In biology you often count things: cells of type A out of total cells of type B, mutant flies out of total flies, etc. The most common move in papers is to compute a ratio per animal and run a t-test on the ratios. This throws away how many cells you actually counted: "5/100" and "50/1000” becomes same, and feeds strictly [0,1] bound data to t-test. The principled alternative is count regression with offset(log(N)): model the raw count directly, bring the total in as a statistical weight, respect the non-Gaussian nature of count data. This week I decided to test this assumption in practice:

Setup. Four methods across two pipelines:

• Animal-level: Welch's t-test on ratios vs CMP GLM (glmmTMB(..., family = compois()))
• Field-level: LMM with (1 | EmbryoID) vs CMP GLMM with the same RE

Three metrics: Type-I error, size-adjusted power (Lloyd correction), median 95% CI width.

The interesting bit. Instead of running ~10k sims at one design, I sampled 300 designs over a 6-dim space with Latin hypercube (log-uniform on multiplicative knobs, linear on CV, discrete on n_animals), ran 200-500 sims per design × method, then fit GP emulators (hetGP, Matérn 5/2 + ARD) on the point estimates. (I try to run and hide but come back to GAMs one way or another :)). LOOCV verified they generalize. Sobol decomposition tells me which design knobs drive each method's response; Monte Carlo marginalization over nuisance knobs gives clean 2D heatmaps of power and CI width on (n_animals, CV).

Findings.

• Both methods hit 80% power at essentially the same (n_animals, CV) spot. Below that threshold, in the underpowered regime where most real experiments live, count regression beats the ratio approach.
• CMP GLMM produces narrower CIs than LMM at essentially 100% of designs (median ~12% narrower). CMP GLM beats Welch at ~97% (~7% narrower).
• Adding random effects shifts the 80% power contour to the left: fewer animals for the same power.
• Sobol shows all four methods have nearly identical sensitivity profiles. The precision advantage isn't about one method responding to a knob the others ignore; it's about how efficiently each one extracts information from the same drivers.

Practical takeaway. Default to glmmTMB(Y ~ Group + offset(log(N)) + (1 | EmbryoID), family = compois()). The CMP advantage is real and lives in the small-n regime. If you have huge n, all four agree.

Full reproducible post with code:

I'm bringing my substack back to life to talk about AI and data science. I have conflicted feelings about both AI and writing about AI but I want to try and work through them in the open. I'd love to know what y'all would like to hear about in future posts! 😀

https://docs.google.com/forms/d/e/1FAIpQLSe2EW1NavZO83E5lgyaRfkOY8jSJ1Ycrt0ixM5lx6ZuyA9zNg/viewform?usp=sharing&ouid=102663571374226674335

qol is a package which can be used as its own ecosystem concerning descriptive evaluations, data wrangling, tabulation and much more. It offers over a hundret high level functions which make the coding life easier. While the last updates implemented many entirely new functions, this update focuses more on refining the existing ones.

printify is the base R zero dependency message system which is directly implemented in qol, but can also be used as a stand alone lightweight package.

A detailed overview for both packages can be seen here:

qol: https://github.com/s3rdia/qol

printify: https://github.com/s3rdia/printify

So what is in the update?

Renamed functions

compute() and recode() have been renamed and now have a "." at the end (compute.() and recode.()) to prevent masking errors in combination with dplyr. This means existing code will break, if these functions where used.

Mesage system

* set_no_color(): Suppresses the color codes so that messages can be printed clean. The option is auto controlled on load via the system variable `NO_COLOR` but can also be set individually by this function. Console output in e.g. RStudio vs. output to a logging system should be handled automatically right now.

* set_up_custom_message(): Waiting symbols as well as the color of the time stamps can now be customized.

* print_step(): Now has a new `in_place` parameter, which prints the message on the same line as before, instead of in the next line. This can e.g. be used inside loops as follows.

new_in_place_steps <- function(){
    print_start_message()
    
    print_step("MAJOR", "Let's get started...")
    
    for (i in seq_len(10)){
        print_step("Minor", "This is in place step [i] of 10", i = i, in_place = TRUE)
        Sys.sleep(0.25)
    }
    
    print_step("MAJOR", "Loop has ended")
    
    print_closing()
}

new_in_place_steps()

Tabulation workflow

any_table() and export_with_style(): If the whole result list from these functions is passed for the `workbook` parameter, the functions now are able to extract the actual workbook from the list and run without error. Additionally if a list is passed, which is not a result list containing the workbook, the functions error and abort execution.

any_table(), frequencies(), crosstabs(): If 'csv' is specified as extension in the `file name` set in the global options or the style parameter the result table will then be exported as 'csv'. Otherwise the actual workbook will be exported as `xlsx` as normal.

