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CORRELATION AND INFORMATION THEORY MEASUREMENTS – PART 3

Before we begin, if you don’t have the data, first get it from the first tutorial here. You will also need to Install and load the ggplot2 and reshape2 packages.
 if(!require(ggplot2)){install.packages(ggplot2, dep=T)} if(!require(reshape2)){install.packages(reshape2, dep=T)} 
Answers to these exercises are available here.

The mutual information quantifies the “amount of information” between the two variables in bits. To transform it into a metric, there has been several variants proposed of the MI; one of those is a normalization that assumes MI as an analog of co-variance and calculated it as a Pearson correlation co-efficient NMI=MI/(Hx+Hy)^(1/2).

Exercise 1
Please write a function to calculate the normalized mutual information with two input parameters x,y as vectors and NMI as the return value. Hint: Reuse the code of the last tutorial .

Exercise 2
Similar to before, we will estimate the linear auto-correlation function. Also, it is possible to estimate a nonlinear auto-correlation function using the NMI as a correlation co-efficient of the lags of the time series. Please load the function createLags(x, numberOfLags, VarName) and create the embedded space for the first 400 lags of the LEVEL and the RAIN.

Exercise 3
To calculate the nonlinear auto-correlation function (NACF), you can estimate the NMI for the first column of lags_level compared with all the other lags. Do it also for the lags_rain.

Exercise 4
To calculate the nonlinear cross correlation function (NCCF), you can estimate the NMI for the first column of lags_level compared with all the lags of lags_rain. Do it also for the lags_rain compared with all the lags of  lags_level .

Learn more about Model Evaluation in the online course Regression Machine Learning with R. In this course, you will learn how to:

• Avoid model over-fitting using cross-validation for optimal parameter selection
• Use correlation to avoid multi-col linearity problems in your model
• And much more

Exercise 5
Another very useful tool of measurement is the the Kullback–Leibler divergence or relative entropy. It measures how one’s probability distribution q diverges from a second expected probability distribution p . It is possible to estimate it using the formula: cross entropy of q respect to p minus the entropy of p.
To estimate the probability distribution p and q, this time we will change our approach and we will use a geom_histogram. Please create a histogram of 10 bins from the level and a histogram from the first lag, then assign it to p and q.
Hint: 1) Remember to always use the interval from p for the histograms. 2) After grabbing the first layer of data from the plot with  layer_data , you can get use from the column \$count .

Exercise 6
Now, please calculate the entropy of p.

Exercise 7
Now calculate the cross entropy  Hp_q with the formula -sum(p*log2(q)). Hint: Remember to avoid negative values of q.

Exercise 8
Finally, please calculate and print the Kullback–Leibler divergence.