Assignment 6

Load the libraries + functions

Load all the libraries or functions that you will use to for the rest of the assignment. It is helpful to define your libraries and functions at the top of a report, so that others can know what they need for the report to compile correctly.

The data for this project has already been loaded. You will be distinguishing between the categories of nerd and geek to determine the influence of respective variables on their category definition.

library(Rling)
library(party)

## Loading required package: grid

## Loading required package: mvtnorm

## Loading required package: modeltools

## Loading required package: stats4

## Loading required package: strucchange

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: sandwich

data(nerd)
head(nerd)

##   Noun Num Century Register    Eval
## 1 nerd  pl      XX     ACAD Neutral
## 2 nerd  pl     XXI     ACAD Neutral
## 3 nerd  pl      XX     ACAD Neutral
## 4 nerd  pl      XX     ACAD Neutral
## 5 nerd  pl      XX     ACAD Neutral
## 6 nerd  pl     XXI     ACAD Neutral

table(nerd$Noun)

## 
## geek nerd 
##  670  646

Description of the data

Dependent variable:

• Noun: which category is represented either nerd or geek.

Independent variables:

• Num: a measure of social group, either pl (plural) or sg (single)
• Century: time measurement, as XX (20th) or XXI (21st) century
• Register: information about where the data was coded from ACAD (academic), MAG (magazine), NEWS (newspapers), and SPOK (spoken)
• Eval: A measure of the semanticity of the word, Neg for negative, Neutral, and Positive

Conditional inference model

• Add a random number generator to start the model.
• Use ctree() to create a conditional inference model.

set.seed(12345)
tree.output = ctree(Noun ~ Num + Century + Register + Eval, data = nerd)

Make a plot

• Plot the conditional inference model.

plot(tree.output)

Interpret the categories

• Write out an interpretation of the results from the model. You can interpret the branches of the tree to determine what featurally defines each category.
• With only two categories, you will see the proportion split as the output in the bar graph - look for the group with the larger proportion.

Conditional inference model predictiveness

• Calculate the percent correct classification for the conditional inference model.

outcomes = table(predict(tree.output), nerd$Noun)
outcomes

##       
##        geek nerd
##   geek  227   61
##   nerd  443  585

sum(diag(outcomes)) / sum(outcomes) * 100

## [1] 61.70213

sum(outcomes[1]) / sum(outcomes[,1]) * 100

## [1] 33.8806

sum(outcomes[4]) / sum(outcomes[,2]) * 100

## [1] 90.55728

sum(outcomes[,1]) / (sum(outcomes[,1]) + sum(outcomes[,2]))

## [1] 0.5091185

sum(outcomes[1,]) / (sum(outcomes[1,]) + sum(outcomes[2,]))

## [1] 0.218845

Variable importance

• Calculate the variable importance from the random forest model.
• Include a dot plot of the importance values.
• Which variables were the most important?

Thought question

• What other variables might be useful in understanding the category membership of geek versus nerd? Basically, what could we add to the model to improve it (there’s no one right answer here - it’s helpful to think about what other facets we have not measured)?

The meanings of word Geek and Nerd and Interchangeable to a lot of researchers and common people.Hence it is important to what Nerd and Geek means, a clearer definition might help remove any ambiguity.