💊 Clinical trials & R&D & Epidemiology
💻 R enthusiast
👩💻 Stats @ loyal.com
https://jesslgraves.github.io
1) LM abandoned his post at 50% consumption but
2) Found a friend, LM2
3) And many more 😵💫
Consumption rates have become exponential and sadly all (visible) Munch Bunchers had to be evicted.
1) LM abandoned his post at 50% consumption but
2) Found a friend, LM2
3) And many more 😵💫
Consumption rates have become exponential and sadly all (visible) Munch Bunchers had to be evicted.
1) still on the same lead (! I was surprised by this!)
2) leaf consumption at 50%
1) still on the same lead (! I was surprised by this!)
2) leaf consumption at 50%
Part 1 of a series on using the binomial distribution to interpret & power studies on rare events -- inspired by Martin Bland's write-up, "Detecting a single event".
Spoiler: rare events are hard to find 😜
🔗: tinyurl.com/2hb9vh68
#rstats #stats101
Part 1 of a series on using the binomial distribution to interpret & power studies on rare events -- inspired by Martin Bland's write-up, "Detecting a single event".
Spoiler: rare events are hard to find 😜
🔗: tinyurl.com/2hb9vh68
#rstats #stats101
I built a #shiny app with #plotly to explore the @xkcd.com color survey results in both 🌈 RGB and HSV space🌈.
🖥️ App: jessgraves.shinyapps.io/xkcd-color-s...
🌐 Personal site: jesslgraves.github.io/apps/2025-07...
I built a #shiny app with #plotly to explore the @xkcd.com color survey results in both 🌈 RGB and HSV space🌈.
🖥️ App: jessgraves.shinyapps.io/xkcd-color-s...
🌐 Personal site: jesslgraves.github.io/apps/2025-07...
Sex is a covariate here, but the imbalanced sampling forces it to become a confounder.
In balanced designs, you don’t have to adjust for sex to get an unbiased age estimate (but it’s less powerful).
In imbalanced designs you do.
Sex is a covariate here, but the imbalanced sampling forces it to become a confounder.
In balanced designs, you don’t have to adjust for sex to get an unbiased age estimate (but it’s less powerful).
In imbalanced designs you do.
You accidentally got a biased sample & now your results might biased be too.
How to recover? Can you?!
🔍 Confounding (yes, ice cream & crime is in there 😅)
📊 Model misspecification
💻 Code, plots, & takeaways
tinyurl.com/4af2td83
#rstats #episky #stats
You accidentally got a biased sample & now your results might biased be too.
How to recover? Can you?!
🔍 Confounding (yes, ice cream & crime is in there 😅)
📊 Model misspecification
💻 Code, plots, & takeaways
tinyurl.com/4af2td83
#rstats #episky #stats
I reference a similar finding in one of my blog posts.
jesslgraves.github.io/posts/2025-0...
I reference a similar finding in one of my blog posts.
jesslgraves.github.io/posts/2025-0...
grep() setting the argument value = TRUE is pretty helpful!
#rstats
grep() setting the argument value = TRUE is pretty helpful!
#rstats
Interactions = the effects change over the range of X — but many models predict beyond the observable range of X. For small but sig effects, if the effects aren’t sig over the observable data, then it’s not really an effect, right?
Interactions = the effects change over the range of X — but many models predict beyond the observable range of X. For small but sig effects, if the effects aren’t sig over the observable data, then it’s not really an effect, right?
🔗 Post can be found here: jesslgraves.github.io/posts/2025-0...
🔗 Post can be found here: jesslgraves.github.io/posts/2025-0...