ETC5521 Worksheet Week 8

Going beyond two variables, exploring high dimensions

Author

Prof. Di Cook

Exercise 1: Parkinsons

This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson’s disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals (“name” column). The main aim of the data is to discriminate healthy people from those with PD, according to “status” column which is set to 0 for healthy and 1 for PD.

The data is available at The UCI Machine Learning Repository in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column. There are 24 variables in the file, including the persons name in column 1.

The data are originally analysed in: Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2008), ‘Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease’, IEEE Transactions on Biomedical Engineering (to appear).

Code 
library(cassowaryr)
# Load the data
data(pk)
  1. How many pairwise plots would you need to look at, to look at all of them?
  1. Compute several of the scagnostics (monotonic, outlying, clumpy2) for the first five variables of variables, except for name. (Note: We are using just five for computing speed, but the scagnostics could be calculated on all variables.)
Code 
# Compute the scagnostics on the relevant variables
s <- calc_scags_wide(pk[,2:5],
                scags=c("outlying","monotonic",
                        "clumpy2"))
s
  1. Sort the scagnostics, separately by the values on (i) monotonic (ii) outlying (iii) clumpy2, and plot the pair of variables with the highest values on each.
  1. Make an interactive scatterplot matrix. Browse over it to choose other interesting pairs of variables and make the plots.
  1. The scagnostics help us to find interesting associations between pairs of variables. However, the problem here is to detect differences between Parkinsons’ patients and normal patients. How would you go about that? Think about some ideas long the line of scagnostics but look for differences between the two groups.
  1. Now try to do this using the scagnistics.