.000
## Studying :303 Peak-Hour:577 Reading : 53 1st Qu.: 0.500
## Shopping : 60 Listening :138 Median : 1.000
## Entertaining:100 Nothing :428 Mean : 1.291
## Others :105 Talking : 34 3rd Qu.: 2.000
## Others : 22 Max. :20.000
##
## PSSW PSSS PSAB PSEB PSQ
## Bad : 29 Bad : 47 Bad : 39 Bad : 8 Bad : 10
## Normal:639 Normal:645 Normal:392 Normal:189 Normal:466
## Good :205 Good :181 Good :442 Good :676 Good :397
##
##
##
##
## SAT LOY IMA PHB PEV
## Not satisfied: 16 Notloyal: 12 Bad : 11 Bad : 16 Bad : 29
## Normal :347 Normal :342 Normal:390 Normal:279 Normal:233
## Satisfied :510 Loyal :519 Good :472 Good :578 Good :611
##
##
##
##
## ATM PPI SIM PPA SBE EXB
## Bad : 35 Bad :121 Bad : 27 Bad : 14 Bad : 21 Bad : 3
## Normal:504 Normal:548 Normal:489 Normal:519 Normal:426 Normal:298
## Good :334 Good :204 Good :357 Good :340 Good :426 Good :572
##
##
##
##
## EC_Stop WC_Stop EC_Bus WC_Bus Gender MarriedStatus
## Ever : 36 Ever :121 Ever : 42 Ever :116 Male :364 Single :535
## Never:837 Never:752 Never:831 Never:757 Female:509 Married:338
##
##
##
##
##
## Occupation Education Income
## Students/Pupils:368 Secondary.school: 57 <5millions :465
## Full.time.job :305 Undergraduate :287 5-10millions :249
## Part.time.job : 69 High.school :368 10-15millions:122
## Retirement : 46 Postgraduate :106 >15millions : 37
## No.job : 3 Others : 55
## Housewife : 54
## Others : 28table(LOY) ## LOY
## 1 2 3
## 12 342 519# Descriptive Statistics of categorical variables
with(DataLOY, table(PSSW, LOY))## LOY
## PSSW Notloyal Normal Loyal
## Bad 3 19 7
## Normal 8 286 345
## Good 1 37 167# Descriptive Statistics of categorical variables
with(DataLOY, do.call(rbind, tapply(TravelTime, LOY, function(x) c(M = mean(x), SD = sd(x)))))## M SD
## Notloyal 1.666667 2.692151
## Normal 1.348763 1.647059
## Loyal 1.243487 1.2896865.1. Estimate Multinominal Logit Regression Model - DataLOY for 2 cities
library(mlogitBMA)## Warning: package 'mlogitBMA' was built under R version 4.0.4## Loading required package: BMA## Loading required package: survival## Loading required package: leaps## Loading required package: robustbase##
## Attaching package: 'robustbase'## The following object is masked from 'package:survival':
##
## heart## Loading required package: inline## Loading required package: rrcov## Scalable Robust Estimators with High Breakdown Point (version 1.5-5)## Loading required package: abind## Loading required package: maxLik## Warning: package 'maxLik' was built under R version 4.0.4## Loading required package: miscTools## Warning: package 'miscTools' was built under R version 4.0.4##
## Attaching package: 'miscTools'## The following objects are masked from 'package:robustbase':
##
## colMedians, rowMedians##
## Please cite the 'maxLik' package as:
## Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.
##
## If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:
## https://r-forge.r-project.org/projects/maxlik/attach(DataLOY)## The following objects are masked from DataLOY (pos = 29):
##
## AGE, ATM, CITY, Departure, EC_Bus, EC_Stop, Education, EXB, FRE,
## Gender, ID, IMA, Income, LOY, MarriedStatus, Occupation, PEV, PHB,
## PPA, PPI, PSAB, PSEB, PSQ, PSSS, PSSW, SAT, SBE, SIM, TimeUseonBus,
## TravelTime, TripPurpose, WC_Bus, WC_Stopstr(DataLOY)## 'data.frame': 873 obs. of 33 variables:
## $ ID : int 3 4 5 6 7 8 10 11 12 13 ...
## $ AGE : Factor w/ 5 levels "16-25","26-35",..: 1 1 1 1 1 1 3 1 1 2 ...
## $ CITY : Factor w/ 2 levels "DaNang","HoChiMinh": 2 2 2 2 2 2 2 2 2 2 ...
## $ FRE : Factor w/ 4 levels ">=3 days/week",..: 1 2 1 1 1 1 1 1 1 1 ...
## $ TripPurpose : Factor w/ 5 levels "Working","Studying",..: 2 4 2 2 2 2 1 2 2 2 ...
## $ Departure : Factor w/ 2 levels "Normal","Peak-Hour": 1 1 2 2 2 2 2 2 1 2 ...
## $ TimeUseonBus : Factor w/ 6 levels "Using.telephone",..: 4 4 4 1 4 6 4 4 3 3 ...
## $ TravelTime : num 3 2 0.17 4 2 2 2 2.5 1.5 2 ...
## $ PSSW : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ PSSS : Factor w/ 3 levels "Bad","Normal",..: 2 1 2 2 2 2 2 2 2 2 ...
## $ PSAB : Factor w/ 3 levels "Bad","Normal",..: 3 2 2 3 2 2 3 2 2 3 ...
## $ PSEB : Factor w/ 3 levels "Bad","Normal",..: 3 3 2 3 2 2 2 3 3 2 ...
## $ PSQ : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 3 2 ...
## $ SAT : Factor w/ 3 levels "Not satisfied",..: 3 2 1 2 2 2 3 2 3 3 ...
## $ LOY : Factor w/ 3 levels "Notloyal","Normal",..: 3 2 2 2 2 2 3 2 3 3 ...
## $ IMA : Factor w/ 3 levels "Bad","Normal",..: 3 2 2 2 2 2 3 3 3 3 ...
## $ PHB : Factor w/ 3 levels "Bad","Normal",..: 3 2 2 2 3 2 3 3 3 3 ...
## $ PEV : Factor w/ 3 levels "Bad","Normal",..: 2 2 3 2 3 2 3 3 3 2 ...
## $ ATM : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ PPI : Factor w/ 3 levels "Bad","Normal",..: 3 2 2 2 2 2 3 1 2 2 ...
## $ SIM : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 3 1 3 2 2 3 ...
## $ PPA : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ SBE : Factor w/ 3 levels "Bad","Normal",..: 2 2 3 2 2 1 2 2 2 2 ...
