1. Data Analytics – ITWS-4600/ITWS-6600/MATP-4450
Labs: (SVM, Multi-Dimensional Scaling, Dimension Reduction), Factor Analysis, RandomForest
Peter Fox
Data Analytics – ITWS-4600/ITWS-6600/MATP-4450
Group 3 Lab 2, March 8, 2018

2. If you did not complete svm
group3/
lab1_svm{1,11}.R
lab1_svm{12,13}.R
lab1_svm_rpart1.R

3. And MDS, DR lab1_mds{1,3}.R lab1_dr{1,4}.R

4. Try these
example_exploratoryFactorAnalysis.R on dataset_exploratoryFactorAnalysis.csv (on website)
(this was the example on courses in the lecture)
Do these – lab2_fa{1,2,4,5}.R

5. Factor Analysis
data(iqitems) # data(ability) ability.irt <- irt.fa(ability) ability.scores <- score.irt(ability.irt,ability) data(attitude) cor(attitude) # Compute eigenvalues and eigenvectors of the correlation matrix. pfa.eigen<-eigen(cor(attitude)) pfa.eigen$values # set a value for the number of factors (for clarity) factors<-2 # Extract and transform two components. pfa.eigen$vectors [ , 1:factors ] %*% + diag ( sqrt (pfa.eigen$values [ 1:factors ] ),factors,factors )

6. Glass
index <- 1:nrow(Glass) testindex <- sample(index, trunc(length(index)/3)) testset <- Glass[testindex,] trainset <- Glass[-testindex,] Cor(testset) Factor Analysis?

7. randomForest
> library(e1071) > library(rpart) > library(mlbench) # etc. > data(kyphosis) > require(randomForest) # or library(randomForest) > fitKF <- randomForest(Kyphosis ~ Age + Number + Start, data=kyphosis) > print(fitKF) # view results > importance(fitKF) # importance of each predictor # what else can you do? data(swiss) # fertility? lab2_rf1.R data(Glass,package=“mlbench”) # Type ~ <what>? data(Titanic) # Survived ~ . Find - Mileage~Price + Country + Reliability + Type