| Title: | Automated Visualization of Statistical Tests |
|---|---|
| Description: | Visualization of the most powerful statistical hypothesis test. The R package `visStatistics` with its core function visstat() allows to quickly visualise raw data and, based on a decision tree, select the statistical hypothesis test with the highest statistical power between the dependent variable (response) and the independent variable (feature). To compare the means of two groups with sample sizes greater than 100 in both groups, visstat() performs a t.test()(Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>). Otherwise, when comparing the mean of two or more groups, the test chosen depends on the p-values of the null that the standardised residuals of the regression model are normally distributed as tested by both shapiro.test() and ad.test(): If both p-values are smaller than the error probability 1-conf.level,the non-parametric tests kruskal.test() resp. wilcox.test() are used, otherwise the parametric tests oneway.test() and aov() resp. t.test() are used. For count data, visstat() tests the null hypothesis, that the feature and the response are independent of each other using the chisqu.test() or fisher.test(). The choice of test is based on Cochran's rule Cochran (1954) <doi:10.2307/3001666>). Implemented tests: lm(), t.test(), wilcox.test(), aov(), kruskal.test(), fisher.test(), chisqu.test(). Implemented tests to check the normal distribution of the standardised residuals: shapiro.test() and ad.test(). Implemented post-hoc tests: TukeyHSD() for aov() and pairwise.wilcox.test() for kruskal.test(). All implemented statistical tests are called with their default parameter sets, except for conf.level, which can be adjusted in the visstat() function call. A detailed description of the decision tree and numerous and numerous examples can be found in the visStatistics vignette. |
| Authors: | Sabine Schilling [cre, aut, cph], Peter Kauf [ctb] |
| Maintainer: | Sabine Schilling <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.2 |
| Built: | 2025-02-26 03:30:04 UTC |
| Source: | https://github.com/shhschilling/visstatistics |
colorscheme(x) selects color scheme of graphical output. Function parameter NULL lists all available color schemes, 1 a color tuple of green and blue
2 a color tuple of dark green and turquoi, 3 a colorplaette as defined by RcolorBrewercolorscheme(x) selects color scheme of graphical output. Function parameter NULL lists all available color schemes, 1 a color tuple of green and blue
2 a color tuple of dark green and turquoi, 3 a colorplaette as defined by RcolorBrewer
colorscheme(colorcode = NULL)colorscheme(colorcode = NULL)
colorcode |
selects color scheme. parameters NULL: list of all available color schemes, 1: colortuple, 2, colortuple2, 3, ColorPalette |
selected color scheme, colors are given with their Hex Code #RRGGBB names
Convert data frame of counts to data frame of cases. data frame must contain a column with frequencies (counts) as generated by as.data.frame from a contingency table
counts_to_cases(x, countcol = "Freq")counts_to_cases(x, countcol = "Freq")
x |
a |
countcol |
character string, name of the column of x containing the counts. Default name of the column is "Freq". |
data frame of cases of dimension (total number of counts as sum of "Freq" in x) times 2.
counts_to_cases(as.data.frame(HairEyeColor[, , 1]), countcol = "Freq")counts_to_cases(as.data.frame(HairEyeColor[, , 1]), countcol = "Freq")
Selects columns defined by characters varsample and varfactor from dataframe, returns selected columns with their names.
get_samples_fact_inputfile(dataframe, varsample, varfactor)get_samples_fact_inputfile(dataframe, varsample, varfactor)
dataframe |
|
varsample |
column name of dependent variable in dataframe, datatype |
varfactor |
column name of independent variable in dataframe, datatype |
selected columns, sample, factor, name_of_sample (character string equaling varsample), name_of_factor (character string equaling varsample)
get_samples_fact_inputfile(trees, "Girth", "Height")get_samples_fact_inputfile(trees, "Girth", "Height")
Cairo wrapper function returning NULL if not type is specified
openGraphCairo( width = 640, height = 480, fileName = NULL, type = NULL, fileDirectory = getwd(), pointsize = 12, bg = "transparent", canvas = "white", units = "px", dpi = 150 )openGraphCairo( width = 640, height = 480, fileName = NULL, type = NULL, fileDirectory = getwd(), pointsize = 12, bg = "transparent", canvas = "white", units = "px", dpi = 150 )
width |
see |
height |
see |
fileName |
name of file to be created. Does not include both file extension ". |
type |
Supported output types are "png", "jpeg", "pdf", "svg", "ps" and "tiff". See |
fileDirectory |
path of directory, where plot is stored. Default current working directory. |
pointsize |
see |
bg |
see |
canvas |
see |
units |
see |
dpi |
DPI used for the conversion of units to pixels. Default value 150. |
openGraphCairo() Cairo() wrapper function. Differences to Cairo:
a) prematurely ends the function call to Cairo() returning NULL, if no output type of types "png", "jpeg", "pdf", "svg", "ps" or "tiff"
is provided.