## $ EXB : Factor w/ 3 levels "Bad","Normal",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EC_Stop : Factor w/ 2 levels "Ever","Never": 2 2 2 2 2 2 2 2 2 2 ...
## $ WC_Stop : Factor w/ 2 levels "Ever","Never": 1 2 2 1 1 1 2 1 2 2 ...
## $ EC_Bus : Factor w/ 2 levels "Ever","Never": 2 1 2 2 2 2 2 2 2 2 ...
## $ WC_Bus : Factor w/ 2 levels "Ever","Never": 1 2 2 1 1 1 2 1 2 2 ...
## $ Gender : Factor w/ 2 levels "Male","Female": 2 2 1 1 1 2 2 2 2 1 ...
## $ MarriedStatus: Factor w/ 2 levels "Single","Married": 1 1 1 1 1 1 2 1 1 1 ...
## $ Occupation : Factor w/ 7 levels "Students/Pupils",..: 1 1 1 1 1 1 2 1 1 7 ...
## $ Education : Factor w/ 5 levels "Secondary.school",..: 2 2 2 2 2 3 1 2 3 5 ...
## $ Income : Factor w/ 4 levels "<5millions","5-10millions",..: 1 1 1 1 1 1 1 1 1 1 ...# All variables - rempve: EC_Stop + WC_Stop + EC_Bus + WC_Bus + SAT and variable have cor > 0.7 - AGE
bma.LOY <- bic.mlogit(LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender + MarriedStatus + Occupation + Education + Income + CITY + FRE + TripPurpose + Departure + TimeUseonBus + TravelTime, data = DataLOY)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## lengthsummary(bma.LOY)##
## Call:
## bic.mlogit(f = LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender + MarriedStatus + Occupation + Education + Income + CITY + FRE + TripPurpose + Departure + TimeUseonBus + TravelTime, data = DataLOY)
##
##
## 1 models were selected
## Best 1 models (cumulative posterior probability = 1 ):
##
## p!=0 EV SD model 1
## Intercept 100 36.0341 1178.9464 36.0341
## Intercept.Notloyal 100 32.0865 0.4177 32.0865
## CITY 0
## . 0.0000 0.0000 .
## FRE 100
## . 0.3336 0.2479 0.3336
## TripPurpose 0
## . 1.4831 0.3405 1.4831
## Departure 0
## . 1.2862 0.3652 1.2862
## TimeUseonBus 0
## . 0.0000 0.0000 .
## TravelTime 0 0.0000 0.0000 .
## PSSW 0
## . 0.0000 0.0000 .
## PSSS 100
## . 0.0000 0.0000 .
## PSAB 0
## . 0.0000 0.0000 .
## PSEB 0
## . 0.0000 0.0000 .
## PSQ 100
## . 0.0000 0.0000 .
## IMA 100
## . 0.0000 0.0000 .
## PHB 100
## . 0.0000 0.0000 .
## PEV 0
## . 0.0000 0.0000 .
## ATM 100
## . 0.0000 0.0000 .
## PPI 0
## . 0.0000 0.0000 .
## SIM 0
## . 0.0000 0.0000 .
## PPA 0
## . 1.1964 0.4612 1.1964
## SBE 0
## . 0.4792 0.5457 0.4792
## EXB 100
## . 0.0000 0.0000 .
## Gender 0
## . 0.0000 0.0000 .
## MarriedStatus 100
## . 0.0000 0.0000 .
## Occupation 0
## . 0.0000 0.0000 .
## Education 0
## . -19.2575 533.3409 -19.2575
## Income 0
## . -20.5222 533.3409 -20.5222
##
## nVar 8
## BIC 29926.4743
## post prob 1
##
## MNL specification:
## ==================
## Response variable: LOY
## Base choice name: Loyal
## Base choice index: 1
## Frequency of alternatives:
## Loyal Normal Notloyal
## 519 342 12
##
## Equations:
## ----------
## alternative intercept 1 2 3 4
## 1 Loyal:
## 2 Normal: asc.Normal + CITY + FRE + TripPurpose + Departure
## 3 Notloyal: asc.Notloyal + CITY + FRE + TripPurpose + Departure
## 5 6 7 8 9 10 11 12 13
## 1
## 2 + TimeUseonBus + TravelTime + PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB
## 3 + TimeUseonBus + TravelTime + PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB
## 14 15 16 17 18 19 20 21 22
## 1
## 2 + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender + MarriedStatus
## 3 + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender + MarriedStatus
## 23 24 25
## 1
## 2 + Occupation + Education + Income
## 3 + Occupation + Education + Income# Remove 3 variables non significant : Gender, Departure, Travel time
bma.LOY1 <- bic.mlogit(LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + MarriedStatus + Occupation + Education + Income + CITY + FRE + TripPurpose + TimeUseonBus, data = DataLOY)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## lengthsummary(bma.LOY1)##
## Call:
## bic.mlogit(f = LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + MarriedStatus + Occupation + Education + Income + CITY + FRE + TripPurpose + TimeUseonBus, data = DataLOY)
