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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.gz
testthat) 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
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