How to fit a copula model in R [heavily revised]. Part 2: fitting the copula

Here I am again with part 2. If you would like to read part 1 of this short tutorial on copulas, please click here.

In this second post I am going to select a copula model, fit it to a test dataset, evaluate the fitting and generate random observations from the fitted multivariate distribution. Furthermore I am going to show how to measure correlation using Spearman's Rho and Kendall's Tau. In order to run the code in this post you need the following packages: copula and VineCopula.

The dataset

For this example I am going to use a test dataset. You can download it from this link. This test dataset contains two variables, x and y, characterized by a strong left tail correlation.

By visually inspecting the plot of x versus y you can easily notice that low values of x tends to be highly correlated with low values of y.

How to fit a copula model in R [heavily revised]. Part 1: basic tools

More than a year ago I wrote a short post on how to fit a copula model in R. The post showed how to make a very raw and basic fitting of a test dataset to a two dimensional normal copula (or a gaussian copula if you wish) using the copula package. The content of the post was not much, but that was a big part of what I knew about how to use copulas in R at that time and there was not much documentation available other than the official copula package documentation which is quite technical in my opinion (as it should be) and may not be that easy for a beginner copula user.

Simulating a mass attached to a spring with control theory

Control system theory is very useful when it comes to simulate physical systems and their behaviour.
Suppose you have a mass attached to a spring on an horizontal flat surface. The force diagram associated with this physical system can be sketched as follows:



By analysing the force diagram, you can actually see that on the x axis the only forces acting on the mass are the elastic force (Fe), the friction (Fr) due to the air resistance and the external force applied to the mass (u). On the y axis the normal force and the force of gravity balance out, therefore there is no motion.

Solving a circuit with a mutual inductor using LTspice

Mutual inductors can be a lot of fun, and sometimes a bit of an headache if you mess something up or represent them in a complicated way. Take for instance the following circuit designed with Circuit Lab a great online circuit design and simulator tool



This is one of the first excercises we were taught in our circuit class. As you can probably see, the circuit is composed by two current sources with different frequency, some resistors and a mutual inductor. The aim of the analysis is to find out the total power consumed by the resistors. Let's analyse each problem carefully before proceeding.

Step response of a RLC series circuit

Today I am going to make a brief description of the step response of a RLC series circuit. This is the schematic made with LTspice

As you can see the components used are a resistor, an inductor and a capacitor connected in series. By applying Kirchhoff voltage law we obtain the following equation

$$u(t) = R x(t) + L\frac{dx(t)}{dt} + \frac{1}{C} \int x(t)dt $$

Three-phase symmetric perfectly balanced system LTspice simulation

A symmetric, three-phase and perfectly balanced system can be easily modelled either by pen and paper with the use of phasors or using LTspice which is faster. In case you would like to try I suggest to first try the pen and paper way and then check the results on the simulator. A basic example of such system would be the following:

How to simulate a simple high pass filters on LTspice

Here I am again after a long break!

During my last engineering class I learnt about the frequency response of a system and how this thing can be applied to solve simple problems. But the crucial question from most of us was the following: how would you build such filters, and perhaps more importantly, how would you tune them?

In principle, you could build a simple filter using nothing more than a resistor and a capacitor and, as you might have guessed, LTspice once again comes at rescuing us from our wandering around.

Let’s say we would like to build a simple high pass filter. Then we could try building the following circuit

Selecting the number of neurons in the hidden layer of a neural network

Recently I wrote a post for DataScience+ (which by the way is a great website for learning about R) explaining how to fit a neural network in R using the neuralnet package, however I glossed over the “how to choose the number of neurons in the hidden layer” part. The glossing over is mainly due to the fact that there is no fixed rule or suggested “best” rule for this task but the mainstream approach (as far as I know) is mostly a trial and error process starting from a set of rules of thumb and a heavy cross validating attitude.

Predicting creditability using logistic regression in R: cross validating the classifier (part 2)

Now that we fitted the classifier and run some preliminary tests, in order to get a grasp at how our model is doing when predicting creditability we need to run some cross validation methods.
Cross validation is a model evaluation method that does not use conventional fitting measures (such as R^2 of linear regression) when trying to evaluate the model. Cross validation is focused on the predictive ability of the model. The process it follows is the following: the dataset is splitted in a training and testing set, the model is trained on the testing set and tested on the test set.

