Explore >> Select a destination
|
You are here 
|
kieranhealy.org | ||
| | | | |
albert-rapp.de
|
|
| | | | | I show you how to create a correlation heat map with {ggplot2}, how to avoid using the wrong colors and how to use some nice variations of standard heat maps. | |
| | | | |
juliasilge.com
|
|
| | | | | Analyzing Injuries Caused by Consumer Products | |
| | | | |
www.nicholas-ollberding.com
|
|
| | | | | Inherent limitations with one-at-a-time (OaaT) feature testing (i.e., single feature differential abundance analysis) have contributed to the increasing popularity of mixture models for correlating microbial features with factors of interest (i. | |
| | | | |
indrajeetpatil.github.io
|
|
| | | Extension of ggplot2, ggstatsplot creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) . | ||
