IST707 Homework 5

Load Packages

library(rpart)
library(caret)

## Loading required package: ggplot2

## Loading required package: lattice

library(arules)

## Loading required package: Matrix

## 
## Attaching package: 'arules'

## The following objects are masked from 'package:base':
## 
##     abbreviate, write

library(rattle)

## Loading required package: tibble

## Loading required package: bitops

## Rattle: A free graphical interface for data science with R.
## Version 5.4.0 Copyright (c) 2006-2020 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.

Set working directory & load in data

setwd("/Users/emma/Documents/SYRACUSE/IST707/Week 4")
data <- read.csv("fedPapers85.csv")

str(data)

## 'data.frame':    85 obs. of  72 variables:
##  $ author  : chr  "dispt" "dispt" "dispt" "dispt" ...
##  $ filename: chr  "dispt_fed_49.txt" "dispt_fed_50.txt" "dispt_fed_51.txt" "dispt_fed_52.txt" ...
##  $ a       : num  0.28 0.177 0.339 0.27 0.303 0.245 0.349 0.414 0.248 0.442 ...
##  $ all     : num  0.052 0.063 0.09 0.024 0.054 0.059 0.036 0.083 0.04 0.062 ...
##  $ also    : num  0.009 0.013 0.008 0.016 0.027 0.007 0.007 0.009 0.007 0.006 ...
##  $ an      : num  0.096 0.038 0.03 0.024 0.034 0.067 0.029 0.018 0.04 0.075 ...
##  $ and     : num  0.358 0.393 0.301 0.262 0.404 0.282 0.335 0.478 0.356 0.423 ...
##  $ any     : num  0.026 0.063 0.008 0.056 0.04 0.052 0.058 0.046 0.034 0.037 ...
##  $ are     : num  0.131 0.051 0.068 0.064 0.128 0.111 0.087 0.11 0.154 0.093 ...
##  $ as      : num  0.122 0.139 0.203 0.111 0.148 0.252 0.073 0.074 0.161 0.1 ...
##  $ at      : num  0.017 0.114 0.023 0.056 0.013 0.015 0.116 0.037 0.047 0.031 ...
##  $ be      : num  0.411 0.393 0.474 0.365 0.344 0.297 0.378 0.331 0.289 0.379 ...
##  $ been    : num  0.026 0.165 0.015 0.127 0.047 0.03 0.044 0.046 0.027 0.025 ...
##  $ but     : num  0.009 0 0.038 0.032 0.061 0.037 0.007 0.055 0.027 0.037 ...
##  $ by      : num  0.14 0.139 0.173 0.167 0.209 0.186 0.102 0.092 0.168 0.174 ...
##  $ can     : num  0.035 0 0.023 0.056 0.088 0 0.058 0.037 0.047 0.056 ...
##  $ do      : num  0.026 0.013 0 0 0 0 0.015 0.028 0 0 ...
##  $ down    : num  0 0 0.008 0 0 0.007 0 0 0 0 ...
##  $ even    : num  0.009 0.025 0.015 0.024 0.02 0.007 0.007 0.018 0 0.006 ...
##  $ every   : num  0.044 0 0.023 0.04 0.027 0.007 0.087 0.064 0.081 0.05 ...
##  $ for.    : num  0.096 0.076 0.098 0.103 0.141 0.067 0.116 0.055 0.127 0.1 ...
##  $ from    : num  0.044 0.101 0.053 0.079 0.074 0.096 0.08 0.083 0.074 0.124 ...
##  $ had     : num  0.035 0.101 0.008 0.016 0 0.022 0.015 0.009 0.007 0 ...
##  $ has     : num  0.017 0.013 0.015 0.024 0.054 0.015 0.036 0.037 0.02 0.019 ...
##  $ have    : num  0.044 0.152 0.023 0.143 0.047 0.119 0.044 0.074 0.074 0.044 ...
##  $ her     : num  0 0 0 0 0 0 0.007 0 0.034 0.025 ...
##  $ his     : num  0.017 0 0 0.024 0.02 0.067 0 0.018 0.02 0.05 ...
##  $ if.     : num  0 0.025 0.023 0.04 0.034 0.03 0.029 0 0 0.025 ...
##  $ in.     : num  0.262 0.291 0.308 0.238 0.263 0.401 0.189 0.267 0.248 0.274 ...
##  $ into    : num  0.009 0.025 0.038 0.008 0.013 0.037 0 0.037 0.013 0.037 ...
