Lmst

#statstab #359 A Pragmatic Approach to Statistical Testing and Estimation (PASTE)

Thought: A (basic) guide to some alternatives to p-values: bayesian posterior intervals, Bayes Factors, and AIC.

#NHST #pvalues #TOST #BayesFactor #AIC #modelcomparison

https://doi.org/10.1016/j.hpe.2017.12.009

#statstab #265 The limited epistemic value of ‘variation analysis’ (R^2)

Thoughts: Interesting post and comments on what we can and can't say from an r2 metric.

#stats #r2 #effectsize #variance #modelcomparison #models #causalinference

https://larspsyll.wordpress.com/2023/05/23/the-limited-epistemic-value-of-variation-analysis/

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
--
https://doi.org/10.1016/j.jag.2022.102716 <-- shared 2022 paper
--
“HIGHLIGHTS:
• [They] evaluated the impacts of topographic correction on forest mapping in the mountains.
• The enhanced C-correction and the physical model reduced topographic effects.
• The corrected Landsat imagery time series resulted in higher accuracy.
• Terrain information improved classification but not as much as topographic correction.
• [They] recommend using topographic correction for forest cover mapping..."
#GIS #spatial #AtmosphericCorrection #IlluminationCondition #LandCover #ModelComparison #TimeSeries #TopographicCorrection #remotesensing #comparasion #topographic #correction #NDVI #forest #vegetation #model #modeling #spatialanalyis #accuracy #forestcover #Russia #Georgia #CaucasusMountains #spatiotemporal #landsat #elevation #DEM

maps / images - Landsat summer (panel A) and autumn (Panel B) images (RGB: 743) for the study area. Subset region marked in black frame. Panels C-F show uncorrected (panel C), corrected summer image using the enhanced C-correction (panel D) or the physical model (panel E), and the corresponding illumination condition (F). Panels G-J show the same as panel C-F, but for the autumn image. The QA layers generated from FORCE were not applied here.

map - Forest cover classification agreement among the 18 sets of input variables. Pixels in red color were classified by all sets of input variables as coniferous, in green color as broadleaf forest, and in blue color as mixed forest. Black color indicates that no forest was predicted by any set of input variables. Two subsets A and B which are marked in white frames were zoomed in for a detailed map comparison in [another figure]

#statstab #196 JASP Bayesian ANOVA

Thoughts: @JASPStats is used by researchers to "add some bayes factors" to their results. But, do you know what those actually reflect? Here is what their team says:

#bayes #bayesfactors #anova #modelcomparison

https://static.jasp-stats.org/about-bayesian-anova.html

#statstab #174 The Principle of Predictive Irrelevance

Thoughts: "when two competing models predict a data set equally well, that data set cannot be used to discriminate the models and the data set is evidentially irrelevant"

#modelcomparison #inference

https://www.bayesianspectacles.org/the-principle-of-predictive-irrelevance-or-why-intervals-should-not-be-used-for-model-comparison-featuring-a-point-null-hypothesis/

#statstab #171 Guideline of Selecting & Reporting Intraclass Correlation Coefficients for Reliability Research

Thoughts: "There are 10 forms of ICCs." Are you reporting the correct one? Find out!

#ICC #modelcomparison #reliability #interraterreliability

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4913118/#!po=15.7143

#statstab #51 R Functions for Variance Decomposition {varde}

Thoughts: A useful package to get more insight into your mixed effects model.

#r #rstats #mixedeffects #lmm #research #modelcomparison

https://github.com/jmgirard/varde

#statstab #44 A note on the interpretation of incremental fit indices

Thoughts: A "good fit" is a meaningless statement. There are no rules of thumb 👍