##
##
## 1 models were selected
## Best 1 models (cumulative posterior probability = 1 ):
##
## p!=0 EV SD model 1
## Intercept 100 36.0341 1178.9464 36.0341
## Intercept.Notloyal 100 32.0865 0.4177 32.0865
## CITY 0
## . 0.0000 0.0000 .
## FRE 100
## . 0.3336 0.2479 0.3336
## TripPurpose 0
## . 1.4831 0.3405 1.4831
## TimeUseonBus 0
## . 1.2862 0.3652 1.2862
## PSSW 0
## . 0.0000 0.0000 .
## PSSS 100
## . 0.0000 0.0000 .
## PSAB 0
## . 0.0000 0.0000 .
## PSEB 0
## . 0.0000 0.0000 .
## PSQ 100
## . 0.0000 0.0000 .
## IMA 100
## . 0.0000 0.0000 .
## PHB 100
## . 0.0000 0.0000 .
## PEV 0
## . 0.0000 0.0000 .
## ATM 100
## . 0.0000 0.0000 .
## PPI 0
## . 0.0000 0.0000 .
## SIM 0
## . 0.0000 0.0000 .
## PPA 0
## . 1.1964 0.4612 1.1964
## SBE 0
## . 0.4792 0.5457 0.4792
## EXB 100
## . 0.0000 0.0000 .
## MarriedStatus 100
## . 0.0000 0.0000 .
## Occupation 0
## . 0.0000 0.0000 .
## Education 0
## . 0.0000 0.0000 .
## Income 0
## . -19.2575 533.3409 -19.2575
##
## nVar 7
## BIC 29926.4743
## post prob 1
##
## MNL specification:
## ==================
## Response variable: LOY
## Base choice name: Loyal
## Base choice index: 1
## Frequency of alternatives:
## Loyal Normal Notloyal
## 519 342 12
##
## Equations:
## ----------
## alternative intercept 1 2 3 4 5
## 1 Loyal:
## 2 Normal: asc.Normal + CITY + FRE + TripPurpose + TimeUseonBus + PSSW
## 3 Notloyal: asc.Notloyal + CITY + FRE + TripPurpose + TimeUseonBus + PSSW
## 6 7 8 9 10 11 12 13 14 15 16 17
## 1
## 2 + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE
## 3 + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE
## 18 19 20 21 22
## 1
## 2 + EXB + MarriedStatus + Occupation + Education + Income
## 3 + EXB + MarriedStatus + Occupation + Education + Income# Only variables related to personal perception , remove SAT
bma.LOY2 <- bic.mlogit(LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + CITY, data = DataLOY)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## Warning in new.output.names[names(output.names)[new.idx[i - 1]]] <-
## output.names[[i - : number of items to replace is not a multiple of replacement
## length
## Warning in new.output.names[names(output.names)[new.idx[i - 1]]] <-
## output.names[[i - : number of items to replace is not a multiple of replacement
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## Warning in new.output.names[names(output.names)[new.idx[i - 1]]] <-
## output.names[[i - : number of items to replace is not a multiple of replacement
## length
## Warning in new.output.names[names(output.names)[new.idx[i - 1]]] <-
## output.names[[i - : number of items to replace is not a multiple of replacement
## length
## Warning in new.output.names[names(output.names)[new.idx[i - 1]]] <-
## output.names[[i - : number of items to replace is not a multiple of replacement
## length
## Warning in new.output.names[names(output.names)[new.idx[i - 1]]] <-
## output.names[[i - : number of items to replace is not a multiple of replacement
## lengthsummary(bma.LOY2)##
## Call:
## bic.mlogit(f = LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + CITY, data = DataLOY)
##
##
## 1 models were selected
## Best 1 models (cumulative posterior probability = 1 ):
##
## p!=0 EV SD model 1
## Intercept 100 36.4914 1174.4700 36.4914
## Intercept.Notloyal 100 32.6028 0.4084 32.6028
## CITY 0
## . 0.0000 0.0000 .
## PSSW 0
## . 0.0000 0.0000 .
## PSSS 100
## . 0.0000 0.0000 .
## PSAB 0
## . 1.1507 0.4641 1.1507
## PSEB 0
## . 0.4067 0.5461 0.4067
## PSQ 100
## . 0.0000 0.0000 .
## IMA 100
## . 0.0000 0.0000 .
## PHB 100
## . 0.0000 0.0000 .
## PEV 100
## . 0.0000 0.0000 .
## ATM 100
## . -19.7402 531.5506 -19.7402
## PPI 0
## . -20.8711 531.5506 -20.8711
## SIM 0
## . -0.7890 1.3412 -0.7890
## PPA 0
## . -1.9856 1.3444 -1.9856
## SBE 0
## . -1.4671 0.9097 -1.4671
## EXB 100
## . -2.3744 0.9281 -2.3744
##
## nVar 9
## BIC 44532.8262
## post prob 1
##
## MNL specification:
## ==================
## Response variable: LOY
## Base choice name: Loyal
## Base choice index: 1
## Frequency of alternatives:
## Loyal Normal Notloyal
## 519 342 12
##
## Equations:
## ----------
## alternative intercept 1 2 3 4 5 6 7 8
## 1 Loyal:
## 2 Normal: asc.Normal + CITY + PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB
## 3 Notloyal: asc.Notloyal + CITY + PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB
## 9 10 11 12 13 14 15
## 1
## 2 + PEV + ATM + PPI + SIM + PPA + SBE + EXB
## 3 + PEV + ATM + PPI + SIM + PPA + SBE + EXB# Multinominal Logit Model
## Way 1 - Use mlogit (in mlogit package) - Khong dung
## Way 2 - Use multinom (in nnet package)
library(nnet)
attach(DataLOY)## The following objects are masked from DataLOY (pos = 4):