##  $ is      : num  0.157 0.038 0.15 0.151 0.189 0.26 0.167 0.083 0.208 0.23 ...
##  $ it      : num  0.175 0.127 0.173 0.222 0.108 0.156 0.102 0.165 0.134 0.131 ...
##  $ its     : num  0.07 0.038 0.03 0.048 0.013 0.015 0 0.046 0.02 0.019 ...
##  $ may     : num  0.035 0.038 0.12 0.056 0.047 0.074 0.08 0.092 0.027 0.106 ...
##  $ more    : num  0.026 0 0.038 0.056 0.067 0.045 0.08 0.064 0.06 0.081 ...
##  $ must    : num  0.026 0.013 0.083 0.071 0.013 0.015 0.044 0.018 0.027 0.068 ...
##  $ my      : num  0 0 0 0 0 0 0.007 0 0 0 ...
##  $ no      : num  0.035 0 0.03 0.032 0.047 0.059 0.022 0.018 0.02 0.044 ...
##  $ not     : num  0.114 0.127 0.068 0.087 0.128 0.134 0.102 0.101 0.094 0.106 ...
##  $ now     : num  0 0 0 0 0 0 0.007 0 0.007 0.012 ...
##  $ of      : num  0.9 0.747 0.858 0.802 0.869 ...
##  $ on      : num  0.14 0.139 0.15 0.143 0.054 0.141 0.051 0.083 0.127 0.118 ...
##  $ one     : num  0.026 0.025 0.03 0.032 0.047 0.052 0.073 0.046 0.06 0.031 ...
##  $ only    : num  0.035 0 0.023 0.048 0.027 0.022 0.007 0.046 0.02 0.012 ...
##  $ or      : num  0.096 0.114 0.06 0.064 0.081 0.074 0.153 0.037 0.154 0.081 ...
##  $ our     : num  0.017 0 0 0.016 0.027 0.03 0.051 0 0.007 0.025 ...
##  $ shall   : num  0.017 0 0.008 0.016 0 0.015 0.007 0 0.02 0 ...
##  $ should  : num  0.017 0.013 0.068 0.032 0 0.03 0.007 0 0 0.012 ...
##  $ so      : num  0.035 0.013 0.038 0.04 0.027 0.007 0.051 0.018 0.04 0.05 ...
##  $ some    : num  0.009 0.063 0.03 0.024 0.067 0.045 0.007 0.028 0.027 0.025 ...
##  $ such    : num  0.026 0 0.045 0.008 0.027 0.015 0.015 0 0.013 0.031 ...
##  $ than    : num  0.009 0 0.023 0 0.047 0.03 0.109 0.055 0.067 0.044 ...
##  $ that    : num  0.184 0.152 0.188 0.238 0.162 0.208 0.233 0.165 0.208 0.218 ...
##  $ the     : num  1.43 1.25 1.49 1.33 1.19 ...
##  $ their   : num  0.114 0.165 0.053 0.071 0.027 0.089 0.109 0.083 0.154 0.081 ...
##  $ then    : num  0 0 0.015 0.008 0.007 0.007 0.015 0.009 0.007 0.012 ...
##  $ there   : num  0.009 0 0.015 0 0.007 0.007 0.036 0.028 0.02 0 ...
##  $ things  : num  0.009 0 0 0 0 0 0 0 0 0.012 ...
##  $ this    : num  0.044 0.051 0.075 0.103 0.094 0.126 0.08 0.11 0.067 0.093 ...
##  $ to      : num  0.507 0.355 0.361 0.532 0.485 0.445 0.56 0.34 0.49 0.498 ...
##  $ up      : num  0 0 0 0 0 0 0.007 0 0 0 ...
##  $ upon    : num  0 0.013 0 0 0 0 0 0 0 0 ...
##  $ was     : num  0.009 0.051 0.008 0.087 0.027 0.007 0.015 0.018 0.027 0 ...
##  $ were    : num  0.017 0 0.015 0.079 0.02 0.03 0.029 0.009 0.007 0 ...
##  $ what    : num  0 0 0.008 0.008 0.02 0.015 0.015 0.009 0.02 0.025 ...
##  $ when    : num  0.009 0 0 0.024 0.007 0.037 0.007 0 0.02 0.012 ...
##  $ which   : num  0.175 0.114 0.105 0.167 0.155 0.186 0.211 0.175 0.201 0.199 ...
##  $ who     : num  0.044 0.038 0.008 0 0.027 0.045 0.022 0.018 0.04 0.031 ...
##  $ will    : num  0.009 0.089 0.173 0.079 0.168 0.111 0.145 0.267 0.154 0.106 ...
##  $ with    : num  0.087 0.063 0.045 0.079 0.074 0.089 0.073 0.129 0.027 0.081 ...
##  $ would   : num  0.192 0.139 0.068 0.064 0.04 0.037 0.073 0.037 0.04 0.031 ...
##  $ your    : num  0 0 0 0 0 0 0 0 0 0 ...

clean_data <- data[,c(-1,-2)]

Discretize values

disc_papers<- discretizeDF(clean_data, default= list(method= "interval", breaks=30))

disc_papers$author <- data$author
str(disc_papers)

## 'data.frame':    85 obs. of  71 variables:
##  $ a     : Factor w/ 30 levels "[0.096,0.108)",..: 15 7 20 15 17 13 21 26 13 29 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.096 0.108 0.121 0.133 0.145 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ all   : Factor w/ 30 levels "[0.015,0.0187)",..: 10 13 21 3 11 12 6 19 7 13 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.015 0.0187 0.0225 0.0262 0.0299 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ also  : Factor w/ 30 levels "[0,0.00157)",..: 6 9 6 11 18 5 5 6 5 4 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00157 0.00313 0.0047 0.00627 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ an    : Factor w/ 30 levels "[0.009,0.0147)",..: 16 6 4 3 5 11 4 2 6 12 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.009 0.0147 0.0203 0.026 0.0317 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ and   : Factor w/ 30 levels "[0.217,0.237)",..: 8 9 5 3 10 4 6 13 7 11 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.217 0.237 0.257 0.277 0.298 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ any   : Factor w/ 30 levels "[0,0.0038)","[0.0038,0.0076)",..: 7 17 3 15 11 14 16 13 9 10 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0038 0.0076 0.0114 0.0152 0.019 0.0228 0.0266 0.0304 0.0342 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ are   : Factor w/ 30 levels "[0.013,0.018)",..: 24 8 12 11 24 20 15 20 29 17 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.013 0.018 0.023 0.028 0.033 0.038 0.043 0.048 0.053 0.058 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ as    : Factor w/ 30 levels "[0.027,0.0345)",..: 13 15 24 12 17 30 7 7 18 10 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.027 0.0345 0.042 0.0495 0.057 0.0645 0.072 0.0795 0.087 0.0945 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ at    : Factor w/ 30 levels "[0,0.00393)",..: 5 29 6 15 4 4 30 10 12 8 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00393 0.00787 0.0118 0.01573 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ be    : Factor w/ 30 levels "[0.04,0.0547)",..: 26 25 30 23 21 18 23 20 17 24 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.04 0.0547 0.0694 0.0841 0.0988 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ been  : Factor w/ 30 levels "[0,0.0055)","[0.0055,0.011)",..: 5 30 3 24 9 6 8 9 5 5 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0055 0.011 0.0165 0.022 0.0275 0.033 0.0385 0.044 0.0495 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ but   : Factor w/ 30 levels "[0,0.00297)",..: 4 1 13 11 21 13 3 19 10 13 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00297 0.00593 0.0089 0.01187 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ by    : Factor w/ 30 levels "[0.027,0.0349)",..: 15 15 19 18 24 21 10 9 18 19 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.027 0.0349 0.0428 0.0507 0.0586 0.0665 0.0744 0.0823 0.0902 0.0981 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ can   : Factor w/ 30 levels "[0,0.00367)",..: 10 1 7 16 25 1 16 11 13 16 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00367 0.00733 0.011 0.01467 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ do    : Factor w/ 30 levels "[0,0.000933)",..: 28 14 1 1 1 1 17 30 1 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.000933 0.001867 0.0028 0.003733 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ down  : Factor w/ 30 levels "[0,0.000567)",..: 1 1 15 1 1 13 1 1 1 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.000567 0.001133 0.0017 0.002267 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ even  : Factor w/ 30 levels "[0,0.00123)",..: 8 21 13 20 17 6 6 15 1 5 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00123 0.00247 0.0037 0.00493 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ every : Factor w/ 30 levels "[0,0.0029)","[0.0029,0.0058)",..: 16 1 8 14 10 3 30 23 28 18 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0029 0.0058 0.0087 0.0116 0.0145 0.0174 0.0203 0.0232 0.0261 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ for.  : Factor w/ 30 levels "[0.03,0.0361)",..: 11 8 12 12 19 7 15 5 16 12 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.03 0.0361 0.0422 0.0483 0.0544 0.0605 0.0666 0.0727 0.0788 0.0849 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ from  : Factor w/ 30 levels "[0.026,0.0305)",..: 4 17 6 12 11 16 12 13 11 22 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.026 0.0305 0.0351 0.0396 0.0441 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ had   : Factor w/ 30 levels "[0,0.0047)","[0.0047,0.0094)",..: 8 22 2 4 1 5 4 2 2 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0047 0.0094 0.0141 0.0188 0.0235 0.0282 0.0329 0.0376 0.0423 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ has   : Factor w/ 30 levels "[0,0.0038)","[0.0038,0.0076)",..: 5 4 4 7 15 4 10 10 6 6 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0038 0.0076 0.0114 0.0152 0.019 0.0228 0.0266 0.0304 0.0342 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ have  : Factor w/ 30 levels "[0.011,0.0168)",..: 6 25 3 23 7 19 6 11 11 6 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.011 0.0168 0.0226 0.0284 0.0342 0.04 0.0458 0.0516 0.0574 0.0632 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ her   : Factor w/ 30 levels "[0,0.005)","[0.005,0.01)",..: 1 1 1 1 1 1 2 1 7 6 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ his   : Factor w/ 30 levels "[0,0.00823)",..: 3 1 1 3 3 9 1 3 3 7 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00823 0.01647 0.0247 0.03293 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ if.   : Factor w/ 30 levels "[0,0.0033)","[0.0033,0.0066)",..: 1 8 7 13 11 10 9 1 1 8 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0033 0.0066 0.0099 0.0132 0.0165 0.0198 0.0231 0.0264 0.0297 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ in.   : Factor w/ 30 levels "[0.189,0.199)",..: 8 10 12 5 8 21 1 8 6 9 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.189 0.199 0.21 0.22 0.23 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ into  : Factor w/ 30 levels "[0,0.0035)","[0.0035,0.007)",..: 3 8 11 3 4 11 1 11 4 11 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0035 0.007 0.0105 0.014 0.0175 0.021 0.0245 0.028 0.0315 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ is    : Factor w/ 30 levels "[0.028,0.0378)",..: 14 2 13 13 17 24 15 6 19 21 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.028 0.0378 0.0477 0.0575 0.0673 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ it    : Factor w/ 30 levels "[0.075,0.082)",..: 15 8 15 22 5 12 4 13 9 9 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.075 0.082 0.0889 0.0959 0.1029 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ its   : Factor w/ 30 levels "[0,0.005)","[0.005,0.01)",..: 15 8 7 10 3 4 1 10 5 4 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ may   : Factor w/ 30 levels "[0,0.00447)",..: 8 9 27 13 11 17 18 21 7 24 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00447 0.00893 0.0134 0.01787 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ more  : Factor w/ 30 levels "[0,0.00433)",..: 7 1 9 13 16 11 19 15 14 19 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00433 0.00867 0.013 0.01733 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ must  : Factor w/ 30 levels "[0,0.0037)","[0.0037,0.0074)",..: 8 4 23 20 4 5 12 5 8 19 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0037 0.0074 0.0111 0.0148 0.0185 0.0222 0.0259 0.0296 0.0333 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ my    : Factor w/ 30 levels "[0,0.00187)",..: 1 1 1 1 1 1 4 1 1 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00187 0.00373 0.0056 0.00747 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ no    : Factor w/ 30 levels "[0,0.00277)",..: 13 1 11 12 17 22 8 7 8 16 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00277 0.00553 0.0083 0.01107 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ not   : Factor w/ 30 levels "[0.02,0.0243)",..: 23 26 12 16 26 27 20 19 18 21 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.02 0.0243 0.0285 0.0328 0.0371 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ now   : Factor w/ 30 levels "[0,0.000867)",..: 1 1 1 1 1 1 9 1 9 14 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.000867 0.001733 0.0026 0.003467 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ of    : Factor w/ 30 levels "[0.562,0.584)",..: 16 9 14 12 15 15 16 22 22 16 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.562 0.584 0.605 0.627 0.649 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ on    : Factor w/ 30 levels "[0,0.0052)","[0.0052,0.0104)",..: 27 27 29 28 11 28 10 16 25 23 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0052 0.0104 0.0156 0.0208 0.026 0.0312 0.0364 0.0416 0.0468 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ one   : Factor w/ 30 levels "[0.005,0.00933)",..: 5 5 6 7 10 11 16 10 13 7 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.005 0.00933 0.01367 0.018 0.02233 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ only  : Factor w/ 30 levels "[0,0.00217)",..: 17 1 11 23 13 11 4 22 10 6 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00217 0.00433 0.0065 0.00867 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ or    : Factor w/ 30 levels "[0.027,0.0368)",..: 8 9 4 4 6 5 13 2 13 6 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.027 0.0368 0.0466 0.0564 0.0662 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ our   : Factor w/ 30 levels "[0,0.00663)",..: 3 1 1 3 5 5 8 1 2 4 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00663 0.01327 0.0199 0.02653 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ shall : Factor w/ 30 levels "[0,0.00263)",..: 7 1 4 7 1 6 3 1 8 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00263 0.00527 0.0079 0.01053 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ should: Factor w/ 30 levels "[0,0.00303)",..: 6 5 23 11 1 10 3 1 1 4 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00303 0.00607 0.0091 0.01213 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ so    : Factor w/ 30 levels "[0,0.0024)","[0.0024,0.0048)",..: 15 6 16 17 12 3 22 8 17 21 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0024 