Note that running this process one time gives you a somewhat unreliable estimate of the performance of your model since this estimate usually has a non-neglectable variance.

Predicting creditability using logistic regression in R (part 1)

As I said in the previous post, this summer I’ve been learning some of the most popular machine learning algorithms and trying to apply what I’ve learned to real world scenarios. The German Credit dataset provided by the UCI Machine Learning Repository is another great example of application.

The German Credit dataset contains 1000 samples of applicants asking for some kind of loan and the creditability (either good or bad)  alongside with 20 features that are believed to be relevant in predicting creditability. Some examples are: the duration of the loan, the amount, the age of the applicant, the sex, and so on. Note that the dataset contains both categorical and quantitative features.
This is a classical example of a binary classification task, where our goal is essentially to build a model that can improve the selection process of the applicants.

RandomForestClassifier on the cars dataset ML

Since the beginning of this summer I have been practicing a lot with Scikit-learn to improve my knowledge of Machine Learning both on theory and practice. Last week I also tried to tackle some of the Kaggle competitions although they are really tough if you wish to get into the top 50 best scores. Perhaps I will post something about the experience in the future.

Scikit-learn is a (almost) ready to use package for Machine Learning in Python. It is so very user friendly and in some cases not that much coding around is needed to achieve interesting results such as in the case of the cars dataset.

The cars dataset, from the UCI Machine Learning Repository, is a collection of about 1700 entries of cars each with 6 features that can be easily recognized by the name (buying, maint, doors, persons, lug_boot, safety). Check the dataset description for more detailed information. The feature to be predicted is “class” and the possible values are unacc,acc,good,v-good. Most of the features are categorical, therefore they need to be encoded into numbers. Pandas is great for quick features encoding.

Using Arduino to measure friction coefficient

As a sideproject I decided to design a simple experiment and use Arduino to measure the friction coefficient of an object sliding on a given material.

Ideally we would like our first object to slide (not roll) on a sheet of a given material as below:

If we know the angle (we can easily set it) and the mass of the wooden block, then the only unknown variable is the friction coefficient and we can easily estimate it by measuring how long it took for the block to go over a certain distance x.

European Option Pricing with Python, Java and C++

Please make sure to read the disclaimer at the bottom of the page before continuing reading.

Plain vanilla call and put european options are one of the simplest financial derivatives existing. A european call (put) option is essentially a contract which by paying a fee gives you the right to buy (sell) a stock at a predetermined price, the strike price, at a future date. This kind of contracts was originally intended as an insurance tool for companies to fix the selling price of their goods and hedge the price risk.

Options have some interesting features, for starters, if you are the buyer of an option the payoff is potentially unlimited and the loss is limited to the option price as you can see by the payoff diagram below

of course the reverse is true for the seller (huge downside and limited upside).

Controlling lights according to sunlight using only few electronic components

At the beginning of this summer, I was asked to provide a simple (and possibly cost-effective) solution to a simple problem: how can I do in order for my garden LED lights to turn themselves on and off according to the sunlight?

There are plenty of ready to use circuits and tools that one can use to answer this question, however I decided to try and design something “new” empowered by what I recently learned about NPN transistors, relays and LT-Spice IV. Let me talk you through my workflow:

The problem and the setting:

The LED lights which provide illumination in the garden are powered by a solar panel which during the day charges the battery. At night, the stored energy is used to power the lamp. The solar charge controller ensures that the charging process is smooth and that everything is going as it should during the charge and discharge processes. Since the solar charge controller is very minimal, it does not have a timer or a switch to turn on and off the lights. A manual switch is therefore used instead (not shown in the picture and to be replaced by this project). The lamp is a 12V 4.5W LED lamp.

The solution

Basic Hidden Markov model

A hidden Markov model is a statistical model which builds upon the concept of a Markov chain.

The idea behind the model is simple: imagine your system can be modeled as a Markov chain and the signals emitted by the system depend only on the current state of the system. If the states of the system are not visible and what you can observe are only the emitted signals, then this is a Hidden Markov model.