Essay Program to Perform Principal Component Analysis (PCA) – Statistics Assignment Help

Assignment Task:

Task:

Problem Description:

Program to perform Principal component analysis (PCA) on the dataset in R PROGRAM: a. Implement PCA on IRIS Dataset. Also compare our results by calculating eigenvectors and eigenvalues separately # Taking the numeric part of the IRIS data data_iris <- iris[1:4]

# Calculating the covariance matrix Cov_data <- cov(data_iris )

# Find out the eigenvectors and eigenvalues using the covariance matrix Eigen_data <- eigen(Cov_data) # Using the inbuilt function PCA_data <- princomp(data_iris ,cor=”False”)

# Let’s now compare the output variances Eigen_data$values PCA_data$sdev^2

# Compare the eigenvectors of both models. PCA_data$loadings[,1:4] Eigen_data$vectors summary(PCA_data) biplot (PCA_data) screeplot(PCA_data, type=”lines”)

# Fit two naive Bayes models. one over the entire data. The second on the first principal component. We will calculate the difference in accuracy between these two models. Building two models. #Select the first principal component for the second model model2 = PCA_data$loadings[,1] #For the second model, we need to calculate scores by multiplying our loadings with the data model2_scores <- as.matrix(data_iris) %*% model2

#Loading libraries for naiveBayes model library(class) library(e1071)

#Fitting the first model over the entire data mod1<-naiveBayes(iris[,1:4], iris[,5])