0.0048 0.0072 0.0096 0.012 0.0144 0.0168 0.0192 0.0216 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ some  : Factor w/ 30 levels "[0,0.00223)",..: 5 29 14 11 30 21 4 13 13 12 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00223 0.00447 0.0067 0.00893 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ such  : Factor w/ 30 levels "[0,0.00283)",..: 10 1 16 3 10 6 6 1 5 11 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00283 0.00567 0.0085 0.01133 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ than  : Factor w/ 30 levels "[0,0.005)","[0.005,0.01)",..: 2 1 5 1 10 7 22 12 14 9 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ that  : Factor w/ 30 levels "[0.081,0.091)",..: 11 8 11 16 9 13 16 9 13 14 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.081 0.091 0.101 0.111 0.121 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ the   : Factor w/ 30 levels "[0.669,0.707)",..: 21 16 22 18 14 22 16 14 20 12 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.669 0.707 0.745 0.782 0.82 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ their : Factor w/ 30 levels "[0.005,0.0109)",..: 19 27 9 12 4 15 18 14 26 13 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.005 0.0109 0.0169 0.0228 0.0287 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ then  : Factor w/ 30 levels "[0,0.0007)","[0.0007,0.0014)",..: 1 1 22 12 11 11 22 13 11 18 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0007 0.0014 0.0021 0.0028 0.0035 0.0042 0.0049 0.0056 0.0063 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ there : Factor w/ 30 levels "[0,0.0035)","[0.0035,0.007)",..: 3 1 5 1 3 3 11 9 6 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0035 0.007 0.0105 0.014 0.0175 0.021 0.0245 0.028 0.0315 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ things: Factor w/ 30 levels "[0,0.0005)","[0.0005,0.001)",..: 18 1 1 1 1 1 1 1 1 25 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0005 0.001 0.0015 0.002 0.0025 0.003 0.0035 0.004 0.0045 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ this  : Factor w/ 30 levels "[0.009,0.0138)",..: 8 9 14 20 18 25 15 22 13 18 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.009 0.0138 0.0186 0.0234 0.0282 0.033 0.0378 0.0426 0.0474 0.0522 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ to    : Factor w/ 30 levels "[0.333,0.348)",..: 12 2 2 14 11 8 16 1 11 12 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.333 0.348 0.363 0.377 0.392 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ up    : Factor w/ 30 levels "[0,0.00107)",..: 1 1 1 1 1 1 7 1 1 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00107 0.00213 0.0032 0.00427 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ upon  : Factor w/ 30 levels "[0,0.0034)","[0.0034,0.0068)",..: 1 4 1 1 1 1 1 1 1 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0034 0.0068 0.0102 0.0136 0.017 0.0204 0.0238 0.0272 0.0306 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ was   : Factor w/ 30 levels "[0,0.0063)","[0.0063,0.0126)",..: 2 9 2 14 5 2 3 3 5 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0063 0.0126 0.0189 0.0252 0.0315 0.0378 0.0441 0.0504 0.0567 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ were  : Factor w/ 30 levels "[0,0.0036)","[0.0036,0.0072)",..: 5 1 5 22 6 9 9 3 2 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0036 0.0072 0.0108 0.0144 0.018 0.0216 0.0252 0.0288 0.0324 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ what  : Factor w/ 30 levels "[0,0.002)","[0.002,0.004)",..: 1 1 5 5 11 8 8 5 11 13 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ when  : Factor w/ 30 levels "[0,0.00243)",..: 4 1 1 10 3 16 3 1 9 5 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00243 0.00487 0.0073 0.00973 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ which : Factor w/ 30 levels "[0.081,0.0875)",..: 15 6 4 14 12 17 20 15 19 19 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.081 0.0875 0.094 0.1005 0.107 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ who   : Factor w/ 30 levels "[0,0.0043)","[0.0043,0.0086)",..: 11 9 2 1 7 11 6 5 10 8 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.0043 0.0086 0.0129 0.0172 0.0215 0.0258 0.0301 0.0344 0.0387 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ will  : Factor w/ 30 levels "[0.006,0.0171)",..: 1 8 15 7 15 10 13 24 14 9 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.006 0.0171 0.0283 0.0394 0.0505 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ with  : Factor w/ 30 levels "[0.027,0.0311)",..: 15 9 5 13 12 16 12 25 1 14 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.027 0.0311 0.0352 0.0393 0.0434 0.0475 0.0516 0.0557 0.0598 0.0639 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ would : Factor w/ 30 levels "[0.009,0.0214)",..: 15 11 5 5 3 3 6 3 3 2 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0.009 0.0214 0.0339 0.0463 0.0587 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ your  : Factor w/ 30 levels "[0,0.00247)",..: 1 1 1 1 1 1 1 1 1 1 ...
##   ..- attr(*, "discretized:breaks")= num [1:31] 0 0.00247 0.00493 0.0074 0.00987 ...
##   ..- attr(*, "discretized:method")= chr "interval"
##  $ author: chr  "dispt" "dispt" "dispt" "dispt" ...

