Assignment 3

library(HistData)
library(vcdExtra)
## Loading required package: vcd
## Loading required package: grid
## Loading required package: gnm
library(plotly)
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:vcdExtra':
## 
##     summarise
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
data("Abortion", package="vcdExtra")
str(Abortion)
##  table [1:2, 1:2, 1:2] 171 152 138 167 79 148 112 133
##  - attr(*, "dimnames")=List of 3
##   ..$ Sex             : chr [1:2] "Female" "Male"
##   ..$ Status          : chr [1:2] "Lo" "Hi"
##   ..$ Support_Abortion: chr [1:2] "Yes" "No"

(a) Taking support for abortion as the outcome variable, produce fourfold displays

showing the association with sex, stratified by status.

fourfold(Abortion,c(3,1,2))

(b)Do the same for the association of support for abortion with status, stratified by sex.

fourfold(Abortion, 3:1)

(c) For each of the problems above, use oddsratio () to calculate the numerical values of the odds ratio, as stratified in the question.

oddsratio(Abortion, log = FALSE)
##  odds ratios for Sex and Status by Support_Abortion 
## 
##       Yes        No 
## 1.3614130 0.6338682
oddsratio(Abortion, stratum="Sex", log=FALSE)
##  odds ratios for Sex and Status by Support_Abortion 
## 
##    Female      Male 
## 1.3614130 0.6338682
oddsratio(Abortion, stratum="Status", log=FALSE)
##  odds ratios for Sex and Status by Support_Abortion 
## 
##        Lo        Hi 
## 1.3614130 0.6338682

(d) Write a brief summary of how support for abortion depends on sex and status.

When compared to Hi status females, higher Lo status females support abortion. Hi Status Male is more likely to support abortion than Lo status Male.

Exercise 4.7 Agresti and Winner (1997) gave the data in below on the ratings of 160 movies by the reviewers Gene Siskel and Roger Ebert for the period from April 1995 through September 1996. The rating categories were Con (“thumbs down”), Mixed, and Pro (“thumbs up”).

siskel = matrix(c(24,8,13,45,8,13,11,32,10,9,64,83,42,30,88,160), ncol=4, byrow=TRUE)
colnames(siskel) = c("Con","Mixed","Pro","Total")
rownames(siskel) = c("Con","Mixed","Pro","Total")
siskel = as.table(siskel)
siskel
##       Con Mixed Pro Total
## Con    24     8  13    45
## Mixed   8    13  11    32
## Pro    10     9  64    83
## Total  42    30  88   160

(a) Assess the strength of agreement between the raters using Cohen’s ??, both unweighted and weighted.

Kappa(siskel)
##              value     ASE     z Pr(>|z|)
## Unweighted 0.09012 0.02882 3.127 0.001767
## Weighted   0.08737 0.03372 2.591 0.009573

(b) Use agreementplot () for a graphical display of agreement here.

agreementplot(siskel,main="Unweighted",weights=1)

agreementplot(siskel,main="Weighted")