This Protestant drama is a little removed from happenings in apostolic Christianity, but encapsulates the dynamic that can play out in any church. From: https://www.americanmantle.org/the-problem-with-the-pca/ #AmericanMantle #PCA #kinism #antiracist #Presbyterian #Calvinism #Christianity #Christian

#programming #engineering #statistics #PCA in #commonLisp #lisp #blog #easy #reference https://screwlisp.small-web.org/programming/lisp-principal-component-analysis/

While I am just feeding no-other-obvious-source lisp pieces of my brain to my young kitten, here is principle component analysis in common lisp using an openly available #ML package #clml from a Japanese telco changing hands 15 years ago, actively developed by a lisp lone wolf up until five years ago.

My demo is in english (#emacs #eev #eepitch) in contrast to the Japanese internals.

Continuing to work through #GCP training – having gotten my #ACE, I now need to get my #PCA.

At any rate, one of the training-modules was talking about data transfer times and popped up a size/rate table to show transfer-times. It instantly transported me back to when I was doing DR-related delivery-consulting.

I still remember one customer being pissed when I'd finished setting up their storage-replication and declared, "well, I'll see you in about 21 days to verify that we're actually finished and functional". They were incredulous, sputtering about "how can you possibly say 21 days". I'd reminded them that I'd noted that their replication-bandwidth was objectively too small (while they had a T3, their data set was large and they couldn't get their networking people to remove the session-limits that were constraining the replication to a fraction of that T3's capability). We'd spent a couple days benchmarking and working with their networking people to try to address the discrepancy between their theoretical bandwidth and the observed bandwidth. In fairness, they probably would have been ill-advised to dedicate the entirety of the T3's bandwidth to replications — since they presumably had other projects that needed some of its bandwidth — but the amount they were able to allocate to the storage replication was way too low. I'd given them an initial transfer-time estimate, the day prior, based on my benchmarking and the first couple hours of the replication's sync-up. My return the next day was mostly to confirm that things were moving along as expected — so that I didn't have to revise my estimate (fortunately, over the intervening 16 hours, my numbers had stayed dead-on).

Just to add some gas to the fire:

• I reminded them that they had had the option to speed up the initial sync by seeing it with a tape-based restore – which would have required them to ship tapes from NJ to AZ, import the tapes into their DR site's tape library system, then do a restore to the DR site's storage array
• I pointed out to them that, with such a low transfer-rate, if they ever had link-loss, it could take them hours to days to get back in sync. I further pointed out and that, worst case (i.e., on a long-enough link-outage), the replication-software might declare the sync "stale" and they'd have to re-initialize and re-do the transfer.

This was probably 2005 or 2006. So, it's not that they had a ton of data to transfer, it's just that cross-country private circuits weren't available with nearly as high of transfer speeds and what options were available were silly-expensive.

To give you a sneak peek into my courses, I’ve just published a free video on YouTube that walks through a complete data project in R.

Watch the video here: https://www.youtube.com/watch?v=l2OgRdofp90

Multispectral Remote Sensing Expression of Lineaments and Alteration Minerals in Part of Crystalline Rock Units of Southwestern Nigeria - Implication on Gold Prospecting
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https://doi.org/10.1016/j.oreoa.2025.100091 <-- shared paper
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#GIS #spatial #mapping #ASTER #Gold #Hydrothermal #HydrothermalAlteration #Lineament #Landsat #ETM #EnhancedThematicMapper #phyllic #Ilesha #schist #belt #geology #mining #Nigeria #naturalresources #mineral #mineralresources #golddeposits #aerogeophysical #remotesensing #geophysical #spatialanalysis #geomorphology #geomorphometry #alteration #propylitic #argillic #mineralogy #XRD #structuralgeology #lithology #PrincipalComponentAnalysis #PCA #prospecting #fracture #joints #faulting #Multispectral #Lineaments #Crystalline

#statstab #271 {bruceR} package

Thoughts: Useful functions for psychologists. I like how it treats t-tests, giving all the options from Lakens (2013) for Cohen's d, and comparison with other software.