Split testing & training info

test <- which(disc_papers$author=="dispt")
test

##  [1]  1  2  3  4  5  6  7  8  9 10 11

test_data <- disc_papers[test,]
train_data <- disc_papers[-test,]
train_data$author <- as.factor(train_data$author)

dim(test_data)

## [1] 11 71

dim(train_data)

## [1] 74 71

Train new tree

fed_tree<- train(author~., data= train_data, 
                 method="rpart",
                 trControl= trainControl(method="cv"))

fed_tree

## CART 
## 
## 74 samples
## 70 predictors
##  4 classes: 'Hamilton', 'HM', 'Jay', 'Madison' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 67, 67, 68, 66, 67, 66, ... 
## Resampling results across tuning parameters:
## 
##   cp         Accuracy   Kappa     
##   0.0000000  0.7505952  0.39768068
##   0.1304348  0.7791667  0.42195876
##   0.2608696  0.6946429  0.05943152
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.1304348.

summary(fed_tree$finalModel)

## Call:
## (function (formula, data, weights, subset, na.action = na.rpart, 
##     method, model = FALSE, x = FALSE, y = TRUE, parms, control, 
##     cost, ...) 
## {
##     Call <- match.call()
##     if (is.data.frame(model)) {
##         m <- model
##         model <- FALSE
##     }
##     else {
##         indx <- match(c("formula", "data", "weights", "subset"), 
##             names(Call), nomatch = 0)
##         if (indx[1] == 0) 
##             stop("a 'formula' argument is required")
##         temp <- Call[c(1, indx)]
##         temp$na.action <- na.action
##         temp[[1]] <- quote(stats::model.frame)
##         m <- eval.parent(temp)
##     }
##     Terms <- attr(m, "terms")
##     if (any(attr(Terms, "order") > 1)) 
##         stop("Trees cannot handle interaction terms")
##     Y <- model.response(m)
##     wt <- model.weights(m)
##     if (any(wt < 0)) 
##         stop("negative weights not allowed")
##     if (!length(wt)) 
##         wt <- rep(1, nrow(m))
##     offset <- model.offset(m)
##     X <- rpart.matrix(m)
##     nobs <- nrow(X)
##     nvar <- ncol(X)
##     if (missing(method)) {
##         method <- if (is.factor(Y) || is.character(Y)) 
##             "class"
##         else if (inherits(Y, "Surv")) 
##             "exp"
##         else if (is.matrix(Y)) 
##             "poisson"
##         else "anova"
##     }
##     if (is.list(method)) {
##         mlist <- method
##         method <- "user"
##         init <- if (missing(parms)) 
##             mlist$init(Y, offset, wt = wt)
##         else mlist$init(Y, offset, parms, wt)
##         keep <- rpartcallback(mlist, nobs, init)
##         method.int <- 4
##         parms <- init$parms
##     }
##     else {
##         method.int <- pmatch(method, c("anova", "poisson", "class", 
##             "exp"))
##         if (is.na(method.int)) 
##             stop("Invalid method")
##         method <- c("anova", "poisson", "class", "exp")[method.int]
##         if (method.int == 4) 
##             method.int <- 2
##         init <- if (missing(parms)) 
##             get(paste("rpart", method, sep = "."), envir = environment())(Y, 
##                 offset, , wt)
##         else get(paste("rpart", method, sep = "."), envir = environment())(Y, 
##             offset, parms, wt)
##         ns <- asNamespace("rpart")
##         if (!is.null(init$print)) 
##             environment(init$print) <- ns
##         if (!is.null(init$summary)) 
##             environment(init$summary) <- ns
##         if (!is.null(init$text)) 
##             environment(init$text) <- ns
##     }
##     Y <- init$y
##     xlevels <- .getXlevels(Terms, m)
##     cats <- rep(0, ncol(X))
##     if (!is.null(xlevels)) 
##         cats[match(names(xlevels), colnames(X))] <- unlist(lapply(xlevels, 
##             length))
##     extraArgs <- list(...)
##     if (length(extraArgs)) {
##         controlargs <- names(formals(rpart.control))
##         indx <- match(names(extraArgs), controlargs, nomatch = 0)
##         if (any(indx == 0)) 
##             stop(gettextf("Argument %s not matched", names(extraArgs)[indx == 
##                 0]), domain = NA)
##     }
##     controls <- rpart.control(...)
##     if (!missing(control)) 
##         controls[names(control)] <- control
##     xval <- controls$xval
##     if (is.null(xval) || (length(xval) == 1 && xval == 0) || 
##         method == "user") {
##         xgroups <- 0
##         xval <- 0
##     }
##     else if (length(xval) == 1) {
##         xgroups <- sample(rep(1:xval, length = nobs), nobs, replace = FALSE)
##     }
##     else if (length(xval) == nobs) {
##         xgroups <- xval
##         xval <- length(unique(xgroups))
##     }
##     else {
##         if (!is.null(attr(m, "na.action"))) {
##             temp <- as.integer(attr(m, "na.action"))
##             xval <- xval[-temp]
##             if (length(xval) == nobs) {
##                 xgroups <- xval
##                 xval <- length(unique(xgroups))
##             }
##             else stop("Wrong length for 'xval'")
##         }
##         else stop("Wrong length for 'xval'")
##     }
##     if (missing(cost)) 
##         cost <- rep(1, nvar)
##     else {
##         if (length(cost) != nvar) 
##             stop("Cost vector is the wrong length")
##         if (any(cost <= 0)) 
##             stop("Cost vector must be positive")
##     }
##     tfun <- function(x) if (is.matrix(x)) 
##         rep(is.ordered(x), ncol(x))
##     else is.ordered(x)
##     labs <- sub("^`(.*)`$", "\\1", attr(Terms, "term.labels"))
##     isord <- unlist(lapply(m[labs], tfun))
##     storage.mode(X) <- "double"
##     storage.mode(wt) <- "double"
##     temp <- as.double(unlist(init$parms))
##     if (!length(temp)) 
##         temp <- 0
##     rpfit <- .Call(C_rpart, ncat = as.integer(cats * !isord), 
##         method = as.integer(method.int), as.double(unlist(controls)), 
##         temp, as.integer(xval), as.integer(xgroups), as.double(t(init$y)), 
##         X, wt, as.integer(init$numy), as.double(cost))
##     nsplit <- nrow(rpfit$isplit)
##     ncat <- if (!is.null(rpfit$csplit)) 
##         nrow(rpfit$csplit)
##     else 0
##     if (nsplit == 0) 
##         xval <- 0
##     numcp <- ncol(rpfit$cptable)
##     temp <- if (nrow(rpfit$cptable) == 3) 
##         c("CP", "nsplit", "rel error")
##     else c("CP", "nsplit", "rel error", "xerror", "xstd")
##     dimnames(rpfit$cptable) <- list(temp, 1:numcp)
##     tname <- c("<leaf>", colnames(X))
##     splits <- matrix(c(rpfit$isplit[, 2:3], rpfit$dsplit), ncol = 5, 
