ANLY545 HW5
Raavi Anvesh
October 28, 2018
6.1
a
library(vcdExtra)## Loading required package: vcd## Loading required package: grid## Loading required package: gnmlibrary(ca)
str(JobSat)## 'table' num [1:4, 1:4] 1 2 1 0 3 3 6 1 10 10 ...
## - attr(*, "dimnames")=List of 2
## ..$ income : chr [1:4] "< 15k" "15-25k" "25-40k" "> 40k"
## ..$ satisfaction: chr [1:4] "VeryD" "LittleD" "ModerateS" "VeryS"(JobSat.ca <- ca(JobSat))##
## Principal inertias (eigenvalues):
## 1 2 3
## Value 0.047496 0.012248 0.002397
## Percentage 76.43% 19.71% 3.86%
##
##
## Rows:
## < 15k 15-25k 25-40k > 40k
## Mass 0.208333 0.229167 0.343750 0.218750
## ChiDist 0.155863 0.258668 0.143508 0.398092
## Inertia 0.005061 0.015333 0.007079 0.034667
## Dim. 1 -0.580000 -0.954935 -0.160048 1.804292
## Dim. 2 0.037584 -1.341216 1.228320 -0.560927
##
##
## Columns:
## VeryD LittleD ModerateS VeryS
## Mass 0.041667 0.135417 0.447917 0.375000
## ChiDist 0.756787 0.367072 0.065080 0.219901
## Inertia 0.023864 0.018246 0.001897 0.018134
## Dim. 1 -3.039806 -1.327426 -0.144206 0.989351
## Dim. 2 -3.199834 2.014331 -0.186638 -0.148932For a one dimensional solution inertia is 0.0474
b
plot(JobSat.ca)
Dimention 1 is ordered by job satisfaction. 25-40K income groups are less dissatisfied and 15-25K are very much dissatisfied with their job.
6.11
a
library(vcdExtra)
accident.tab <- xtabs(Freq ~ gender + mode + age+ result, data=Accident)
accident.tab## , , age = 0-9, result = Died
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 65 5 6 89
## Male 70 26 6 150
##
## , , age = 10-19, result = Died
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 61 31 54 28
## Male 150 76 362 70
##
## , , age = 20-29, result = Died
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 107 10 82 24
## Male 353 55 660 78
##
## , , age = 30-49, result = Died
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 199 24 98 49
## Male 720 146 889 223
##
## , , age = 50+, result = Died
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 253 56 78 378
## Male 513 396 742 704
##
## , , age = 0-9, result = Injured
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 1362 126 131 1967
## Male 1593 378 181 3341
##
## , , age = 10-19, result = Injured
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 2593 7218 3587 1495
## Male 3543 3407 12311 1827
##
## , , age = 20-29, result = Injured
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 4361 609 4010 864
## Male 9084 1565 18558 1521
##
## , , age = 30-49, result = Injured
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 7712 1118 3664 1814
## Male 15086 3024 18909 3178
##
## , , age = 50+, result = Injured
##
## mode
## gender 4-Wheeled Bicycle Motorcycle Pedestrian
## Female 5552 1030 1387 5449
## Male 7423 3863 8597 5206accident.mjca <- mjca(accident.tab)
summary(accident.mjca)##
## Principal inertias (eigenvalues):
##
## dim value % cum% scree plot
## 1 0.025429 46.5 46.5 ****************
## 2 0.011848 21.7 68.1 *******
## 3 0.001889 3.5 71.6 *
## 4 0.000491 0.9 72.5
## -------- -----
## Total: 0.054700
##
##
## Columns:
## name mass qlt inr k=1 cor ctr k=2 cor ctr
## 1 | gender:Female | 77 788 77 | -203 686 126 | 78 101 40 |
## 2 | gender:Male | 173 788 35 | 91 686 56 | -35 101 18 |
## 3 | mode:4-Wheeled | 81 230 73 | -8 2 0 | -80 228 43 |
## 4 | mode:Bicycle | 31 762 98 | -156 127 30 | 349 635 320 |
## 5 | mode:Motorcycle | 99 686 70 | 209 684 170 | 11 2 1 |
## 6 | mode:Pedestrian | 38 677 100 | -401 600 241 | -144 77 66 |
## 7 | age:0-9 | 13 672 107 | -551 561 152 | -246 111 65 |
## 8 | age:10-19 | 49 678 91 | -40 13 3 | 292 665 354 |
## 9 | age:20-29 | 56 784 85 | 215 747 102 | -48 37 11 |
## 10 | age:30-49 | 76 546 75 | 103 396 32 | -63 149 26 |
## 11 | age:50+ | 56 687 85 | -196 616 84 | -67 72 21 |
## 12 | result:Died | 11 515 100 | -90 92 3 | -192 422 34 |
## 13 | result:Injured | 239 515 5 | 4 92 0 | 9 422 2 |b
plot(accident.mjca)
People with age groups 30-49 and with 4 wheeled vehicles are the people wiht more number of reported deaths.