#r #psychology #ttest #PCA #ANOVA #education

https://psychbruce.github.io/bruceR/reference/TTEST.html

'Robust Principal Component Analysis using Density Power Divergence', by Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh.

http://jmlr.org/papers/v25/23-1096.html

#pca #robust #divergence

Principal Component Analysis (PCA) is a powerful technique for reducing the dimensionality of your data while maintaining its essential structure. A key advantage of PCA is its ability to transform high-dimensional data into a lower-dimensional space, enabling new ways to visualize complex data. Two crucial plots that help visualize PCA are loading plots and biplots.

I've created a video to walk you through these visualizations: https://www.youtube.com/watch?v=f34zq2jErK0

A few days ago, the Statistics Globe YouTube channel reached 30K subscribers (see here: https://www.youtube.com/c/statisticsglobe)!

To celebrate this milestone, I've launched a 3-day promotion offering a 33% discount on all my courses, with only 24 hours left. The promotion ends tomorrow, Friday, at 3:33 pm GMT+2: https://statisticsglobe.com/courses

Series of tutorial posts on Principal Component Analysis (PCA) on my social media accounts.

Here are some of the posts:
- PCA Explained: https://facebook.com/joachim.schork/posts/pfbid02Yx3RHmDakew21EiZBU9ZAP8hSAqoERc3ThiMBwevYSDTwZh2b7JDgEHgB7TmWKtGl
- Pros & Cons of PCA: https://x.com/JoachimSchork/status/1820334157219647749
- How to Visualize PCA Results: https://linkedin.com/posts/joachim-schork_dataanalytics-rprogramming-bigdata-activity-7224307564044267520-V8Q1/

This series is also a teaser for my online course: https://statisticsglobe.com/online-course-pca-theory-application-r

'Sharp analysis of power iteration for tensor PCA', by Yuchen Wu, Kangjie Zhou.

http://jmlr.org/papers/v25/24-0006.html

#tensor #pca #iterations

In case you want to know why I've completely given up on #Christianity (no reason that you *should*), this is a good summary:

https://youtu.be/isp4243WPO0?si=k5kaQHB0u8kigDoH

BTW: the interviewer question at about the 12m55s mark is spot on.

I used to be a Christian, but I just can't be any more. At least not here anymore...

I wrote a tool in jvarkit to manually select some samples after `plink --cluster` . https://jvarkit.readthedocs.io/en/latest/SwingPLinkSelectCluster/ #pca

I love how PCA results differ widely depending on whether you normalise input data or not, and whether you apply it to the covariance or correlation matrix. I think figuring the correct approach out for this dataset might be a tomorrow problem. If anyone has a great go-to resource, please link me!

'Unlabeled Principal Component Analysis and Matrix Completion', by Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris.

http://jmlr.org/papers/v25/22-0816.html

#pca #algorithms #matrices

Principal Component Analysis (PCA) reduces the dimensionality of your data, enhancing the efficiency and accuracy of K-means clustering by focusing on the most informative features.

More info in my upcoming course: https://statisticsglobe.com/online-course-pca-theory-application-r

PCA needs to be used with care as it may not capture nonlinear relationships, assumes higher variance components are more important, and can obscure the interpretability of original features due to its transformation process.

Learn how to use PCA correctly in my online course: https://statisticsglobe.com/online-course-pca-theory-application-r

Off to Chicago! Any other fediverse folks going to the Grateful Dead Studies Association conference at the PCA? I know @bourgwick will be there, anyone else? #gratefuldead #pca #academia

A PCA scree plot displays the variance explained by each principal component, helping to determine the number of components to retain by identifying where the variance explained significantly drops off.

Learn more in my online course: https://statisticsglobe.com/online-course-pca-theory-application-r