##
## AGE, ATM, CITY, Departure, EC_Bus, EC_Stop, Education, EXB, FRE,
## Gender, ID, IMA, Income, LOY, MarriedStatus, Occupation, PEV, PHB,
## PPA, PPI, PSAB, PSEB, PSQ, PSSS, PSSW, SAT, SBE, SIM, TimeUseonBus,
## TravelTime, TripPurpose, WC_Bus, WC_Stop## The following objects are masked from DataLOY (pos = 31):
##
## AGE, ATM, CITY, Departure, EC_Bus, EC_Stop, Education, EXB, FRE,
## Gender, ID, IMA, Income, LOY, MarriedStatus, Occupation, PEV, PHB,
## PPA, PPI, PSAB, PSEB, PSQ, PSSS, PSSW, SAT, SBE, SIM, TimeUseonBus,
## TravelTime, TripPurpose, WC_Bus, WC_Stop# Model - all variable according bma.LOY with 8 variables (including LOY)
#DataLOY$LOY <- relevel (DataLOY$LOY, ref = "Notloyal")
mlm <- multinom(LOY ~ SBE + EXB + Education + Income + FRE + TripPurpose + Departure, data = DataLOY)## # weights: 63 (40 variable)
## initial value 959.088528
## iter 10 value 529.560444
## iter 20 value 480.933302
## iter 30 value 466.344540
## iter 40 value 465.724566
## iter 50 value 465.663985
## iter 60 value 465.656585
## final value 465.656434
## convergedsummary(mlm)## Call:
## multinom(formula = LOY ~ SBE + EXB + Education + Income + FRE +
## TripPurpose + Departure, data = DataLOY)
##
## Coefficients:
## (Intercept) SBENormal SBEGood EXBNormal EXBGood
## Normal 13.340905 -1.076233 -1.301380 3.396392 5.354233
## Loyal -5.371008 -1.644830 -1.293262 22.106039 26.442854
## EducationUndergraduate EducationHigh.school EducationPostgraduate
## Normal -11.14706 -12.52156 9.262681
## Loyal -11.55997 -13.15208 8.767315
## EducationOthers Income5-10millions Income10-15millions Income>15millions
## Normal 4.939751 -0.3243689 -1.639542 -0.3940470
## Loyal 4.591174 -0.4497308 -1.033433 -0.4039593
## FRE2days/month-2days/week FRE2days/year-1day/month FRE<2 days/year
## Normal -0.6639159 -0.4499595 -1.408535
## Loyal -1.0621024 -1.8065568 -2.609713
## TripPurposeStudying TripPurposeShopping TripPurposeEntertaining
## Normal -0.1896839 12.13219 0.7661826
## Loyal -0.2519730 13.16394 1.1329677
## TripPurposeOthers DeparturePeak-Hour
## Normal -0.7080448 0.5532958
## Loyal 0.2875327 0.2635579
##
## Std. Errors:
## (Intercept) SBENormal SBEGood EXBNormal EXBGood EducationUndergraduate
## Normal 1.424073 2.098640 2.091548 2.2188336 2.456066 0.8210815
## Loyal 1.487793 2.137898 2.132830 0.8154054 1.038864 0.8308179
## EducationHigh.school EducationPostgraduate EducationOthers
## Normal 0.6627280 0.2315569 0.2521608
## Loyal 0.6739911 0.2315569 0.2521608
## Income5-10millions Income10-15millions Income>15millions
## Normal 0.9047301 1.033293 1.386459
## Loyal 0.9231890 1.049415 1.420909
## FRE2days/month-2days/week FRE2days/year-1day/month FRE<2 days/year
## Normal 0.9759974 1.126319 0.8778376
## Loyal 0.9890472 1.150157 0.9184374
## TripPurposeStudying TripPurposeShopping TripPurposeEntertaining
## Normal 1.003048 0.2197874 1.380563
## Loyal 1.020922 0.2197873 1.398536
## TripPurposeOthers DeparturePeak-Hour
## Normal 1.137552 0.6747175
## Loyal 1.157813 0.6867699
##
## Residual Deviance: 931.3129
## AIC: 1011.313# Model 1 - bma1 - 7 variables (including LOY)
mlm1 <- multinom(LOY ~ SBE + EXB + Income + FRE + TripPurpose + TimeUseonBus, data = DataLOY)## # weights: 63 (40 variable)
## initial value 959.088528
## iter 10 value 530.506671
## iter 20 value 478.737234
## iter 30 value 470.754391
## iter 40 value 470.430184
## iter 50 value 470.378116
## iter 60 value 470.376759
## final value 470.376732
## convergedsummary(mlm1)## Call:
## multinom(formula = LOY ~ SBE + EXB + Income + FRE + TripPurpose +
## TimeUseonBus, data = DataLOY)
##
## Coefficients:
## (Intercept) SBENormal SBEGood EXBNormal EXBGood Income5-10millions
## Normal 2.009112 -0.1795463 -0.3027600 2.341089 4.097177 -0.1591257
## Loyal -10.523816 -0.8292513 -0.3595794 14.193496 18.332198 -0.3036824
## Income10-15millions Income>15millions FRE2days/month-2days/week
## Normal -1.513586 -0.9647707 -0.5277123
## Loyal -1.014448 -1.0811549 -0.9311274
## FRE2days/year-1day/month FRE<2 days/year TripPurposeStudying
## Normal -0.4709656 -1.561699 -0.6436715
## Loyal -1.8216605 -2.709011 -0.7767144
## TripPurposeShopping TripPurposeEntertaining TripPurposeOthers
## Normal 10.89496 0.5985948 -0.6497576
## Loyal 12.04208 0.9934240 0.3536449
## TimeUseonBusReading TimeUseonBusListening TimeUseonBusNothing
## Normal -0.4192942 0.7781136 0.3187461
## Loyal -0.3065901 0.8848335 0.5220651
## TimeUseonBusTalking TimeUseonBusOthers
## Normal -1.249049 -0.4991282
## Loyal -1.367315 -1.2822985
##
## Std. Errors:
## (Intercept) SBENormal SBEGood EXBNormal EXBGood Income5-10millions
## Normal 1.882346 1.648319 1.692526 1.8687275 2.107781 0.9112530
## Loyal 1.523750 1.698119 1.742289 0.8183327 1.058051 0.9294481
## Income10-15millions Income>15millions FRE2days/month-2days/week
## Normal 1.040424 1.256988 0.9356782
## Loyal 1.054755 1.293470 0.9502614
## FRE2days/year-1day/month FRE<2 days/year TripPurposeStudying
## Normal 1.151380 0.8726954 1.026066
## Loyal 1.175021 0.9112343 1.041604
## TripPurposeShopping TripPurposeEntertaining TripPurposeOthers
## Normal 0.2147097 1.327335 1.036041
## Loyal 0.2147087 1.349291 1.063685
## TimeUseonBusReading TimeUseonBusListening TimeUseonBusNothing
## Normal 1.250009 1.205276 0.7839716
## Loyal 1.272051 1.221551 0.7995423
## TimeUseonBusTalking TimeUseonBusOthers
## Normal 1.445085 1.274265
## Loyal 1.482142 1.358611
##
## Residual Deviance: 940.7535
## AIC: 1020.753# Model 2 - bma2 - 9 variables (including LOY) - Chon
mlm2 <- multinom(LOY ~ PSAB + PSEB + PHB + PEV + ATM + PPI + SIM + PPA, data = DataLOY)## # weights: 54 (34 variable)
