Dr Mircea Zloteanu 🌺🌞🍃#statstab #533 Analysis of Covariance and Standardization as Instances of Prediction
Thoughts: An interesting read from some of the best. ANCOVA seems so simple yet it is quite complex.
#ancova #analysis #standardization #prediction #marginaleffects
#statstab #485 Bayesian ANCOVA and the ATE
Thoughts: Still grappling with the implications of using the causal inference approach to randomized experiments. But it's interesting.
#ATE #causalinference #ancova #ANOVA #rstats #estimand #counterfactuals
#statstab #476 Experimental : causal
Thoughts: Randomized experiments are the gold standard for inference for a reason. But they are hard to design.
#design #r #statistics #methods #experiment #tutorial #pedagogy #education #hypothesis #nhst #causal #ancova
https://book.declaredesign.org/library/experimental-causal.html
Fabrizio MusacchioDue to a recent discussion with colleagues on whether and when to use #LinearMixedModels (#LMM), I wrote a blog post comparing LMM to other approaches using simulated data. I thought, it may also be useful for others working with hierarchical data structures in #neuroscience and beyond.
🌍 https://www.fabriziomusacchio.com/blog/2026-01-31-linear_mixed_models/
#Python #Statistics #DataScience #MixedModels #Statsmodels #ANOVA #ANCOVA #GLMM #regression
#statstab #460 {permuco} permutation tests in linear models with nuisances variables
Thoughts: Supports ANOVA, ANCOVA, t-tests and more.
#permutation #randomization #ANOVA #rstats #r #pvalues #ancova #ttest
Permutation Tests for Regression, (Repeated Measures) ANOVA/ANCOVA and Comparison of Signals
Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).