twoway

Analysis of Two-Way Tables a la Tukey

Version 0.7.1; documentation built for pkgdown 2026-09-01

The twoway package provides analysis and graphical methods for two-way tables with one observation per cell, most typically used in an Analysis of Variance (ANOVA) context. The methods follow Tukey (1949), “One Degree of Freedom for Non-additivity”, explained more concretely in Tukey (1972), Exploratory Data Analysis, but the graphical ideas are more interesting and general:

• How to display an assumed additive relation between two factors graphically, and visualize departures from an additive fit? The plot method here uses a rectangle for the row and column effects, but rotated 40 degrees so that the vertical axis represents the fitted value under an additive model. In this, the actual value can be shown as a vertical segment representing the departure from an additive model.

• How to assess visually whether a power transformation of the response, $y^p$ might be more nearly additive in the factors? The diagnostic plot implemented here shows a plot designed so that the slope $b$ of a fitted line suggests a transformation $y \rightarrow y^{1-b}$.

The goal of the package is to introduce these ideas in R, and allow further development. This R implementation is based on my SAS macro, twoway.sas.

Installation 📦

This currently released CRAN package can be installed directly from CRAN:

install.packages("twoway")

You can install the development version twoway from github with:

# install.packages("devtools")
# devtools::install_github("friendly/twoway")
install.packages("twoway", repos = c('https://friendly.r-universe.dev', 'https://cloud.r-project.org'))

Example

A trivial example shows the analysis of a 3 x 3 table, containing mean reaction times for three subjects presented with three types of sentences and asked to judge whether the sentence was TRUE or FALSE. The questions are:

• How does reaction time vary with subject and sentence type?
• Can the results be accounted for by an additive model, with an effect for subject and for sentence type?
• Are there any unusual observations that deviate from an additive model?
• If there are systematic departures from additivity, could a power transformation of the response (1/x, log(x), sqrt(x), $x^2$, …) make the simpler additive model more tenable, and more easily understood?

library(twoway)
data("sentRT")
sentRT
#>        Sent
#> Subj    sent1 sent2 sent3
#>   subj1   1.7   1.9   2.0
#>   subj2   4.4   4.5   5.7
#>   subj3   6.6   7.4  10.5

The twoway() function gives the basic analysis: a decomposition of the two-way table, giving the:

• grand mean ($\mu = \bar{x}_{..}$),
• row effects ($\alpha_i = \bar{x}_{i.}-\mu$),
• column effects ($\beta_j = \bar{x}_{.j}-\mu$), and
• residuals ($x_{ij}-\mu -\alpha_i -\beta_j$)

sent.2way <- twoway(sentRT)
print(sent.2way)
#> 
#> Mean decomposition (Dataset: "sentRT"; Response: Value)
#> Residuals bordered by row effects, column effects, and overall
#> 
#>         Sent
#> Subj       sent1    sent2    sent3      roweff  
#>          + -------- -------- -------- + --------
#>   subj1  |  0.56667  0.40000 -0.96667 : -3.10000
#>   subj2  |  0.26667  0.00000 -0.26667 : -0.10000
#>   subj3  | -0.83333 -0.40000  1.23333 :  3.20000
#>          + ........ ........ ........ + ........
#>   coleff | -0.73333 -0.36667  1.10000 :  4.96667

twoway() also allows for a robust fitting by row and column medians, using Tukey’s idea of median polish, as implemented in stats::medpolish(). This uses method="median" in the call to twoway().

print(twoway(sentRT, method="median"), border=2)
#> 
#> Median polish decomposition (Dataset: "sentRT"; Response: Value)
#> Residuals bordered by row effects, column effects, and overall
#> 
#>         Sent
#> Subj       sent1 sent2 sent3   roweff
#>          + ----  ----  ----  + ----  
#>   subj1  |  0.0   0.0  -1.1  : -2.6  
#>   subj2  |  0.1   0.0   0.0  :  0.0  
#>   subj3  | -0.6   0.0   1.9  :  2.9  
#>          + ....  ....  ....  + ....  
#>   coleff | -0.2   0.0   1.2  :  4.5

The result is much simpler, in that most residuals are closer to 0, and one large one stands out.