## initial value 959.088528
## iter 10 value 440.792978
## iter 20 value 422.539924
## iter 30 value 420.412418
## iter 40 value 420.402272
## final value 420.402218
## convergedsummary(mlm2)## Call:
## multinom(formula = LOY ~ PSAB + PSEB + PHB + PEV + ATM + PPI +
## SIM + PPA, data = DataLOY)
##
## Coefficients:
## (Intercept) PSABNormal PSABGood PSEBNormal PSEBGood PHBNormal PHBGood
## Normal -0.3059144 1.080030 0.7505609 -0.6250326 1.259825 2.965953 3.316235
## Loyal -2.6407798 1.272423 1.4888842 0.1006248 2.724897 3.331470 5.039895
## PEVNormal PEVGood ATMNormal ATMGood PPINormal PPIGood SIMNormal
## Normal -2.106228 -3.258704 0.9687162 0.6638212 0.5131894 -0.08541818 1.607859
## Loyal -3.003782 -3.202613 0.8832463 1.9445627 0.3395883 0.01854697 2.685873
## SIMGood PPANormal PPAGood
## Normal 0.6639493 0.2609759 0.3741495
## Loyal 2.5635209 -1.4446922 -1.1348244
##
## Std. Errors:
## (Intercept) PSABNormal PSABGood PSEBNormal PSEBGood PHBNormal PHBGood
## Normal 1.999025 0.8653326 1.124664 1.616563 1.797284 1.498112 1.636963
## Loyal 2.351430 0.9374917 1.175167 1.810333 1.963063 1.707677 1.831369
## PEVNormal PEVGood ATMNormal ATMGood PPINormal PPIGood SIMNormal
## Normal 1.614666 1.781084 0.7690741 1.346193 0.8109683 1.068711 1.048240
## Loyal 1.715557 1.865437 0.8398455 1.373638 0.8294600 1.083364 1.210105
## SIMGood PPANormal PPAGood
## Normal 1.158878 1.436198 1.671576
## Loyal 1.302728 1.631320 1.835302
##
## Residual Deviance: 840.8044
## AIC: 908.8044# CHON: Models all varibles , removing : EC_Stop + WC_Stop + EC_Bus + WC_Bus + SAT and variable have cor > 0.7 - AGE, Occupation
mlm3 <- multinom(LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender + MarriedStatus + Education + Income + CITY + FRE + TripPurpose + Departure + TimeUseonBus + TravelTime, data = DataLOY)## # weights: 162 (106 variable)
## initial value 959.088528
## iter 10 value 448.583007
## iter 20 value 325.461769
## iter 30 value 312.497693
## iter 40 value 309.339581
## iter 50 value 305.306603
## iter 60 value 298.178261
## iter 70 value 291.012723
## iter 80 value 284.411255
## iter 90 value 284.345707
## final value 284.345406
## convergedsummary(mlm3)## Call:
## multinom(formula = LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA +
## PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender +
## MarriedStatus + Education + Income + CITY + FRE + TripPurpose +
## Departure + TimeUseonBus + TravelTime, data = DataLOY)
##
## Coefficients:
## (Intercept) PSSWNormal PSSWGood PSSSNormal PSSSGood PSABNormal
## Normal 1257.661 -584.1355 -682.6143 415.1726 392.8677 -362.6641
## Loyal -2094.867 -583.2539 -681.6248 413.5470 391.5456 -362.2854
## PSABGood PSEBNormal PSEBGood PSQNormal PSQGood IMANormal IMAGood
## Normal -670.4267 -887.8612 -537.8440 1660.626 2302.456 -908.5712 -825.1226
## Loyal -669.7998 -887.7083 -537.3299 4611.436 5254.662 -907.7426 -822.8407
## PHBNormal PHBGood PEVNormal PEVGood ATMNormal ATMGood PPINormal
## Normal 1461.082 1722.701 -785.2219 -894.9369 309.8065 468.7040 -235.4085
## Loyal 1462.893 1725.056 -785.4505 -893.9425 309.8411 469.7273 -235.6623
## PPIGood SIMNormal SIMGood PPANormal PPAGood SBENormal SBEGood
## Normal -562.6968 264.4541 517.6814 667.9605 543.9452 -1236.047 -1372.306
## Loyal -562.7633 266.6799 520.7478 665.7408 541.7493 -1237.640 -1374.599
## EXBNormal EXBGood GenderFemale MarriedStatusMarried
## Normal 396.3756 622.1244 10.30465 621.6174
## Loyal 794.8300 1021.9930 10.42988 622.4924
## EducationUndergraduate EducationHigh.school EducationPostgraduate
## Normal -206.5152 -679.3822 681.9964
## Loyal -206.6971 -680.0214 681.1378
## EducationOthers Income5-10millions Income10-15millions Income>15millions
## Normal -395.9537 -321.6366 -536.4471 -229.1254
## Loyal -396.3906 -322.3382 -536.3944 -229.2058
## CITYHoChiMinh FRE2days/month-2days/week FRE2days/year-1day/month
## Normal 91.73704 -533.1651 -292.5247
## Loyal 92.19897 -533.3950 -294.2563
## FRE<2 days/year TripPurposeStudying TripPurposeShopping
## Normal -426.4553 -151.2124 -235.9813
## Loyal -427.7876 -151.1273 -235.4869
## TripPurposeEntertaining TripPurposeOthers DeparturePeak-Hour
## Normal -358.5433 -143.1627 140.7479
## Loyal -358.7073 -143.1526 140.3890
## TimeUseonBusReading TimeUseonBusListening TimeUseonBusNothing
## Normal -491.3556 388.1959 -101.4723
## Loyal -490.6909 388.3560 -101.7316
## TimeUseonBusTalking TimeUseonBusOthers TravelTime
## Normal -304.4614 -195.0872 -70.00133
## Loyal -304.5148 -195.1695 -70.15726
##
## Std. Errors:
## (Intercept) PSSWNormal PSSWGood PSSSNormal PSSSGood PSABNormal
## Normal 0.6581299 0.3922035 0.4146871 0.2682563 0.3147663 0.2795347
## Loyal 0.6581298 0.3922035 0.4146871 0.2682563 0.3147663 0.2795347
## PSABGood PSEBNormal PSEBGood PSQNormal PSQGood IMANormal IMAGood
## Normal 0.2916871 0.8229319 0.8225688 0.3316257 0.3406582 0.6253969 0.6299991
## Loyal 0.2916872 0.8229319 0.8225690 0.3316257 0.3406581 0.6253969 0.6299990
## PHBNormal PHBGood PEVNormal PEVGood ATMNormal ATMGood PPINormal
## Normal 0.6026896 0.6077969 0.4019731 0.3986046 0.3263172 0.354034 0.1636842
## Loyal 0.6026896 0.6077970 0.4019731 0.3986045 0.3263172 0.354034 0.1636842
## PPIGood SIMNormal SIMGood PPANormal PPAGood SBENormal SBEGood
## Normal 0.2085011 0.4186579 0.4338032 0.7330583 0.7436011 0.5014321 0.5215122
## Loyal 0.2085011 0.4186579 0.4338033 0.7330583 0.7436010 0.5014321 0.5215122
## EXBNormal EXBGood GenderFemale MarriedStatusMarried
## Normal 0.3321563 0.3381117 0.1186229 0.1541702
## Loyal 0.3321563 0.3381117 0.1186229 0.1541702
## EducationUndergraduate EducationHigh.school EducationPostgraduate
## Normal 0.2680115 0.2759763 0.3072579
## Loyal 0.2680115 0.2759763 0.3072579
## EducationOthers Income5-10millions Income10-15millions Income>15millions
## Normal 0.3360395 0.1619749 0.2156094 0.3245623
## Loyal 0.3360395 0.1619749 0.2156094 0.3245623
## CITYHoChiMinh FRE2days/month-2days/week FRE2days/year-1day/month
## Normal 0.1436039 0.1491633 0.2203619
## Loyal 0.1436039 0.1491633 0.2203619
## FRE<2 days/year TripPurposeStudying TripPurposeShopping
## Normal 0.2352733 0.1749363 0.2893050
## Loyal 0.2352733 0.1749363 0.2893051
## TripPurposeEntertaining TripPurposeOthers DeparturePeak-Hour
## Normal 0.2186078 0.2252801 0.1239134
## Loyal 0.2186078 0.2252801 0.1239134
## TimeUseonBusReading TimeUseonBusListening TimeUseonBusNothing
## Normal 0.2682147 0.1748448 0.1421872
## Loyal 0.2682148 0.1748448 0.1421872
## TimeUseonBusTalking TimeUseonBusOthers TravelTime
## Normal 0.310812 0.4005356 0.04643793
## Loyal 0.310812 0.4005356 0.04643792
##
## Residual Deviance: 568.6908
## AIC: 780.6908 ## Calculate OR and CI
exp(coef(mlm3))## (Intercept) PSSWNormal PSSWGood PSSSNormal PSSSGood
## Normal Inf 2.056757e-254 3.502313e-297 2.028405e+180 4.171341e+170
## Loyal 0 4.966270e-254 9.421676e-297 3.991876e+179 1.111978e+170
## PSABNormal PSABGood PSEBNormal PSEBGood PSQNormal PSQGood
## Normal 3.140522e-158 6.876598e-292 0 2.614189e-234 Inf Inf
## Loyal 4.586090e-158 1.287183e-291 0 4.370969e-234 Inf Inf
## IMANormal IMAGood PHBNormal PHBGood PEVNormal PEVGood ATMNormal
## Normal 0 0 Inf Inf 0 0 3.525593e+134
## Loyal 0 0 Inf Inf 0 0 3.649978e+134
## ATMGood PPINormal PPIGood SIMNormal SIMGood
## Normal 3.593767e+203 5.799300e-103 4.205987e-245 7.094893e+114 6.701295e+224
## Loyal 9.998979e+203 4.499692e-103 3.935550e-245 6.570636e+115 1.438510e+226
## PPANormal PPAGood SBENormal SBEGood EXBNormal
## Normal 1.234708e+290 1.707695e+236 0 0 1.392336e+172
## Loyal 1.341454e+289 1.899972e+235 0 0 Inf
## EXBGood GenderFemale MarriedStatusMarried EducationUndergraduate
## Normal 1.531792e+270 29871.24 9.225600e+269 2.049216e-90
## Loyal Inf 33856.44 2.213196e+270 1.708358e-90
## EducationHigh.school EducationPostgraduate EducationOthers
## Normal 8.873190e-296 1.539136e+296 1.095180e-172
## Loyal 4.682396e-296 6.521899e+295 7.075585e-173
## Income5-10millions Income10-15millions Income>15millions CITYHoChiMinh
## Normal 2.065468e-140 1.056814e-233 3.105193e-100 6.932517e+39
## Loyal 1.024014e-140 1.113967e-233 2.865348e-100 1.100278e+40
## FRE2days/month-2days/week FRE2days/year-1day/month FRE<2 days/year
## Normal 2.814156e-232 9.080874e-128 6.205996e-186
## Loyal 2.236051e-232 1.607403e-128 1.637556e-186
## TripPurposeStudying TripPurposeShopping TripPurposeEntertaining
## Normal 2.134481e-66 3.270714e-103 1.934701e-156
## Loyal 2.324093e-66 5.362340e-103 1.642138e-156
## TripPurposeOthers DeparturePeak-Hour TimeUseonBusReading
## Normal 6.686720e-63 1.336757e+61 4.045524e-214
## Loyal 6.754807e-63 9.335897e+60 7.864231e-214
## TimeUseonBusListening TimeUseonBusNothing TimeUseonBusTalking
## Normal 3.902548e+168 8.533504e-45 5.944441e-133
## Loyal 4.580196e+168 6.584600e-45 5.634983e-133
## TimeUseonBusOthers TravelTime
## Normal 1.882346e-85 3.970169e-31
## Loyal 1.733647e-85 