##         dimnames = list(tname[rpfit$isplit[, 1] + 1], c("count", 
##             "ncat", "improve", "index", "adj")))
##     index <- rpfit$inode[, 2]
##     nadd <- sum(isord[rpfit$isplit[, 1]])
##     if (nadd > 0) {
##         newc <- matrix(0, nadd, max(cats))
##         cvar <- rpfit$isplit[, 1]
##         indx <- isord[cvar]
##         cdir <- splits[indx, 2]
##         ccut <- floor(splits[indx, 4])
##         splits[indx, 2] <- cats[cvar[indx]]
##         splits[indx, 4] <- ncat + 1:nadd
##         for (i in 1:nadd) {
##             newc[i, 1:(cats[(cvar[indx])[i]])] <- -as.integer(cdir[i])
##             newc[i, 1:ccut[i]] <- as.integer(cdir[i])
##         }
##         catmat <- if (ncat == 0) 
##             newc
##         else {
##             cs <- rpfit$csplit
##             ncs <- ncol(cs)
##             ncc <- ncol(newc)
##             if (ncs < ncc) 
##                 cs <- cbind(cs, matrix(0, nrow(cs), ncc - ncs))
##             rbind(cs, newc)
##         }
##         ncat <- ncat + nadd
##     }
##     else catmat <- rpfit$csplit
##     if (nsplit == 0) {
##         frame <- data.frame(row.names = 1, var = "<leaf>", n = rpfit$inode[, 
##             5], wt = rpfit$dnode[, 3], dev = rpfit$dnode[, 1], 
##             yval = rpfit$dnode[, 4], complexity = rpfit$dnode[, 
##                 2], ncompete = 0, nsurrogate = 0)
##     }
##     else {
##         temp <- ifelse(index == 0, 1, index)
##         svar <- ifelse(index == 0, 0, rpfit$isplit[temp, 1])
##         frame <- data.frame(row.names = rpfit$inode[, 1], var = tname[svar + 
##             1], n = rpfit$inode[, 5], wt = rpfit$dnode[, 3], 
##             dev = rpfit$dnode[, 1], yval = rpfit$dnode[, 4], 
##             complexity = rpfit$dnode[, 2], ncompete = pmax(0, 
##                 rpfit$inode[, 3] - 1), nsurrogate = rpfit$inode[, 
##                 4])
##     }
##     if (method.int == 3) {
##         numclass <- init$numresp - 2
##         nodeprob <- rpfit$dnode[, numclass + 5]/sum(wt)
##         temp <- pmax(1, init$counts)
##         temp <- rpfit$dnode[, 4 + (1:numclass)] %*% diag(init$parms$prior/temp)
##         yprob <- temp/rowSums(temp)
##         yval2 <- matrix(rpfit$dnode[, 4 + (0:numclass)], ncol = numclass + 
##             1)
##         frame$yval2 <- cbind(yval2, yprob, nodeprob)
##     }
##     else if (init$numresp > 1) 
##         frame$yval2 <- rpfit$dnode[, -(1:3), drop = FALSE]
##     if (is.null(init$summary)) 
##         stop("Initialization routine is missing the 'summary' function")
##     functions <- if (is.null(init$print)) 
##         list(summary = init$summary)
##     else list(summary = init$summary, print = init$print)
##     if (!is.null(init$text)) 
##         functions <- c(functions, list(text = init$text))
##     if (method == "user") 
##         functions <- c(functions, mlist)
##     where <- rpfit$which
##     names(where) <- row.names(m)
##     ans <- list(frame = frame, where = where, call = Call, terms = Terms, 
##         cptable = t(rpfit$cptable), method = method, parms = init$parms, 
##         control = controls, functions = functions, numresp = init$numresp)
##     if (nsplit) 
##         ans$splits = splits
##     if (ncat > 0) 
##         ans$csplit <- catmat + 2
##     if (nsplit) 
##         ans$variable.importance <- importance(ans)
##     if (model) {
##         ans$model <- m
##         if (missing(y)) 
##             y <- FALSE
##     }
##     if (y) 
##         ans$y <- Y
##     if (x) {
##         ans$x <- X
##         ans$wt <- wt
##     }
##     ans$ordered <- isord
##     if (!is.null(attr(m, "na.action"))) 
##         ans$na.action <- attr(m, "na.action")
##     if (!is.null(xlevels)) 
##         attr(ans, "xlevels") <- xlevels
##     if (method == "class") 
##         attr(ans, "ylevels") <- init$ylevels
##     class(ans) <- "rpart"
##     ans
## })(formula = .outcome ~ ., data = list(c(0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 1, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 
## 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0), c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0), c(0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
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## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0), c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
## 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
## 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
## 4, 4, 4, 4)), control = list(20, 7, 0, 4, 5, 2, 0, 30, 0))
##   n= 74 
## 
##          CP nsplit rel error
## 1 0.2608696      0 1.0000000
## 2 0.0000000      1 0.7391304
## 
## Variable importance
##   there[0.007,0.0105) even[0.00493,0.00617)       an[0.0543,0.06) 
##                    50                    14                     9 
##      are[0.078,0.083)       by[0.161,0.169)     can[0.033,0.0367) 
##                     9                     9                     9 
## 
## Node number 1: 74 observations,    complexity param=0.2608696
##   predicted class=Hamilton  expected loss=0.3108108  P(node) =1
##     class counts:    51     3     5    15
##    probabilities: 0.689 0.041 0.068 0.203 
##   left son=2 (63 obs) right son=3 (11 obs)
##   Primary splits:
##       there[0.007,0.0105) < 0.5 to the left,  improve=6.909793, (0 missing)
##       was[0.0189,0.0252)  < 0.5 to the left,  improve=3.330029, (0 missing)
##       of[0.843,0.865)     < 0.5 to the left,  improve=2.540745, (0 missing)
##       also[0.0125,0.0141) < 0.5 to the left,  improve=2.366277, (0 missing)
##       in.[0.261,0.272)    < 0.5 to the left,  improve=2.153057, (0 missing)
##   Surrogate splits:
##       even[0.00493,0.00617) < 0.5 to the left,  agree=0.892, adj=0.273, (0 split)
##       an[0.0543,0.06)       < 0.5 to the left,  agree=0.878, adj=0.182, (0 split)
##       are[0.078,0.083)      < 0.5 to the left,  agree=0.878, adj=0.182, (0 split)
##       by[0.161,0.169)       < 0.5 to the left,  agree=0.878, adj=0.182, (0 split)
##       can[0.033,0.0367)     < 0.5 to the left,  agree=0.878, adj=0.182, (0 split)
## 
## Node number 2: 63 observations
##   predicted class=Hamilton  expected loss=0.2222222  P(node) =0.8513514
##     class counts:    49     3     4     7
##    probabilities: 0.778 0.048 0.063 0.111 
## 
## Node number 3: 11 observations
##   predicted class=Madison   expected loss=0.2727273  P(node) =0.1486486
##     class counts:     2     0     1     8
##    probabilities: 0.182 0.000 0.091 0.727

