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Line 1: − + − == How does one find relations between variables ==+ − To do this you need to have your data parsed, cleaned, all in one place, and ideally even visualized. The next step is to find relations between variables. Some people do this by themselves, by using programming languages such as R and Python in notebooks or apps. Coding platforms such as the notebooks on [[Open Humans]], Kaggle or GitHub can help, but it frequently − requires technical skills. − There are also a number of tools or apps that can semi-automatically perform these correlations and help in doing the analyses.+ + + + Line 56:
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Finding relations between variables in time series (edit)
Revision as of 19:07, 26 July 2023
, 19:07, 26 July 2023no edit summary
{{Topic Infobox}}
{{Topic Infobox}}
Most personal science projects require finding relationships between different variables of the type 'time series'<ref>Core-Guide_Longitudinal-Data-Analysis_10-05-17.pdf (duke.edu)</ref>. An example could be the question "does my daily chocolate consumption correlate with my daily focus score?". Most internet resources treat time series as (regular cyclical) series, which is not useful as most of the tracking variables have no regularly cyclical component.
Most personal science projects require finding relationships between different variables of the type 'time series'<ref>Core-Guide_Longitudinal-Data-Analysis_10-05-17.pdf (duke.edu)</ref>. An example could be the question "does my daily chocolate consumption correlate with my daily focus score?".
You could do experiments if you control everything rigidly or if the effects are strong and quick, like less than a week. Old data may be useable as Baseline.
Finding more complicated relationships require better statistical tests and algorithms and data science skills. Apps that would do this automatically or at least easily are not yet available. See below. Most internet resources treat time series as (regular cyclical) series, which is not useful as most of the tracked variables have irregular patterns and don't even have a regularly cyclical component.
To do anything mentioned above you need to have your data parsed, cleaned, all in one place, and ideally even visualized.
== List of less technical tools ==
== List of less technical tools ==
There are also a number of tools or apps that can semi-automatically perform these correlations and help in doing the analyses.
==== [[Open Humans]] and their Personal Analysis notebooks ====
==== [[Open Humans]] and their Personal Analysis notebooks ====
== List of very technical tools ==
== List of very technical tools ==
Some people do all the data science by themselves, by using programming languages such as R and Python in notebooks or apps. Coding platforms such as the notebooks on [[Open Humans]], Kaggle or GitHub can help.
Programming languages for statistics; Matlab, R, Python, Julia.
Programming languages for statistics; Matlab, R, Python, Julia.
==== [[List of Interesting Self-Tracking Results#Observational.2C%20Many%20variables|DIY Individuals]] ====
==== [[List of Interesting Self-Tracking Results#Observational.2C%20Many%20variables|DIY Individuals]] ====
== Reasons time series analysis especially as applied to QS is hard ==
== Reasons time series analysis especially as applied to QS is hard ==
[https://forum.quantifiedself.com/t/my-baseline-network-physiology-10-days-of-eeg-egg-ekg-cgm-temperature-activity-and-food-logs/5671/19 Wavelet coherence] is one potential solution.
[https://forum.quantifiedself.com/t/my-baseline-network-physiology-10-days-of-eeg-egg-ekg-cgm-temperature-activity-and-food-logs/5671/19 Wavelet coherence] is one potential solution.