3.396943e-31exp(confint(mlm3))## , , Normal
##
## 2.5 % 97.5 %
## (Intercept) Inf Inf
## PSSWNormal 9.535396e-255 4.436363e-254
## PSSWGood 1.553720e-297 7.894731e-297
## PSSSNormal 1.198987e+180 3.431586e+180
## PSSSGood 2.250848e+170 7.730459e+170
## PSABNormal 1.815773e-158 5.431780e-158
## PSABGood 3.882300e-292 1.218031e-291
## PSEBNormal 0.000000e+00 0.000000e+00
## PSEBGood 5.213930e-235 1.310717e-233
## PSQNormal Inf Inf
## PSQGood Inf Inf
## IMANormal 0.000000e+00 0.000000e+00
## IMAGood 0.000000e+00 0.000000e+00
## PHBNormal Inf Inf
## PHBGood Inf Inf
## PEVNormal 0.000000e+00 0.000000e+00
## PEVGood 0.000000e+00 0.000000e+00
## ATMNormal 1.859818e+134 6.683346e+134
## ATMGood 1.795542e+203 7.192902e+203
## PPINormal 4.207725e-103 7.992890e-103
## PPIGood 2.795062e-245 6.329136e-245
## SIMNormal 3.123084e+114 1.611789e+115
## SIMGood 2.863550e+224 1.568241e+225
## PPANormal 2.934836e+289 5.194511e+290
## PPAGood 3.976088e+235 7.334404e+236
## SBENormal 0.000000e+00 0.000000e+00
## SBEGood 0.000000e+00 0.000000e+00
## EXBNormal 7.261262e+171 2.669783e+172
## EXBGood 7.895844e+269 2.971673e+270
## GenderFemale 2.367453e+04 3.768992e+04
## MarriedStatusMarried 6.819691e+269 1.248029e+270
## EducationUndergraduate 1.211870e-90 3.465130e-90
## EducationHigh.school 5.166166e-296 1.524022e-295
## EducationPostgraduate 8.428272e+295 2.810706e+296
## EducationOthers 5.668239e-173 2.116035e-172
## Income5-10millions 1.503644e-140 2.837212e-140
## Income10-15millions 6.925824e-234 1.612596e-233
## Income>15millions 1.643693e-100 5.866195e-100
## CITYHoChiMinh 5.231847e+39 9.186009e+39
## FRE2days/month-2days/week 2.100778e-232 3.769782e-232
## FRE2days/year-1day/month 5.895969e-128 1.398621e-127
## FRE<2 days/year 3.913331e-186 9.841844e-186
## TripPurposeStudying 1.514908e-66 3.007449e-66
## TripPurposeShopping 1.855179e-103 5.766330e-103
## TripPurposeEntertaining 1.260476e-156 2.969568e-156
## TripPurposeOthers 4.299861e-63 1.039853e-62
## DeparturePeak-Hour 1.048521e+61 1.704228e+61
## TimeUseonBusReading 2.391498e-214 6.843520e-214
## TimeUseonBusListening 2.770257e+168 5.497642e+168
## TimeUseonBusNothing 6.457991e-45 1.127606e-44
## TimeUseonBusTalking 3.232566e-133 1.093137e-132
## TimeUseonBusOthers 8.585447e-86 4.127014e-85
## TravelTime 3.624774e-31 4.348475e-31
##
## , , Loyal
##
## 2.5 % 97.5 %
## (Intercept) 0.000000e+00 0.000000e+00
## PSSWNormal 2.302428e-254 1.071210e-253
## PSSWGood 4.179707e-297 2.123785e-296
## PSSSNormal 2.359592e+179 6.753318e+179
## PSSSGood 6.000214e+169 2.060753e+170
## PSABNormal 2.651565e-158 7.932004e-158
## PSABGood 7.267008e-292 2.279947e-291
## PSEBNormal 0.000000e+00 0.000000e+00
## PSEBGood 8.717778e-235 2.191542e-233
## PSQNormal Inf Inf
## PSQGood Inf Inf
## IMANormal 0.000000e+00 0.000000e+00
## IMAGood 0.000000e+00 0.000000e+00
## PHBNormal Inf Inf
## PHBGood Inf Inf
## PEVNormal 0.000000e+00 0.000000e+00
## PEVGood 0.000000e+00 0.000000e+00
## ATMNormal 1.925434e+134 6.919138e+134
## ATMGood 4.995757e+203 2.001290e+204
## PPINormal 3.264784e-103 6.201704e-103
## PPIGood 2.615345e-245 5.922184e-245
## SIMNormal 2.892312e+115 1.492690e+116
## SIMGood 6.146939e+225 3.366409e+226
## PPANormal 3.188567e+288 5.643601e+289
## PPAGood 4.423774e+234 8.160216e+235
## SBENormal 0.000000e+00 0.000000e+00
## SBEGood 0.000000e+00 0.000000e+00
## EXBNormal Inf Inf
## EXBGood Inf Inf
## GenderFemale 2.683301e+04 4.271823e+04
## MarriedStatusMarried 1.636025e+270 2.993986e+270
## EducationUndergraduate 1.010293e-90 2.888756e-90
## EducationHigh.school 2.726193e-296 8.042288e-296
## EducationPostgraduate 3.571377e+295 1.191002e+296
## EducationOthers 3.662056e-173 1.367098e-172
## Income5-10millions 7.454741e-141 1.406628e-140
## Income10-15millions 7.300378e-234 1.699806e-233
## Income>15millions 1.516734e-100 5.413090e-100
## CITYHoChiMinh 8.303602e+39 1.457936e+40
## FRE2days/month-2days/week 1.669220e-232 2.995365e-232
## FRE2days/year-1day/month 1.043644e-128 2.475695e-128
## FRE<2 days/year 1.032598e-186 2.596935e-186
## TripPurposeStudying 1.649482e-66 3.274610e-66
## TripPurposeShopping 3.041567e-103 9.453907e-103
## TripPurposeEntertaining 1.069868e-156 2.520513e-156
## TripPurposeOthers 4.343644e-63 1.050441e-62
## DeparturePeak-Hour 7.322861e+60 1.190231e+61
## TimeUseonBusReading 4.648913e-214 1.330335e-213
## TimeUseonBusListening 3.251291e+168 6.452265e+168
## TimeUseonBusNothing 4.983099e-45 8.700804e-45
## TimeUseonBusTalking 3.064283e-133 1.036230e-132
## TimeUseonBusOthers 7.907226e-86 3.800994e-85
## TravelTime 3.101417e-31 3.720628e-31est3 <- estimate.mlogit(LOY ~ PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender + MarriedStatus + Education + Income + CITY + FRE + TripPurpose + Departure + TimeUseonBus + TravelTime, data = DataLOY)
