vis_anova_assumptions {visStatistics} | R Documentation |
Testing ANOVA assumptions
Description
vis_anova_assumptions
checks for normality of the standardised residuals of the anova both graphically by qq-plots as well as performing
the Shapiro-Wilk-test shapiro.test
and the Anderson-Darling-Test ad.test
.
aov
further tests the homoscedacity of each factor level in fact
with the bartlett.test
.
Usage
vis_anova_assumptions(
samples,
fact,
conf.level = 0.95,
samplename = "",
factorname = "",
cex = 1
)
Arguments
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. |
Value
my_list: list containing the test statistics of the anova
aov(samples~fact)
,bartlett.test(samples~fact)
and the tests of normality of the standardized residuals of aov, ks_test
and shapiro_test
Examples
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)