ARTool: R Package for the Aligned Rank Transform for Nonparametric Factorial ANOVAs
ARTool is an R package implementing the Aligned Rank Transform for conducting nonparametric analyses of variance on factorial models. This implementation is based on the ART procedure as used in the original implementation of ARTool by Wobbrock et al.
The package automates the Aligning-and-Ranking process using the
art function. It also automates the process of running a series of ANOVAs on the transformed data and extracting the results of interest. It supports traditional ANOVA models (fit using
lm), repeated measures ANOVAs (fit using
aov), and mixed effects models (fit using
lmer); the model used is determined by the formula passed to
Note: The documentation of this package assumes some level of familiarity with when and why you may want to use the aligned rank transform; the ARTool page provides a more in-depth (and highly approachable) introduction to the aligned rank transform and the motivation for its use.
You can install the latest released version from CRAN with this R command:
Or, you can install the latest development version from GitHub with these R commands:
The general approach to using ART is to transform your data using
art , verify the ART procedure is appropriate to the dataset using
summary , and then run an anova on the transformed data using
First, let us load some example data:
Higgins1990Table5 is a data frame from an experiment in which the effects of
DryMatter in peat pots was tested. Four pots were placed on each
Tray , with
Moisture varied between
Tray s and
Fertilizer varied within
Tray s. We can see the basic structure of the data:
## 'data.frame': 48 obs. of 4 variables: ## $ Tray : Factor w/ 12 levels "t1","t2","t3",..: 1 1 1 1 2 2 2 2 3 3 ... ## $ Moisture : Factor w/ 4 levels "m1","m2","m3",..: 1 1 1 1 1 1 1 1 1 1 ... ## $ Fertilizer: Factor w/ 4 levels "f1","f2","f3",..: 1 2 3 4 1 2 3 4 1 2 ... ## $ DryMatter : num 3.3 4.3 4.5 5.8 4 4.1 6.5 7.3 1.9 3.8 ...
## Tray Moisture Fertilizer DryMatter ## 1 t1 m1 f1 3.3 ## 2 t1 m1 f2 4.3 ## 3 t1 m1 f3 4.5 ## 4 t1 m1 f4 5.8 ## 5 t2 m1 f1 4.0 ## 6 t2 m1 f2 4.1 ## 7 t2 m1 f3 6.5 ## 8 t2 m1 f4 7.3
Step 1: Transform the data
To analyze this data using the aligned rank transform, we first transform the data using
art . We specify the response variable (
DryMatter ), the fixed effects and all of their interactions (
Moisture*Fertilizer, or equivalently
Moisture + Fertilizer + Moisture:Fertilizer), and any grouping terms if present (here,
(1|Tray) has no effect on the results of the aligned rank transformation, it will be used by
anova to determine the type of model to run: when grouping terms are present, mixed effects models are run using
lmer. If you wish to use a repeated measures ANOVA instead of a mixed effects model, you can use an
Error term instead (see below for an example of this). If you do not having repeated measures, do not include any grouping terms or error terms.
m <- art(DryMatter ~ Moisture*Fertilizer + (1|Tray), data=Higgins1990Table5)
Step 2: Verify appropriateness of ART
To verify that the ART procedure was correctly applied and is appropriate for this dataset, we can look at the output of
## Aligned Rank Transform of Factorial Model ## ## Call: ## art(formula = DryMatter ~ Moisture * Fertilizer + (1 | Tray), ## data = Higgins1990Table5) ## ## Column sums of aligned responses (should all be ~0): ## Moisture Fertilizer Moisture:Fertilizer ## 0 0 0 ## ## F values of ANOVAs on aligned responses not of interest (should all be ~0): ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 0 0 0 0 0
We see that the columns sums of aligned responses and the F values of ANOVAs on aligned responses not of interest are all ~0, indicating that the alignment correctly "stripped out" effects not of interest. Thus, we can apply the ANOVA on the transformed data.
Step 3: Run the ANOVA
ARTool automatically selects the model to be used for the ANOVA. Because we have included a grouping term,
(1|Tray), ARTool will fit mixed effects models using
lmer and run the ANOVAs on them:
## Analysis of Variance of Aligned Rank Transformed Data ## ## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) ## Model: Mixed Effects (lmer) ## Response: art(DryMatter) ## ## F Df Df.res Pr(>F) ## 1 Moisture 23.833 3 8 0.00024199 *** ## 2 Fertilizer 122.402 3 24 1.1124e-14 *** ## 3 Moisture:Fertilizer 5.118 9 24 0.00064665 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Alternative model: Repeated Measures ANOVA
This particular study could also be analyzed using a repeated measures ANOVA, yielding the same results (note that repeated measures ANOVAs and mixed effects models will not always yield the same results). To instead run a repeated measures ANOVA, add an
Error term to the model as you might for a call to
m <- art(DryMatter ~ Moisture*Fertilizer + Error(Tray), data=Higgins1990Table5) anova(m)
## Analysis of Variance of Aligned Rank Transformed Data ## ## Table Type: Repeated Measures Analysis of Variance Table (Type I) ## Model: Repeated Measures (aov) ## Response: art(DryMatter) ## ## Error Df Df.res F value Pr(>F) ## 1 Moisture Tray 3 8 23.833 0.00024199 *** ## 2 Fertilizer Withn 3 24 122.402 1.1124e-14 *** ## 3 Moisture:Fertilizer Withn 9 24 5.118 0.00064665 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
For an example of how to run contrast tests on an
art model, see this vignette:
This vignette is also available here.
Wobbrock J, Findlater L, Gergle D and Higgins J (2011). "The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only ANOVA Procedures." In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '11), Vancouver, British Columbia (May 7-12, 2011). New York: ACM Press, pp. 143-146. http://depts.washington.edu/aimgroup/proj/art/. DOI: 10.1145/1978942.1978963.