summary(est3)##
## successive function values within relative tolerance limit (reltol)
##
## Log-Likelihood: -360.6
## Null Log-Likelihood: -959.1
## Likelihood ratio index: 0.624
## AIC: 773.2037
## BIC: 897.274
## Sample size: 873
## Iterations: 15
## Suggested |t-value| > 2.602294
## Convergence statistics: 1.397838e-05
##
## Estimated using BHHH in 0.8686872 s.
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## asc.Normal 16.17349 1.67343 9.66486 0.000e+00
## asc.Notloyal 12.82368 1.70156 7.53643 4.841e-14
## CITY -0.39589 0.26639 -1.48612 1.372e-01
## FRE 0.51684 0.14242 3.62905 2.845e-04
## TripPurpose -0.01477 0.09686 -0.15254 8.788e-01
## Departure 0.31480 0.22863 1.37693 1.685e-01
## TimeUseonBus 0.05929 0.07580 0.78218 4.341e-01
## TravelTime 0.14216 0.09389 1.51417 1.300e-01
## PSSW -0.20452 0.26881 -0.76084 4.468e-01
## PSSS 0.03583 0.26397 0.13573 8.920e-01
## PSAB -0.18273 0.21869 -0.83553 4.034e-01
## PSEB -0.16135 0.27509 -0.58653 5.575e-01
## PSQ -1.39215 0.25232 -5.51750 3.439e-08
## IMA -1.30570 0.22377 -5.83503 5.378e-09
## PHB -0.55016 0.22762 -2.41702 1.565e-02
## PEV -0.83429 0.21227 -3.93034 8.483e-05
## ATM -0.74085 0.24479 -3.02650 2.474e-03
## PPI 0.09453 0.19382 0.48773 6.257e-01
## SIM -0.98546 0.22578 -4.36463 1.273e-05
## PPA 0.01585 0.26714 0.05933 9.527e-01
## SBE 0.67864 0.24868 2.72895 6.354e-03
## EXB -1.35733 0.24612 -5.51493 3.489e-08
## Gender -0.14065 0.23309 -0.60341 5.462e-01
## MarriedStatus -0.85054 0.26823 -3.17094 1.519e-03
## Education 0.23828 0.11419 2.08667 3.692e-02
## Income 0.06244 0.16046 0.38914 6.972e-01
##
## Response variable: LOY
## Base choice name: Loyal
## Base choice index: 1
## Frequency of alternatives:
## Loyal Normal Notloyal
## 519 342 12
##
## Equations:
## ----------
## alternative intercept 1 2 3 4
## 1 Loyal:
## 2 Normal: asc.Normal + CITY + FRE + TripPurpose + Departure
## 3 Notloyal: asc.Notloyal + CITY + FRE + TripPurpose + Departure
## 5 6 7 8 9 10 11 12 13
## 1
## 2 + TimeUseonBus + TravelTime + PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB
## 3 + TimeUseonBus + TravelTime + PSSW + PSSS + PSAB + PSEB + PSQ + IMA + PHB
## 14 15 16 17 18 19 20 21 22
## 1
## 2 + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender + MarriedStatus
## 3 + PEV + ATM + PPI + SIM + PPA + SBE + EXB + Gender + MarriedStatus
## 23 24
## 1
## 2 + Education + Income
## 3 + Education + Income ## Calculate p-value
z3 <- summary(mlm3)$coefficients/summary(mlm3)$standard.errors
z3## (Intercept) PSSWNormal PSSWGood PSSSNormal PSSSGood PSABNormal
## Normal 1910.962 -1489.368 -1646.095 1547.671 1248.125 -1297.385
## Loyal -3183.060 -1487.121 -1643.708 1541.612 1243.925 -1296.030
## PSABGood PSEBNormal PSEBGood PSQNormal PSQGood IMANormal IMAGood
## Normal -2298.445 -1078.900 -653.8589 5007.532 6758.846 -1452.791 -1309.720
## Loyal -2296.295 -1078.714 -653.2339 13905.547 15425.031 -1451.467 -1306.098
## PHBNormal PHBGood PEVNormal PEVGood ATMNormal ATMGood PPINormal
## Normal 2424.269 2834.337 -1953.419 -2245.175 949.4028 1323.895 -1438.187
## Loyal 2427.273 2838.211 -1953.987 -2242.681 949.5091 1326.786 -1439.737
## PPIGood SIMNormal SIMGood PPANormal PPAGood SBENormal SBEGood
## Normal -2698.772 631.6711 1193.355 911.197 731.5013 -2465.035 -2631.398
## Loyal -2699.090 636.9877 1200.424 908.169 728.5484 -2468.211 -2635.795
## EXBNormal EXBGood GenderFemale MarriedStatusMarried
## Normal 1193.341 1839.996 86.86902 4032.019
## Loyal 2392.940 3022.649 87.92474 4037.695
## EducationUndergraduate EducationHigh.school EducationPostgraduate
## Normal -770.5460 -2461.741 2219.622
## Loyal -771.2248 -2464.057 2216.827
## EducationOthers Income5-10millions Income10-15millions Income>15millions
## Normal -1178.295 -1985.718 -2488.051 -705.9522
## Loyal -1179.595 -1990.050 -2487.806 -706.1999
## CITYHoChiMinh FRE2days/month-2days/week FRE2days/year-1day/month
## Normal 638.8198 -3574.371 -1327.474
## Loyal 642.0364 -3575.913 -1335.332
## FRE<2 days/year TripPurposeStudying TripPurposeShopping
## Normal -1812.596 -864.3854 -815.6833
## Loyal -1818.258 -863.8989 -813.9741
## TripPurposeEntertaining TripPurposeOthers DeparturePeak-Hour
## Normal -1640.121 -635.4878 1135.857
## Loyal -1640.871 -635.4428 1132.960
## TimeUseonBusReading TimeUseonBusListening TimeUseonBusNothing
## Normal -1831.948 2220.232 -713.6531
## Loyal -1829.470 2221.147 -715.4765
## TimeUseonBusTalking TimeUseonBusOthers TravelTime
## Normal -979.5676 -487.0659 -1507.417
## Loyal -979.7396 -487.2713 -1510.775pvalue3 <- (1-pnorm(abs(z3), 0, 1))*2
pvalue3## (Intercept) PSSWNormal PSSWGood PSSSNormal PSSSGood PSABNormal PSABGood
## Normal 0 0 0 0 0 0 0
## Loyal 0 0 0 0 0 0 0
## PSEBNormal PSEBGood PSQNormal PSQGood IMANormal IMAGood PHBNormal
## Normal 0 0 0 0 0 0 0
## Loyal 0 0 0 0 0 0 0
## PHBGood PEVNormal PEVGood ATMNormal ATMGood PPINormal PPIGood SIMNormal
## Normal 0 0 0 0 0 0 0 0
## Loyal 0 0 0 0 0 0 0 0
## SIMGood PPANormal PPAGood SBENormal SBEGood EXBNormal EXBGood
## Normal 0 0 0 0 0 0 0
## Loyal 0 0 0 0 0 0 0
## GenderFemale MarriedStatusMarried EducationUndergraduate
## Normal 0 0 0
## Loyal 0 0 0
## EducationHigh.school EducationPostgraduate EducationOthers
## Normal 0 0 0
## Loyal 0 0 0
## Income5-10millions Income10-15millions Income>15millions CITYHoChiMinh
## Normal 0 0 0 0
## Loyal 0 0 0 0
## FRE2days/month-2days/week FRE2days/year-1day/month FRE<2 days/year
## Normal 0 0 0
## Loyal 0 0 0
## TripPurposeStudying TripPurposeShopping TripPurposeEntertaining
## Normal 0 0 0
## Loyal 0 0 0
## TripPurposeOthers DeparturePeak-Hour TimeUseonBusReading
## Normal 0 0 0
## Loyal 0 0 0
## TimeUseonBusListening TimeUseonBusNothing TimeUseonBusTalking
## Normal 0 0 0
## Loyal 0 0 0
## TimeUseonBusOthers TravelTime
## Normal 0 0
## Loyal 0 0