b)
The file argument of the underlying Cairo function is generated by file.path(fileDirectory,paste(fileName,".", type, sep = "")).
NULL, if no type is specified. Otherwise see Cairo()
## adapted from example in \code{Cairo()} openGraphCairo(fileName = "normal_dist", type = "pdf", fileDirectory = tempdir()) plot(rnorm(4000), rnorm(4000), col = "#ff000018", pch = 19, cex = 2) dev.off() # creates a file "normal_dist.pdf" in the directory specified in fileDirectory # ## remove the plot from fileDirectory file.remove(file.path(tempdir(), "normal_dist.pdf"))## adapted from example in \code{Cairo()} openGraphCairo(fileName = "normal_dist", type = "pdf", fileDirectory = tempdir()) plot(rnorm(4000), rnorm(4000), col = "#ff000018", pch = 19, cex = 2) dev.off() # creates a file "normal_dist.pdf" in the directory specified in fileDirectory # ## remove the plot from fileDirectory file.remove(file.path(tempdir(), "normal_dist.pdf"))
Closes all graphical devices with dev.off() and saves the output only if both fileName and type are provided.
saveGraphVisstat( fileName = NULL, type = NULL, fileDirectory = getwd(), oldfile = NULL )saveGraphVisstat( fileName = NULL, type = NULL, fileDirectory = getwd(), oldfile = NULL )
fileName |
name of file to be created in directory |
type |
see |
fileDirectory |
path of directory, where graphic is stored. Default setting current working directory. |
oldfile |
old file of same name to be overwritten |
NULL, if no type or fileName is provided, TRUE if graph is created
# very simple KDE (adapted from example in Cairo()) openGraphCairo(type = "png", fileDirectory = tempdir()) plot(rnorm(4000), rnorm(4000), col = "#ff000018", pch = 19, cex = 2) # save file "norm.png" in directory specified in fileDirectory saveGraphVisstat("norm", type = "png", fileDirectory = tempdir()) file.remove(file.path(tempdir(), "norm.png")) # remove file "norm.png" from fileDirectory.# very simple KDE (adapted from example in Cairo()) openGraphCairo(type = "png", fileDirectory = tempdir()) plot(rnorm(4000), rnorm(4000), col = "#ff000018", pch = 19, cex = 2) # save file "norm.png" in directory specified in fileDirectory saveGraphVisstat("norm", type = "png", fileDirectory = tempdir()) file.remove(file.path(tempdir(), "norm.png")) # remove file "norm.png" from fileDirectory.
vis_anova_assumptions checks for normality of the standardised residuals of the ANOVA. Both the
Shapiro-Wilk test shapiro.test() and the Anderson-Darling test ad.test() check the
null that the standardised residuals are normally distributed.
It generates a scatter plot
of the standardised residuals versus the fitted mean values of the linear models for each level of fact.
Furthermore a normal QQ plot of the standardised residuals is generated.
