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visStatistics
View on CRAN: Click
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Download and install visStatistics package within the R console
Install from CRAN:
install.packages("visStatistics")
Install from Github:
library("remotes")
install_github("cran/visStatistics")
Install by package version:
library("remotes")
install_version("visStatistics", "0.1.1")
Attach the package and use:
library("visStatistics")
Maintained by
Sabine Schilling
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-12
Latest Update: 2021-02-12
Description:
Visualization of the most powerful statistical hypothesis test. The function vistat() visualizes the statistical hypothesis testing between the dependent variable (response) varsample and the independent variable (feature) varfactor. The statistical hypothesis test (including the eventual corresponding post-hoc analysis) with the highest statistical power fulfilling the assumptions of the corresponding test is chosen based on a decision tree. A graph displaying the raw data accordingly to the chosen test is generated, the test statistics including eventual post-hoc-analysis are returned. The automated workflow is especially suited for browser based interfaces to server-based deployments of R. Implemented tests: lm(), t.test(), wilcox.test(), aov(), kruskal.test(), fisher.test(), chisqu.test(). Implemented tests to check the normal distribution of standardized residuals: shapiro.test() and ad.test(). Implemented post-hoc tests: TukeyHSD() for aov() and pairwise.wilcox.test() for kruskal.test(). For the comparison of averages, the following algorithm is implemented: If the p-values of the standardized residuals of both shapiro.test() or ad.test() are smaller than 1-conf.level, kruskal.test() resp. wilcox.test() are performed, otherwise the oneway.test() and aov() resp. t.test() are performed and displayed. Exception: If the sample size is bigger than 100, t.test() is always performed and wilcox.test() is never executed (Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>). For the test of independence of count data, Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is implemented: If more than 20 percent of all cells have a count smaller than 5, fisher.test() is performed and displayed, otherwise chisqu.test(). In both cases case an additional mosaic plot is generated.
How to cite:
Sabine Schilling (2021). visStatistics: Automated Visualization of Statistical Tests. R package version 0.1.1, https://cran.r-project.org/web/packages/visStatistics
Previous versions and publish date:
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imports, suggests or enhances
Functions, R codes and Examples using
the visStatistics R package
Some associated functions: colorscheme . counts_to_cases . get_samples_fact_inputfile . openGraphCairo . saveGraphVisstat . vis_anova_assumptions . visstat .
Some associated R codes: counts_to_cases.R . detach_package.R . get_samples_fact_inputfile.R . openGraphCairo.R . saveGraphVisstat.R . test_and_visuals.R . visstat.R . Full visStatistics package functions and examples
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