-
-
Save doc22940/98a58e55251774b73293b9b8c724bbee to your computer and use it in GitHub Desktop.
R scripts accompanying the class notes for Week 10 of Applied Multivariate Statistical Analysis course.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# 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") | |
) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment