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colleyRstats helps streamline a typical analysis
workflow: configure a session, check assumptions, create a plot, and
generate manuscript-ready text.
colleyRstats::colleyRstats_setup(
set_options = FALSE,
set_theme = FALSE,
set_conflicts = FALSE,
print_citation = FALSE,
verbose = FALSE
)
#> Registered S3 methods overwritten by 'ggpp':
#> method from
#> heightDetails.titleGrob ggplot2
#> widthDetails.titleGrob ggplot2
#> Registered S3 method overwritten by 'lme4':
#> method from
#> na.action.merMod carcolleyRstats::generateEffectPlot(
data = transform(main_df, Group = ConditionID),
x = "ConditionID",
y = "score",
fillColourGroup = "Group",
ytext = "Score",
xtext = "Condition"
)
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?art_summary <- data.frame(
Effect = "ConditionID",
Df = 1,
`F value` = 5.42,
`Pr(>F)` = 0.027,
Df.res = 19,
check.names = FALSE
)
colleyRstats::reportART(art_summary, dv = "score")
#> The ART found a significant main effect of \ConditionID on score (\F{1}{19}{5.42}, \p{0.027}, $\eta_{p}^{2}$ = 0.22, 95\% CI: [0.01, 1.00]).reportMeanAndSD() and reportDunnTest().generateMoboPlot() or
generateMoboPlot2() for optimization studies.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.