visstat {visStatistics}R Documentation

Wrapper for visstat_core allowing two different input styles

Description

A wrapper around the core function visstat_core defining the decision logic for statistical hypothesis testing and visualisation between two variables of class "numeric", "integer", or "factor".

Usage

visstat(
  x,
  y,
  ...,
  conf.level = 0.95,
  numbers = TRUE,
  minpercent = 0.05,
  graphicsoutput = NULL,
  plotName = NULL,
  plotDirectory = getwd()
)

Arguments

x

A vector of class "numeric", "integer", or "factor" (standardised usage), or a data.frame containing the relevant columns (backward-compatible usage).

y

A second vector (standardised usage), or a character string specifying the name of a column in x (backward-compatible usage).

...

If x is a data frame and y is a character string, an additional character string must follow, naming the second column.

conf.level

Confidence level for statistical inference; default is 0.95.

numbers

Logical. Whether to annotate plots with numeric values.

minpercent

Minimum proportion (between 0 and 1) required to display a category in plots.

graphicsoutput

Optional. Output format for plots (e.g., "pdf", "png").

plotName

Optional. File name prefix for saving plot output.

plotDirectory

Directory in which to save plots; defaults to the current working directory.

Details

This wrapper supports two input formats:

The interpretation of x and y depends on the variable classes: In the following, data of class numeric or integer are both referred to by their common mode numeric

This wrapper standardises the input and calls visstat_core, which selects and executes the appropriate test with visual output and assumption diagnostics.

Value

A list as returned by visstat_core, containing statistical results and graphical outputs.

See Also

the core function visstat_core, the package's vignette vignette("visStatistics") for the overview, and the accompanying webpage https://shhschilling.github.io/visStatistics/.

Examples

## Standardised usage (preferred):
visstat(mtcars$am, mtcars$mpg)

## Backward-compatible usage (same result):
visstat(mtcars, "mpg", "am")

## Wilcoxon rank sum test
grades_gender <- data.frame(
  Sex = as.factor(c(rep("Girl", 20), rep("Boy", 20))),
  Grade = c(
    19.3, 18.1, 15.2, 18.3, 7.9, 6.2, 19.4, 20.3, 9.3, 11.3,
    18.2, 17.5, 10.2, 20.1, 13.3, 17.2, 15.1, 16.2, 17.3, 16.5,
    5.1, 15.3, 17.1, 14.8, 15.4, 14.4, 7.5, 15.5, 6.0, 17.4,
    7.3, 14.3, 13.5, 8.0, 19.5, 13.4, 17.9, 17.7, 16.4, 15.6
  )
)
visstat(grades_gender$Sex, grades_gender$Grade)

## Welch's one-way ANOVA
visstat(npk$block, npk$yield)

## Kruskal-Wallis
visstat(iris$Species, iris$Petal.Width)

## Simple linear regression
visstat(trees$Height, trees$Girth, conf.level = 0.99)

## Chi-squared
HairEyeColorDataFrame <- counts_to_cases(as.data.frame(HairEyeColor))
visstat(HairEyeColorDataFrame$Eye, HairEyeColorDataFrame$Hair)

## Fisher's test
HairEyeColorMaleFisher <- HairEyeColor[, , 1]
blackBrownHazelGreen <- HairEyeColorMaleFisher[1:2, 3:4]
blackBrownHazelGreen <- counts_to_cases(as.data.frame(blackBrownHazelGreen))
visstat(blackBrownHazelGreen$Eye, blackBrownHazelGreen$Hair)

## Save PNG
visstat(blackBrownHazelGreen$Hair, blackBrownHazelGreen$Eye,
        graphicsoutput = "png", plotDirectory = tempdir())

## Save PDF
visstat(iris$Species, iris$Petal.Width, graphicsoutput = "pdf",
          plotDirectory = tempdir())

## Custom plot name
visstat(iris$Species, iris$Petal.Width,
        graphicsoutput = "pdf", plotName = "kruskal_iris", plotDirectory = tempdir())


[Package visStatistics version 0.1.7 Index]