Plot methods

The plot method for twoway objects currently provides two types of plots:

• a plot of fitted values under the additive models and residuals (the default, which="fit")
• a diagnostic plot of interaction residuals vs. comparison values under additivity (which="diagnose").

plot(sent.2way)

The diagnostic plot shows the regression of residuals under the additive model against the comparison values under the additive model. If the points in this plot are reasonably linear and have a non-zero slope, b, a suggested power transformation of the response to $x^{1-b}$ will often remove non-additivity.

plot(sent.2way, which="diagnose")

#> Slope of Residual on comparison value:  1.6 
#> Suggested power transformation:         -0.6 
#> Ladder of powers transformation:        reciprocal root

There is an opposite-corner pattern to the residuals in the analysis by means. In the diagnostic plot, the positive slope, $b=1.6$ suggests a power transformation $x^{1-b} = x^{-0.6}$, which can be taken as close to $1 / \sqrt{x}$. Alternatively, reaction time data is often more easily analyzed by classical methods and the results more easily understood in terms of response speed, using the transformation $1/x$.

anova method

In the present version, the anova() method for a "twoway" object gives the results of two analyses: one for the additive model, and one for the model allowing 1 df for non-additivity. Both of these assume that row and column effects are fit using means (method="mean").

anova(sent.2way)
#> Dataset: sentRT; method: "mean"
#> 
#> Analysis of Variance Table, assuming additivity
#> 
#>           Df Sum Sq Mean Sq F value   Pr(>F)   
#> Subj       2 59.580 29.7900 30.2949 0.003835 **
#> Sent       2  5.647  2.8233  2.8712 0.168574   
#> Residuals  4  3.933  0.9833                    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> 
#> Analysis of Variance Table, allowing non-additivity
#> 
#>            Df Sum Sq Mean Sq F value    Pr(>F)    
#> Subj        2 59.580 29.7900 513.449 0.0001572 ***
#> Sent        2  5.647  2.8233  48.662 0.0051710 ** 
#> nonadd      1  3.759  3.7593  64.793 0.0040046 ** 
#> pure error  3  0.174  0.0580                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

There is a variety of other tests for additivity in twoway tables with $n=1$ per cell. A number of these are implemented in the additivityTests package.

Other methods and functions

• The as.data.frame() method for a "twoway" object gives a tidy data.frame result, containing the components of the fitted values and other quantities.

as.data.frame(sent.2way)
#>    Subj  Sent data  fit  dif residual roweff coleff   nonadd
#> 1 subj1 sent1  1.7 1.13 7.33    0.567   -3.1 -0.733  0.45772
#> 2 subj2 sent1  4.4 4.13 4.33    0.267   -0.1 -0.733  0.01477
#> 3 subj3 sent1  6.6 7.43 1.03   -0.833    3.2 -0.733 -0.47248
#> 4 subj1 sent2  1.9 1.50 7.70    0.400   -3.1 -0.367  0.22886
#> 5 subj2 sent2  4.5 4.50 4.70    0.000   -0.1 -0.367  0.00738
#> 6 subj3 sent2  7.4 7.80 1.40   -0.400    3.2 -0.367 -0.23624
#> 7 subj1 sent3  2.0 2.97 9.17   -0.967   -3.1  1.100 -0.68658
#> 8 subj2 sent3  5.7 5.97 6.17   -0.267   -0.1  1.100 -0.02215
#> 9 subj3 sent3 10.5 9.27 2.87    1.233    3.2  1.100  0.70872

• The utility functions to_long() and to_wide() facilitate conversion between matrix format and a long format with variables such as Row, Col and Value.

TODOs

This package is at a middle stage of development. There are some small TODOs scattered throughout the code. In addition:

• It would be nicer to use the names of the row and column variables in some displays, rather than Row and Col.