The null of homogeneity of variances of each factor level is tested with the bartlett.test().
vis_anova_assumptions( samples, fact, conf.level = 0.95, samplename = "", factorname = "", cex = 1 )vis_anova_assumptions( samples, fact, conf.level = 0.95, samplename = "", factorname = "", cex = 1 )
samples |
vector containing dependent variable, datatype numeric |
fact |
vector containing independent variable, datatype factor |
conf.level |
confidence level, 0.95=default |
samplename |
name of sample used in graphical output, dataype character , ""=default |
factorname |
name of sample used in graphical output, dataype character, ""=default |
cex |
number indicating the amount by which plotting text and symbols should be scaled relative to the default. 1=default, 1.5 is 50% larger, 0.5 is 50% smaller, etc. |
list containing the test statistics of the anova, the p values generated by the
Shapiro-Wilk test shapiro.test(), the Anderson-Darling test ad.test() and the bartlett.test().
ToothGrowth$dose <- as.factor(ToothGrowth$dose) vis_anova_assumptions(ToothGrowth$len, ToothGrowth$dose) vis_anova_assumptions(ToothGrowth$len, ToothGrowth$supp) vis_anova_assumptions(iris$Petal.Width, iris$Species)ToothGrowth$dose <- as.factor(ToothGrowth$dose) vis_anova_assumptions(ToothGrowth$len, ToothGrowth$dose) vis_anova_assumptions(ToothGrowth$len, ToothGrowth$supp) vis_anova_assumptions(iris$Petal.Width, iris$Species)
Based on a decision tree, visstat() picks the statistical hypothesis
test with the highest statistical power between the dependent variable
(response) and the independent variable (feature) in a data.frame
named dataframe.
Data in the provided dataframe must be structured column wise,
where varsample and varfactor are character strings
corresponding to the column names of the dependent and independent variable
respectively. For each test visstat() returns both a graph with the
main test statistics in its title as well as a list of the test statistics
including eventual post-hoc analysis.
visstat( dataframe, varsample, varfactor, conf.level = 0.95, numbers = TRUE, minpercent = 0.05, graphicsoutput = NULL, plotName = NULL, plotDirectory = getwd() )visstat( dataframe, varsample, varfactor, conf.level = 0.95, numbers = TRUE, minpercent = 0.05, graphicsoutput = NULL, plotName = NULL, plotDirectory = getwd() )
dataframe |
|
varsample |
column name of the dependent variable in |
varfactor |
column name of the independent variable in |
conf.level |
confidence level of the interval. |
numbers |
a logical indicating whether to show numbers in mosaic count plots. |
minpercent |
number between 0 and 1 indicating minimal fraction of total count data of a category to be displayed in mosaic count plots. |
graphicsoutput |
saves plot(s) of type "png", "jpg", "tiff" or "bmp" in directory specified in |
plotName |
graphical output is stored following the naming convention "plotName.graphicsoutput" in |
plotDirectory |
specifies directory, where generated plots are stored. Default is current working directory. |
Implemented tests: lm(),t.test(), wilcox.test(),
aov(), kruskal.test(), fisher.test(), chisqu.test().
Implemented tests for 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 depends on the value of the parameter of conf.level, which defaults to 0.95.
If the p-values of the standardized residuals of shapiro.test() or ks.test() are smaller
than the error probability 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 of both levels is bigger than 30, wilcox.test() is never executed,instead always the t.test() is performed (Lumley et al. (2002)
<doi:10.1146/annurev.publheath.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 an expected count smaller than 5 or
an expected cell count is zero, fisher.test() is performed and displayed, otherwise the chisqu.test().
In both cases case an additional mosaic plot showing Pearson's residuals is generated.
list containing statistics of test with highest statistical power meeting assumptions. All values are returned as invisibly copies. Values can be accessed by assigning a return value to visstat.