plot(fed_tree$finalModel, unifrom=FALSE, main= "Classification Tree")

## Warning in plot.window(...): "unifrom" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "unifrom" is not a graphical parameter

## Warning in title(...): "unifrom" is not a graphical parameter

text(fed_tree$finalModel, use.n=TRUE, all=TRUE, cex=.5)

fancyRpartPlot(fed_tree$finalModel)

Predictions based on tree

fed_predict <- predict(fed_tree, newdata=test_data)
table(fed_predict)

## fed_predict
## Hamilton       HM      Jay  Madison 
##        8        0        0        3

test_data$prediction <- fed_predict
test_data$prediction

##  [1] Madison  Hamilton Hamilton Hamilton Madison  Madison  Hamilton Hamilton
##  [9] Hamilton Hamilton Hamilton
## Levels: Hamilton HM Jay Madison

test_data$file <- data$filename[test]
predictions <- table(test_data$file, test_data$prediction)
predictions 

##                   
##                    Hamilton HM Jay Madison
##   dispt_fed_49.txt        0  0   0       1
##   dispt_fed_50.txt        1  0   0       0
##   dispt_fed_51.txt        1  0   0       0
##   dispt_fed_52.txt        1  0   0       0
##   dispt_fed_53.txt        0  0   0       1
##   dispt_fed_54.txt        0  0   0       1
##   dispt_fed_55.txt        1  0   0       0
##   dispt_fed_56.txt        1  0   0       0
##   dispt_fed_57.txt        1  0   0       0
##   dispt_fed_62.txt        1  0   0       0
##   dispt_fed_63.txt        1  0   0       0

Tune tree

fed_tree2<- train(author~., data= train_data, 
                  method="rpart",
                  trControl= trainControl(method="optimism_boot"))
fed_predict2 <- predict(fed_tree2, newdata=test_data)
table(fed_predict2)

## fed_predict2
## Hamilton       HM      Jay  Madison 
##        8        0        0        3

Tune tree again

fed_tree_tune<- train(author~., data=train_data, method="rpart", 
                      metric="Accuracy",
                      tuneLength=15)
print(fed_tree_tune)

## CART 
## 
## 74 samples
## 70 predictors
##  4 classes: 'Hamilton', 'HM', 'Jay', 'Madison' 
## 
## No pre-processing
## Resampling: Bootstrapped (25 reps) 
## Summary of sample sizes: 74, 74, 74, 74, 74, 74, ... 
## Resampling results across tuning parameters:
## 
##   cp          Accuracy   Kappa    
##   0.00000000  0.7343583  0.2782963
##   0.01863354  0.7343583  0.2782963
##   0.03726708  0.7371661  0.2699076
##   0.05590062  0.7371661  0.2610967
##   0.07453416  0.7371661  0.2610967
##   0.09316770  0.7475619  0.2583808
##   0.11180124  0.7504190  0.2576044
##   0.13043478  0.7538106  0.2493040
##   0.14906832  0.7432611  0.2150802
##   0.16770186  0.7432611  0.2150802
##   0.18633540  0.7467394  0.2201155
##   0.20496894  0.7397907  0.1902838
##   0.22360248  0.7414573  0.1930560
##   0.24223602  0.7397958  0.1694835
##   0.26086957  0.7397958  0.1694835
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.1304348.

fancyRpartPlot(fed_tree_tune$finalModel)

fed_predict_tune <- predict(fed_tree_tune, newdata=test_data)
table(fed_predict_tune)

## fed_predict_tune
## Hamilton       HM      Jay  Madison 
##        8        0        0        3