New way to transpose data

transpose_plus() can now in a wide to long transposition not only put results below each other, but also side by side.

# Example formats
age. <- discrete_format(
    "Total"          = 0:100,
    "under 18"       = 0:17,
    "18 to under 25" = 18:24,
    "25 to under 55" = 25:54,
    "55 to under 65" = 55:64,
    "65 and older"   = 65:100)

sex. <- discrete_format(
    "Total"  = 1:2,
    "Male"   = 1,
    "Female" = 2)

# Example data frame
my_data <- dummy_data(1000)

# Transpose from long to wide and use a multilabel to generate additional categories
long_to_wide <- my_data |>
    transpose_plus(preserve = c(year, age),
                   pivot    = "sex",
                   values   = c(income, weight),
                   formats  = list(sex = sex., age = age.),
                   weight   = weight,
                   na.rm    = TRUE) |>
    rename_multi("income_Total"  = "Total",
                 "income_Male"   = "Male",
                 "income_Female" = "Female")

# Transpose back from wide to long but this time put results side by side.
# To do that every list entry has to have the same name. The values parameter
# is then used to give the new value variables a name. For the expressions of
# the new categorical variable the variable names from the first pivot list
# entry are used.
wide_to_long <- long_to_wide |>
    transpose_plus(preserve = c(year, age),
                   values   = c(income, weight),
                   pivot    = list(sex = c("Total", "Male", "Female"),
                                   sex = c("weight_Total", "weight_Male", "weight_Female")))

if.() can now explicitly delete

If the new `delete` keyword is passed instead of a variable assignment, the provided condition deletes observations instead of keeping them.

subset_df <- my_data |> if.(sex == 1, delete)

# Is the same as
subset_df <- my_data |> if.(sex != 1)

https://www.surveymonkey.com/r/LBF25FP

Hey r/stats,

I wanted to share a personal project I’ve been working on: statistical-calculators.site.

It’s a web-based platform with over 90 free statistical calculators designed to make life easier for students, researchers, and data analysts.

What’s inside?

• Hypothesis Testing: One/Two sample t-tests, One-Way & Two-Way ANOVA, Chi-Square (Independence & Goodness of fit).
• Probability Distributions: Normal, Binomial, Poisson, Geometric, Exponential, and Uniform distributions.
• Non-Parametric Tests: Wilcoxon Signed Rank, Mann-Whitney, Kruskal-Wallis, Fisher's exact test, and McNemar.
• Regression & Correlation: Simple & Multivariate Linear Regression, Pearson, Spearman, Kendall's rho, and Cramer's V.
• Descriptive Stats & Intervals: Confidence intervals (proportions, means, variances), frequency tables, histograms, and basic plots.
• Plus some extra daily tools like BMI, finance, and geometry calculators.
• And much much more; in statistics, but not just statistics.

Why I made it: I wanted to create something straightforward that runs directly in the browser with zero barriers:

• 100% Free
• No registration / No account required
• No software installation needed

Multi-language support: Most calculators are fully accessible in English and French, and I'm currently working on expanding the Spanish version as well (you can toggle languages right on the site) .

I would honestly love to get your feedback! Are there any specific features, UI tweaks, or additional calculators you think are missing?

Would love to hear what you think;

Thank you so much!

statistical-calculators.site

Hey everyone! I'm currently doing a school project that required I do a survey. If anyone is willing to answer these three quick questions anonymously I'd really apriciate it!

The link to the survey is here:

https://docs.google.com/forms/d/e/1FAIpQLSfvDCuKTPgpPNcfnXoQy9jbsCOaQaZY4vCZYA4-5k-0hhWGGg/viewform?usp=publish-editor

Hello guys, I’m fairly new to the research game and need your advice. For my medical research I have line charts. You can see the example on the picture. On the x Axis a different time stamps and Im comparing the time stamps to one another. My supervisor wants me to add significance asterisk and p-Values to the line charts. What is the most common depiction for that? Do you have examples how it should look?
(My supervisor is sadly not very helpful and expects me to figure is our by myself.)
PS: English is not my first language