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topological_relations_code.R

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+#####################################
+#MIT License
+
+#Copyright (c) 2016 Bradley T. Rentz
+
+#Permission is hereby granted, free of charge, to any person obtaining a copy
+#of this software and associated documentation files (the "Software"), to deal
+#in the Software without restriction, including without limitation the rights
+#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+#copies of the Software, and to permit persons to whom the Software is
+#furnished to do so, subject to the following conditions:
+#  
+#  The above copyright notice and this permission notice shall be included in all
+#copies or substantial portions of the Software.
+#
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+#SOFTWARE.
+#####################################
+## Author: Bradley T. Rentz
+## This code was last modified on 23 October 2016
+## Base R Version: 3.3.1
+## OS: Mac OS 10.10.5
+## Evtree version 1.0-1
+## Patrykit version 1.1-1
+## Rpart version 4.1-10
+## Code edited in R Studio version 0.99.892
+## Description: This code runs 3 tree classification models on topological relations data for the Pohnpeian language 
+## based on elicitation sessions from five L1 Pohnpeian speakers using the BowPed Topological Relations Toolkit
+## The classification trees include classification tree (rpart), conditional inference classification tree (partykit),
+## and evolution tree (evtree)
+## Author contact: [email protected]
+####################################
+
+library(rpart)
+library(partykit)
+library(tree)
+library(rpart.plot)
+library(evtree)
+library(randomForest)
+
+## load csv file
+data <- read.csv("topological_relations_coded_data.csv")
+
+## set variables to factors
+data$contact <- as.factor(data$contact)
+data$horizontal.ground <- as.factor(data$horizontal.ground)
+data$X3d.ground <- as.factor(data$X3d.ground)
+data$top.of.ground <- as.factor(data$top.of.ground)
+data$containment <- as.factor(data$containment)
+data$X3d.fig <- as.factor(data$X3d.fig)
+data$go.around.ground <- as.factor(data$go.around.ground)
+data$go.around.fig <- as.factor(data$go.around.fig)
+data$ground.vertical <- as.factor(data$ground.vertical)
+data$fig.attached.to.ground <- as.factor(data$fig.attached.to.ground)
+data$ground.larger.2D.fig <- as.factor(data$ground.larger.2D.fig)
+data$ground.building <- as.factor(data$ground.building)
+data$fig.side.ground <- as.factor(data$fig.side.ground)
+data$fig.far.ground <- as.factor(data$fig.far.ground)
+
+### Classification trees
+### 1. Rpart
+# make basic classification tree, exclude image, speaker, and ground 
+topo.rp = as.party(rpart(word ~ ., data=data[ ,-c(1,3, 13)],control=rpart.control(minsplit=20))) ## change minsplit to desired minimum split group size
+
+plot(topo.rp, compress=T, branch=1, margin=0.7)
+
+### 2. ctree
+###conditional inference version
+topo.party = ctree(word ~ ., data=data[ ,-c(1,3, 13)])
+plot(topo.party,drop_terminal=0) # 15in x 50in save as
+plot(topo.party,type="simple")
+
+
+#### 3. Evolution Tree (evtree package)
+### evtree ### note: each run of the evtree may produce slightly different result because of stochastic nature of algorithm so set seed
+set.seed(146235)
+tree.evtree = evtree(word ~  contact + horizontal.ground + X3d.ground+ animacy.fig   + top.of.ground + containment + X3d.fig + go.around.ground + go.around.fig + ground.vertical + fig.attached.to.ground + ground.larger.2D.fig + ground.building + fig.side.ground+fig.far.ground, data=data)
+
+plot(tree.evtree) # plot tree
+
+plot(tree.evtree, gp = gpar(fontsize = 6))   ## change font size
+
+
+## compare error rates and evaluation function of evtree, CART (rpart), and Condition Inference Tree)
+mc <- function(obj) 1 - mean(predict(obj) == data$word)
+evalfun <- function(obj) 2 * nrow(data) * mc(obj) +
+  width(obj) * log(nrow(data))
+trees <- list("evtree" = tree.evtree, "rpart" = topo.rp, "ctree" = topo.party)
+round(sapply(trees, function(obj) c("misclassification" = mc(obj),
+                                    "evaluation function" = evalfun(obj))), digits = 3)
+### to evaluate models, misclassification says which percentage of current data is misclassified by model.
+### the evaluation function (2 * number of rows * misclassification rate + the number of terminal nodes * log(number of rows))
+### describes the misclassfication rate as a function of tree complexity (the more complex the tree, the higher the evaluation function value)
+### The evaluation function aims at describing generalizability vs. overfitting
+### A good tree would have low values for both measures. If tree has low misclassification error and high evaluation function it may be overfitted to the data
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