# Search the site for the term 'R code'

• ## Time series, RQA and neural networks I

11/06/2019

In some other articles in this blog, I have already written about complex time series, recurrence quantification analysis (RQA) and neural networks. In this series of articles, I will discuss some points to take into account when combining the use of these two tools to identify patterns in complex series, such as detecting anomalies in electrocardiograms or electroencephalograms.

• ## EDF files of physiological signals

28/03/2019

When we try to learn how to work with time series, it is very useful to have good data sets, and much better if they contain real data. It is difficult to obtain long series, or series presenting interesting and well located and identified patterns, with which we can perform practices. An excellent source of complex time series is our own organism, and everything we can learn by working with them can be extrapolated to any other context.

• ## Beyond chaos, randomness

18/11/2016

In this article I will show how, through a very simple and totally deterministic process, we can move from a stationary system to a completely random one, going through periodic and chaotic dynamics. For this, I will generate several time series with these characteristics using the program R and several packages that can help us in the analysis of them.

• ## PISA Data Analytics, Correspondece Analysis

21/10/2016

The PISA database contains, in addition to the scores of students, a lot of demographic, socioeconomic and cultural data about them, collected through a series of questionnaires, that allow contextualize the academic results and make studies with a great number of variables. Most of these data are categorical, making the correspondence analysis a particularly appropriate tool to work with them. In this article I will show you how to easily perform this analysis using the ca package of the R program.

• ## Complex Time Series II, Web diagrams

10/09/2016

I continue the series on graphic characterization of the complexity in time series using the helper application GraphStudy. In this article I will show how to construct a graph with which you can easily distinguish whether a particular series from an iterated function presents a chaotic dynamics, the web diagram.

• ## PISA Database, R code for plausible values

12/03/2016

In this post you can download the R code samples to work with plausible values in the PISA database, to calculate averages, mean differences or linear regression of the scores of the students, using replicate weights to compute standard errors.

• ## PISA Data Analytics, the Student Scores

12/03/2016

In the previous article in this series we viewed how to computing standard errors with replicate weights in PISA database, in this article we will take an overview of one of the most controversial points of these studies, the complex system of scores implemented.

• ## PISA Database Calculations with Replicate Weights R Source Code

28/02/2016

In this post you can download the R code examples to compute the standard errors of the mean, standard deviation, proportions or mean differences, on the data of the PISA database, using the replicate weights method.

• ## PISA Data Analytics, Sample Weights and Replicate Weights

28/02/2016

In the previous article in this series we saw an introduction to PISA data analytics, with examples of functions in R code for sampling, and we talked about the sampling weights, which ponder each student so that it represents a group of individuals with the same characteristics rather than a single student, (remember that PISA aims to assess the effect of educational policies on the whole population of the country, not on individual students). In this article, we will see how to use these weights to calculate estimators from samples and we'll see also how to calculate standard errors of these estimators using replicated weights.

• ## PISA Database Basic R Source Code

19/02/2016

In this post you will find examples of R code for data sampling in PISA database. In these examples the different weights of students, schools or parents are corrected depending on the number of records selected for the sample. Also there are examples of stratified sampling using the values in a particular column in the data set.