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April 2, 2016 18:50
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R scripts accompanying the class notes for Week 10 of Applied Multivariate Statistical Analysis course.
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# Initialize Libraries | |
library("MASS") | |
library("ROCR") | |
# Function Definitions | |
evaluate.performance <- function(model, data){ | |
yhat <- predict(model, newdata = data)$class | |
cmat <- table(data$y, yhat) | |
accuracy <- sum(diag(cmat)) / nrow(data) | |
error <- (1 - accuracy) | |
return(list("accuracy" = accuracy, "error" = error)) | |
} | |
show.legend <- function(dataset, location = "bottomright"){ | |
legend( | |
location, legend = sort(unique(dataset$y)), bty = "n", | |
horiz = T, inset = 0.03, fill = sort(unique(dataset$y)) + 2, | |
title = "Class" | |
) | |
} | |
# Tuesday, 29 of March, 2016 | |
# Banana Data Set | |
banana <- read.csv("banana-shaped-data-1.csv", header = T) | |
plot(banana[,-3], col = banana$y + 2) | |
show.legend(banana) | |
# LDA | |
lda.banana <- lda(y ~ ., data = banana) | |
lda.banana.performance <- evaluate.performance(lda.banana, banana) | |
# QDA | |
qda.banana <- qda(y ~ ., data = banana) | |
qda.banana.performance <- evaluate.performance(qda.banana, banana) | |
# Four Corners Data Set | |
fourcorners <- read.csv("four-corners-data-1.csv", header = T) | |
plot(fourcorners[,-3], col = fourcorners$y + 2) | |
show.legend(fourcorners, location = "top") | |
lda.fourcorners <- lda(y ~ ., data = fourcorners) | |
lda.fourcorners.performance <- evaluate.performance(lda.fourcorners, fourcorners) | |
# QDA | |
qda.fourcorners <- qda(y ~ ., data = fourcorners) | |
qda.fourcorners.performance <- evaluate.performance(qda.fourcorners, fourcorners) | |
# Easy Doughnut Data Set | |
easydoughnut <- read.csv("doughnuts-easy.csv", header = T) | |
plot(easydoughnut[,-3], col = easydoughnut$y + 2) | |
show.legend(easydoughnut) | |
# LDA | |
lda.easydoughnut <- lda(y ~ ., data = easydoughnut) | |
lda.easydoughnut.performance <- evaluate.performance(lda.easydoughnut, easydoughnut) | |
# QDA | |
qda.easydoughnut <- qda(y ~ ., data = easydoughnut) | |
qda.easydoughnut.performance <- evaluate.performance(qda.easydoughnut, easydoughnut) | |
# Doughnut Data Set | |
doughnut <- read.csv("doughnuts.csv", header = T) | |
plot(doughnut[,-3], col = doughnut$y + 2) | |
show.legend(doughnut) | |
# LDA | |
lda.doughnut <- lda(y ~ ., data = doughnut) | |
lda.doughnut.performance <- evaluate.performance(lda.doughnut, doughnut) | |
# QDA | |
qda.doughnut <- qda(y ~ ., data = doughnut) | |
qda.doughnut.performance <- evaluate.performance(qda.doughnut, doughnut) | |
# Brain Cancer Data Set | |
brain <- read.csv("brain-cancer-1.csv", header = T) | |
# Variance-Covariance Matrix | |
S <- cov(brain[,-1]) | |
# Determinant of S will be Zero indicating that S is ill-conditioned/singular | |
generalized.variance <- det(S) | |
# LDA | |
lda.brain <- lda(brain.y ~ ., data = brain) | |
lda.brain.performance <- evaluate.performance(lda.brain, brain) | |
# QDA | |
qda.brain <- qda(brain.y ~ ., data = brain) | |
qda.brain.performance <- evaluate.performance(qda.brain, brain) | |
# Thursday, 31 of March, 2016 | |
dataset <- read.csv("four-corners-data-1.csv", header = T) | |
qda.model <- qda(y ~ ., data = dataset) | |
qda.model.performance <- performance( | |
prediction(predict(qad.model, dataset)$posterior[,2], dataset$y), | |
measure = "tpr", x.measure = "fpr" | |
) | |
plot( | |
qda.model.performance, | |
main = "ROC Curve", xlab = "False Positive Rate", ylab = "True Positive Rate", | |
col = "forestgreen", lwd = 2 | |
) | |
abline(a = 0, b = 1, lwd = 2, lty = 2) | |
legend( | |
"bottomright", col = c("black", "forestgreen"), lty = c(2, 1), bty = "n", lwd = 2, | |
horiz = T, legend = c("Random", "Optimal") | |
) |
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