Apply the aligned rank transform to a factorial model (with optional grouping terms). Usually done in preparation for a nonparametric analyses of variance on models with numeric or ordinal responses, which can be done by following up with anova.art.

FAT2DIC.art(
  f1,
  f2,
  response,
  decreasing = TRUE,
  sup = NA,
  xlab = " ",
  ylab = "Sum of posts",
  legend.title = "Factor",
  theme = theme_classic()
)

Arguments

f1

Numeric or complex vector with factor 1 levels

f2

Numeric or complex vector with factor 2 levels

response

Numerical vector containing the response of the experiment.

decreasing

Letter order (default is TRUE)

sup

Number of units above the standard deviation or average bar on the graph

xlab

Treatments name (Accepts the expression() function)

ylab

Variable response name (Accepts the expression() function)

legend.title

Legend title name

theme

ggplot2 theme (default is theme_classic())

Value

The function returns the Anova of aligned ranks, the multiple comparison test and the interaction graph.

References

Wobbrock, J. O., Findlater, L., Gergle, D., Higgins, J. J. (2011, May). The aligned rank transform for nonparametric factorial analyses using only anova procedures. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 143-146).

Kay, M., Wobbrock, J. O. (2020). Package ‘ARTool’.

See also

Author

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

data(cloro) with(cloro, FAT2DIC.art(f1,f2,resp))
#> #> ----------------------------------------------------------------- #> Analysis of Variance of Aligned Rank Transformed Data #> ----------------------------------------------------------------- #> FV Df Df.res SQ SQres Fvalue p-value #> fator1 fator1 1 32 640.0 4642.0 4.411891 4.365822e-02 #> fator2 fator2 3 32 2570.2 2745.8 9.984510 8.493625e-05 #> fator1:fator2 fator1:fator2 3 32 4103.8 1199.2 36.502557 1.916135e-10 #> #> #> ----------------------------------------------------------------- #> fator1 fator2 emmean SE df lower.CL upper.CL .group #> 2 IN Plantio 25.0 2.7377 32 19.423488 30.576512 A #> 1 NI Plantio 17.8 2.7377 32 12.223488 23.376512 A #> 4 IN R1+15 27.6 2.7377 32 22.023488 33.176512 A #> 3 NI R1+15 14.4 2.7377 32 8.823488 19.976512 B #> 5 NI V1+15 36.8 2.7377 32 31.223488 42.376512 A #> 6 IN V1+15 3.0 2.7377 32 -2.576512 8.576512 B #> 8 IN V3+15 28.0 2.7377 32 22.423488 33.576512 A #> 7 NI V3+15 11.4 2.7377 32 5.823488 16.976512 B #> #> #> ----------------------------------------------------------------- #> fator2 fator1 emmean SE df lower.CL upper.CL .group #> 3 V3+15 IN 28.0 2.7377 32 22.423488 33.576512 a #> 1 R1+15 IN 27.6 2.7377 32 22.023488 33.176512 a #> 2 Plantio IN 25.0 2.7377 32 19.423488 30.576512 a #> 4 V1+15 IN 3.0 2.7377 32 -2.576512 8.576512 b #> 8 V1+15 NI 36.8 2.7377 32 31.223488 42.376512 a #> 6 Plantio NI 17.8 2.7377 32 12.223488 23.376512 b #> 5 R1+15 NI 14.4 2.7377 32 8.823488 19.976512 b #> 7 V3+15 NI 11.4 2.7377 32 5.823488 16.976512 b