Homework4- Dan Liu
Exercise 5.1 a.
library(logmult)## Warning: package 'logmult' was built under R version 3.5.3## Loading required package: gnm## Warning: package 'gnm' was built under R version 3.5.3##
## Attaching package: 'logmult'## The following object is masked from 'package:gnm':
##
## selibrary(MASS)## Warning: package 'MASS' was built under R version 3.5.3library(vcd)## Warning: package 'vcd' was built under R version 3.5.3## Loading required package: grid##
## Attaching package: 'vcd'## The following object is masked from 'package:logmult':
##
## assocdata("criminal",package="logmult")
criminal## Age
## Year 15 16 17 18 19
## 1955 141 285 320 441 427
## 1956 144 292 342 441 396
## 1957 196 380 424 462 427
## 1958 212 424 399 442 430loglm(Year~Age,data = criminal)## Call:
## loglm(formula = Year ~ Age, data = criminal)
##
## Statistics:
## X^2 df P(> X^2)
## Likelihood Ratio 84.14370 15 1.210687e-11
## Pearson 84.29411 15 1.135747e-11Answer: The p value is very small, and the cha square value is 84.14. There is association between Year and Age. And we can see, the as the year goes, the criminal age gradually drops.
mosaic(criminal, shade=TRUE)
mosaic(criminal,gp=shading_Friendly)
Answer: from the graph, we can see, the Year and Age have assciation. In the 1955, age 19 have the most impact. And when 1958. the age goes down to 16 has the most impact.
Exercise 5.9 a.
library(vcdExtra)## Warning: package 'vcdExtra' was built under R version 3.5.3data("Accident",package = "vcdExtra")
str(Accident,vec.len=12)## 'data.frame': 80 obs. of 5 variables:
## $ age : Ord.factor w/ 5 levels "0-9"<"10-19"<..: 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 ...
## $ result: Factor w/ 2 levels "Died","Injured": 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 2 2 ...
## $ mode : Factor w/ 4 levels "4-Wheeled","Bicycle",..: 4 4 2 2 3 3 1 1 4 4 2 2 3 3 1 1 4 4 2 2 3 3 1 1 4 4 2 2 3 3 ...
## $ gender: Factor w/ 2 levels "Female","Male": 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 ...
## $ Freq : int 704 378 396 56 742 78 513 253 5206 5449 3863 1030 8597 1387 7423 5552 223 49 146 24 889 98 720 199 3178 1814 3024| __truncated__ ...Accident## age result mode gender Freq
## 1 50+ Died Pedestrian Male 704
## 2 50+ Died Pedestrian Female 378
## 3 50+ Died Bicycle Male 396
## 4 50+ Died Bicycle Female 56
## 5 50+ Died Motorcycle Male 742
## 6 50+ Died Motorcycle Female 78
## 7 50+ Died 4-Wheeled Male 513
## 8 50+ Died 4-Wheeled Female 253
## 9 50+ Injured Pedestrian Male 5206
## 10 50+ Injured Pedestrian Female 5449
## 11 50+ Injured Bicycle Male 3863
## 12 50+ Injured Bicycle Female 1030
## 13 50+ Injured Motorcycle Male 8597
## 14 50+ Injured Motorcycle Female 1387
## 15 50+ Injured 4-Wheeled Male 7423
## 16 50+ Injured 4-Wheeled Female 5552
## 17 30-49 Died Pedestrian Male 223
## 18 30-49 Died Pedestrian Female 49
## 19 30-49 Died Bicycle Male 146
## 20 30-49 Died Bicycle Female 24
## 21 30-49 Died Motorcycle Male 889
## 22 30-49 Died Motorcycle Female 98
## 23 30-49 Died 4-Wheeled Male 720
## 24 30-49 Died 4-Wheeled Female 199
## 25 30-49 Injured Pedestrian Male 3178
## 26 30-49 Injured Pedestrian Female 1814
## 27 30-49 Injured Bicycle Male 3024
## 28 30-49 Injured Bicycle Female 1118
## 29 30-49 Injured Motorcycle Male 18909
## 30 30-49 Injured Motorcycle Female 3664
## 31 30-49 Injured 4-Wheeled Male 15086
## 32 30-49 Injured 4-Wheeled Female 7712
## 33 20-29 Died Pedestrian Male 78
## 34 20-29 Died Pedestrian Female 24
## 35 20-29 Died Bicycle Male 55
## 36 20-29 Died Bicycle Female 10
## 37 20-29 Died Motorcycle Male 660
## 38 20-29 Died Motorcycle Female 82
## 39 20-29 Died 4-Wheeled Male 353
## 40 20-29 Died 4-Wheeled Female 107
## 41 20-29 Injured Pedestrian Male 1521
## 42 20-29 Injured Pedestrian Female 864
## 43 20-29 Injured Bicycle Male 1565
## 44 20-29 Injured Bicycle Female 609
## 45 20-29 Injured Motorcycle Male 18558
## 46 20-29 Injured Motorcycle Female 4010
## 47 20-29 Injured 4-Wheeled Male 9084
## 48 20-29 Injured 4-Wheeled Female 4361
## 49 10-19 Died Pedestrian Male 70
## 50 10-19 Died Pedestrian Female 28
## 51 10-19 Died Bicycle Male 76
## 52 10-19 Died Bicycle Female 31
## 53 10-19 Died Motorcycle Male 362
## 54 10-19 Died Motorcycle Female 54
## 55 10-19 Died 4-Wheeled Male 150
## 56 10-19 Died 4-Wheeled Female 61
## 57 10-19 Injured Pedestrian Male 1827
## 58 10-19 Injured Pedestrian Female 1495
## 59 10-19 Injured Bicycle Male 3407
## 60 10-19 Injured Bicycle Female 7218
## 61 10-19 Injured Motorcycle Male 12311
## 62 10-19 Injured Motorcycle Female 3587
## 63 10-19 Injured 4-Wheeled Male 3543
## 64 10-19 Injured 4-Wheeled Female 2593
## 65 0-9 Died Pedestrian Male 150
## 66 0-9 Died Pedestrian Female 89
## 67 0-9 Died Bicycle Male 26
## 68 0-9 Died Bicycle Female 5
## 69 0-9 Died Motorcycle Male 6
## 70 0-9 Died Motorcycle Female 6
## 71 0-9 Died 4-Wheeled Male 70
## 72 0-9 Died 4-Wheeled Female 65
## 73 0-9 Injured Pedestrian Male 3341
## 74 0-9 Injured Pedestrian Female 1967
## 75 0-9 Injured Bicycle Male 378
## 76 0-9 Injured Bicycle Female 126
## 77 0-9 Injured Motorcycle Male 181
## 78 0-9 Injured Motorcycle Female 131
## 79 0-9 Injured 4-Wheeled Male 1593
## 80 0-9 Injured 4-Wheeled Female 1362mod1=loglm(Freq~age+mode+gender+result,data = Accident)abbrev <- list(abbreviate=c(FALSE, FALSE, 1))
mosaic(mod1,labeling_args=abbrev)
mod2=loglm(Freq~mode+gender+result+age,data = Accident)
mosaic(mod2,labeling_args=abbrev)
mod3=loglm(Freq~gender+result+age+mode,data = Accident)
mosaic(mod3,labeling_args=abbrev)
mod4=loglm(Freq~age*mode*gender+result,data = Accident)mosaic(mod4,labeling_args=abbrev)
Answer: From the graph, we can tell the 50+ year old male pedestrian is more likely to result in death in a traffic accident.