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Visualization of the statistical hypothesis test between two groups of categorical or numerical data.
The function visstat()
visualizes the
statistical hypothesis testing between two groups of
data, where varsample
is the dependent variable (or
response) and varfactor
is the independent variable
(feature). The statistical hypothesis test with the highest statistical
power and fulfilling the assumptions of the corresponding test is
performed and visualized. A graph displaying the raw data accordingly to
the chosen test as well as the test statistics is generated. Furthermore
visstat()
returns the corresponding test statistics as
text. The automated workflow is especially suited for browser based
interfaces to server-based deployments of R. Implemented tests:
lm(), t.test(), wilcox.test(), aov(), oneway.test(),kruskal.test(), fisher.test(),chisqu.test()
.
install.packages("devtools")
library(devtools)
install_github("shhschilling/visStatistics")
library(visStatistics)
?visstat
visstat(trees,"Girth","Height")
visstat(iris,"Petal.Width", "Species")
visstat(InsectSprays,"count","spray")
InsectSpraysAB <- InsectSprays[ which(InsectSprays$spray == 'A'| InsectSprays$spray == 'B'), ] #select only sprays 'A und 'B'
InsectSpraysAB$spray = factor(InsectSpraysAB$spray)
visstat(InsectSpraysAB,"count","spray")
visstat(ToothGrowth,"len", "supp")
HairEyeColorMale = counts_to_cases(as.data.frame(HairEyeColor[,,1]))
visstat(HairEyeColorMale,"Hair","Eye")
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.