## Welch Two Sample t-test (calling t.test()) visstat(mtcars, "mpg", "am") ## Wilcoxon rank sum test (calling wilcox.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, "Grade", "Sex") ## One-way analysis of means (oneway.test()) anova_npk <- visstat(npk, "yield", "block") anova_npk # prints summary of tests ## Kruskal-Wallis rank sum test (calling kruskal.test()) visstat(iris, "Petal.Width", "Species") visstat(InsectSprays, "count", "spray") ## Linear regression visstat(trees, "Girth", "Height", conf.level = 0.99) ## Pearson's Chi-squared test and mosaic plot with Pearson residuals ### Transform array to data.frame HairEyeColorDataFrame <- counts_to_cases(as.data.frame(HairEyeColor)) visstat(HairEyeColorDataFrame, "Hair", "Eye") ## 2x2 contingency tables with Fisher's exact test and mosaic plot with Pearson residuals HairEyeColorMaleFisher <- HairEyeColor[, , 1] ### slicing out a 2 x2 contingency table blackBrownHazelGreen <- HairEyeColorMaleFisher[1:2, 3:4] blackBrownHazelGreen <- counts_to_cases(as.data.frame(blackBrownHazelGreen)) fisher_stats <- visstat(blackBrownHazelGreen, "Hair", "Eye") fisher_stats # print out summary statistics ## Saving the graphical output in directory plotDirectory ## A) saving graphical output of type "png" in temporary directory tempdir() ## with default naming convention: visstat(blackBrownHazelGreen, "Hair", "Eye", graphicsoutput = "png", plotDirectory = tempdir()) ## remove graphical output from plotDirectory file.remove(file.path(tempdir(), "chi_squared_or_fisher_Hair_Eye.png")) file.remove(file.path(tempdir(), "mosaic_complete_Hair_Eye.png")) ## B) Specifying pdf as output type: visstat(iris, "Petal.Width", "Species", graphicsoutput = "pdf", plotDirectory = tempdir()) ## remove graphical output from plotDirectory file.remove(file.path(tempdir(), "kruskal_Petal_Width_Species.pdf")) ## C) Specifiying plotName overwrites default naming convention visstat(iris, "Petal.Width", "Species", graphicsoutput = "pdf", plotName = "kruskal_iris", plotDirectory = tempdir() ) ## remove graphical output from plotDirectory file.remove(file.path(tempdir(), "kruskal_iris.pdf"))## Welch Two Sample t-test (calling t.test()) visstat(mtcars, "mpg", "am") ## Wilcoxon rank sum test (calling wilcox.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, "Grade", "Sex") ## One-way analysis of means (oneway.test()) anova_npk <- visstat(npk, "yield", "block") anova_npk # prints summary of tests ## Kruskal-Wallis rank sum test (calling kruskal.test()) visstat(iris, "Petal.Width", "Species") visstat(InsectSprays, "count", "spray") ## Linear regression visstat(trees, "Girth", "Height", conf.level = 0.99) ## Pearson's Chi-squared test and mosaic plot with Pearson residuals ### Transform array to data.frame HairEyeColorDataFrame <- counts_to_cases(as.data.frame(HairEyeColor)) visstat(HairEyeColorDataFrame, "Hair", "Eye") ## 2x2 contingency tables with Fisher's exact test and mosaic plot with Pearson residuals HairEyeColorMaleFisher <- HairEyeColor[, , 1] ### slicing out a 2 x2 contingency table blackBrownHazelGreen <- HairEyeColorMaleFisher[1:2, 3:4] blackBrownHazelGreen <- counts_to_cases(as.data.frame(blackBrownHazelGreen)) fisher_stats <- visstat(blackBrownHazelGreen, "Hair", "Eye") fisher_stats # print out summary statistics ## Saving the graphical output in directory plotDirectory ## A) saving graphical output of type "png" in temporary directory tempdir() ## with default naming convention: visstat(blackBrownHazelGreen, "Hair", "Eye", graphicsoutput = "png", plotDirectory = tempdir()) ## remove graphical output from plotDirectory file.remove(file.path(tempdir(), "chi_squared_or_fisher_Hair_Eye.png")) file.remove(file.path(tempdir(), "mosaic_complete_Hair_Eye.png")) ## B) Specifying pdf as output type: visstat(iris, "Petal.Width", "Species", graphicsoutput = "pdf", plotDirectory = tempdir()) ## remove graphical output from plotDirectory file.remove(file.path(tempdir(), "kruskal_Petal_Width_Species.pdf")) ## C) Specifiying plotName overwrites default naming convention visstat(iris, "Petal.Width", "Species", graphicsoutput = "pdf", plotName = "kruskal_iris", plotDirectory = tempdir() ) ## remove graphical output from plotDirectory file.remove(file.path(tempdir(), "kruskal_iris.pdf"))