ScottKnott is an R package that implements the Scott
& Knott clustering algorithm as a multiple comparison method in the
Analysis of Variance (ANOVA) context, for both balanced and unbalanced
designs.
formula, aov,
lm, aovlist, and lmerMod
objects.emmeans) for
unbalanced data.plot method with customisable dispersion bands
(min–max, SD, CI, pooled CI).xtable.Install from CRAN:
install.packages("ScottKnott")Install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("ivanalaman/ScottKnott")library(ScottKnott)
## Completely Randomized Design (CRD) — balanced
data(CRD1)
sk1 <- with(CRD1,
SK(y ~ x,
data = dfm,
which = 'x'))
summary(sk1)
plot(sk1,
dispersion = 'sd',
d.col = 'steelblue')
## Randomized Complete Block Design (RCBD)
data(RCBD)
sk2 <- with(RCBD,
SK(y ~ blk + tra,
data = dfm,
which = 'tra'))
summary(sk2)
plot(sk2,
dispersion = 'ci',
d.col = 'red')/R: Core functions and S3 methods./man: Reference documentation (.Rd
files)./data: Example datasets (CRD, RCBD, LSD, FE, SPE, SPET,
SSPE, sorghum)./demo: Runnable demos for each experimental
design./inst: Package citation file.Contributions are welcome. Open an issue or submit a pull request with:
To check and build locally:
R CMD check ScottKnott
R CMD build ScottKnott
R CMD INSTALL ScottKnott_X.X-X.tar.gztestthat) for all experimental
designs.Developed by:
Faria, J. C.; Jelihovschi, E. G.; Allaman, I. B.
Universidade Estadual de Santa Cruz - UESC
Departamento de Ciencias Exatas - DCEX
Ilheus - Bahia - Brasil