Posts tagged with ‘R’

  • Workbook: Change Point Analysis of HRV Data

    By Mladen Jovanovic on 02/08/2015

    What is Change Point Analysis and why and how can you use it to help you out to make sense of your monitoring data? Find out more….

  • R Playbook: Introduction to Multilevel/Hierarchical Models

    By Mladen Jovanovic on 09/07/2015

    I wrote about mixed-level models before and I want to expand on it here. I sort-of finished Andrew Gelman’s Data Analysis Using Regression and Multilevel/Hierarchical Models and will continue to play with it. As far as I know Andrew uses the term multilevel models and avoids the terms fixed and random effect. This is a great book to have.

    My…

  • Velocity-Based Training: Signal vs. Noise

    By Mladen Jovanovic on 29/04/2015

    This is a R workbook using my older bench press data, in which I want to discuss Signal vs. Noise of Velocity-Based Training (VBT) measurements. This could be used for future reliability studies. The goal is to compare within-individual variations of velocity over load-velocity relationship (noise) with smallest practical velocity difference (in my opinion difference in velocities across nRM,…

  • Non-responders: are they really?

    By Mladen Jovanovic on 09/04/2015

    Latest obsession of the researches is individual variation in training responses. The motivation behind this approach (known and emphasized in theory of training as individualization principle) is the creation of personalized medicine or personalized training.

    Unfortunately, sometimes we see these individual differences (in treatment reaction), although they are artefacts of within-individual typical variation/error of measurement and regression to the mean.

  • R Playbook: Introduction to Mixed-Models

    By Mladen Jovanovic on 24/03/2015

    I just started reading more about mixed-models (multilevel/hierarchical) and will use this as a playbook. Mostly because I learn the best by experimenting with the data and I suggest everyone to try to do the same. So please note that this is just a published playbook – if you find it useful, great, if you find some errors, please…

  • AFL Game GPS Stats Analytics Workbook

    By Mladen Jovanovic on 14/03/2015

    Keith Lyons shared one game of data for one AFL game across four quarters for the #UCSIA15 course. I took some time to analyze it using R and created interactive and reproducible document (HTML) using knitr and markdown. You can download markdown file and CSV data file HERE

  • Banister Impulse~Response model in R [part 2]

    By Mladen Jovanovic on 18/10/2014

    In the previous part I’ve introduced multivariate modeling of impulse and response using Banister model. In this part I will continue with exploration of this model, mainly visualizing reaction predicted by the model on standardize impulse (load) and compare prediction using multiple impulses. I will use same data sets: one by Skiba and one randomly generated as in first…

  • Banister Impulse~Response Model in R [Part 1]

    By Mladen Jovanovic on 14/10/2014

    Banister Impulse~Response Model in R Before you start reading this post, please read EXCELLENT paper by Clark and Skiba, especially on the topic of Banister impulse-response model. I decided to write code in R, but also allow for multivariate analysis (where impulse can be multiple variables, as is the case in sports) which can speed the thing...

  • What Are Biomotor Abilities?

    By Mladen Jovanovic on 01/07/2014

    Do you know what are biomotor abilities? How did they ‘emerge’? The purpose of this video is to explain to you the ontology of biomotor abilities, the certain flaws of their use (The Root problem, buckets, periodization based on those qualities) and also provide statistical analysis in RStudio with simulated data using Factor Analysis and Hierarchical Clustering.

  • Stats Playbook: What is Anscombe’s Quartet and why is it important?

    By Mladen Jovanovic on 27/05/2014

    Stats Playbook: What is Anscombe’s Quartet and why is it important?   The following paragraph is take from Wikipedia “Anscombe’s quartet comprises four datasets that have nearly identical simple statistical properties, yet appear very different when graphed. Each dataset consists of eleven (x,y) points. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance...

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