# R FOR HYDROLOGISTS – Correlation and Information Theory Measurements: Part 3: Exercises

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**R FOR HYDROLOGISTS **

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 `

.

**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.

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